Mon, 15 Apr 2024

Package rtables updated to version 0.6.7 with previous version 0.6.6 dated 2023-12-08

Title: Reporting Tables
Description: Reporting tables often have structure that goes beyond simple rectangular data. The 'rtables' package provides a framework for declaring complex multi-level tabulations and then applying them to data. This framework models both tabulation and the resulting tables as hierarchical, tree-like objects which support sibling sub-tables, arbitrary splitting or grouping of data in row and column dimensions, cells containing multiple values, and the concept of contextual summary computations. A convenient pipe-able interface is provided for declaring table layouts and the corresponding computations, and then applying them to data.
Author: Gabriel Becker [aut] , Adrian Waddell [aut], Daniel Sabanes Bove [ctb], Maximilian Mordig [ctb], Davide Garolini [ctb], Emily de la Rua [ctb], Abinaya Yogasekaram [ctb], Joe Zhu [ctb, cre], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Joe Zhu <joe.zhu@roche.com>

Diff between rtables versions 0.6.6 dated 2023-12-08 and 0.6.7 dated 2024-04-15

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More information about rtables at CRAN
Permanent link

Package rlistings updated to version 0.2.8 with previous version 0.2.7 dated 2023-12-06

Title: Clinical Trial Style Data Readout Listings
Description: Listings are often part of the submission of clinical trial data in regulatory settings. We provide a framework for the specific formatting features often used when displaying large datasets in that context.
Author: Gabriel Becker [aut] , Adrian Waddell [aut], Joe Zhu [aut, cre], Davide Garolini [ctb], Emily de la Rua [ctb], Abinaya Yogasekaram [ctb], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Joe Zhu <joe.zhu@roche.com>

Diff between rlistings versions 0.2.7 dated 2023-12-06 and 0.2.8 dated 2024-04-15

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More information about rlistings at CRAN
Permanent link

Package ggeffects updated to version 1.5.2 with previous version 1.5.1 dated 2024-03-26

Title: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs
Description: Compute marginal effects and adjusted predictions from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Effects and predictions can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'.
Author: Daniel Luedecke [aut, cre] , Frederik Aust [ctb] , Sam Crawley [ctb] , Mattan S. Ben-Shachar [ctb] , Sean C. Anderson [ctb]
Maintainer: Daniel Luedecke <d.luedecke@uke.de>

Diff between ggeffects versions 1.5.1 dated 2024-03-26 and 1.5.2 dated 2024-04-15

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Package tidyfst updated to version 1.7.8 with previous version 1.7.7 dated 2023-07-17

Title: Tidy Verbs for Fast Data Manipulation
Description: A toolkit of tidy data manipulation verbs with 'data.table' as the backend. Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain. This package is an extension of 'data.table'. While enjoying a tidy syntax, it also wraps combinations of efficient functions to facilitate frequently-used data operations.
Author: Tian-Yuan Huang [aut, cre]
Maintainer: Tian-Yuan Huang <huang.tian-yuan@qq.com>

Diff between tidyfst versions 1.7.7 dated 2023-07-17 and 1.7.8 dated 2024-04-15

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Package reticulate updated to version 1.36.0 with previous version 1.35.0 dated 2024-01-31

Title: Interface to 'Python'
Description: Interface to 'Python' modules, classes, and functions. When calling into 'Python', R data types are automatically converted to their equivalent 'Python' types. When values are returned from 'Python' to R they are converted back to R types. Compatible with all versions of 'Python' >= 2.7.
Author: Tomasz Kalinowski [ctb, cre], Kevin Ushey [aut], JJ Allaire [aut], RStudio [cph, fnd], Yuan Tang [aut, cph] , Dirk Eddelbuettel [ctb, cph], Bryan Lewis [ctb, cph], Sigrid Keydana [ctb], Ryan Hafen [ctb, cph], Marcus Geelnard [ctb, cph]
Maintainer: Tomasz Kalinowski <tomasz@posit.co>

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Package formatters updated to version 0.5.6 with previous version 0.5.5 dated 2023-12-06

Title: ASCII Formatting for Values and Tables
Description: We provide a framework for rendering complex tables to ASCII, and a set of formatters for transforming values or sets of values into ASCII-ready display strings.
Author: Gabriel Becker [aut] , Adrian Waddell [aut], Davide Garolini [ctb], Emily de la Rua [ctb], Abinaya Yogasekaram [ctb], Joe Zhu [ctb, cre], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Joe Zhu <joe.zhu@roche.com>

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Package d4storagehub4R updated to version 0.4-4 with previous version 0.4-3 dated 2023-03-01

Title: Interface to 'D4Science' 'StorageHub' API
Description: Provides an interface to 'D4Science' 'StorageHub' API (<https://dev.d4science.org/>). Allows to get user profile, and perform actions over the 'StorageHub' (workspace) including creation of folders, files management (upload/update/deletion/sharing), and listing of stored resources.
Author: Emmanuel Blondel [aut, cre]
Maintainer: Emmanuel Blondel <emmanuel.blondel1@gmail.com>

Diff between d4storagehub4R versions 0.4-3 dated 2023-03-01 and 0.4-4 dated 2024-04-15

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Permanent link

Package RestRserve updated to version 1.2.2 with previous version 1.2.1 dated 2022-09-12

Title: A Framework for Building HTTP API
Description: Allows to easily create high-performance full featured HTTP APIs from R functions. Provides high-level classes such as 'Request', 'Response', 'Application', 'Middleware' in order to streamline server side application development. Out of the box allows to serve requests using 'Rserve' package, but flexible enough to integrate with other HTTP servers such as 'httpuv'.
Author: Dmitry Selivanov [aut, cre] , Artem Klevtsov [aut] , David Zimmermann [ctb], rexy.ai [cph, fnd]
Maintainer: Dmitry Selivanov <ds@rexy.ai>

Diff between RestRserve versions 1.2.1 dated 2022-09-12 and 1.2.2 dated 2024-04-15

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Package gpboost updated to version 1.4.0.1 with previous version 1.4.0 dated 2024-04-11

Title: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models
Description: An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist (2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the methodology.
Author: Fabio Sigrist [aut, cre], Tim Gyger [aut], Pascal Kuendig [aut], Benoit Jacob [cph], Gael Guennebaud [cph], Nicolas Carre [cph], Pierre Zoppitelli [cph], Gauthier Brun [cph], Jean Ceccato [cph], Jitse Niesen [cph], Other authors of Eigen for the incl [...truncated...]
Maintainer: Fabio Sigrist <fabiosigrist@gmail.com>

Diff between gpboost versions 1.4.0 dated 2024-04-11 and 1.4.0.1 dated 2024-04-15

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Package blavaan updated to version 0.5-4 with previous version 0.5-3 dated 2024-01-19

Title: Bayesian Latent Variable Analysis
Description: Fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) <doi:10.18637/jss.v085.i04>; Merkle et al. (2021) <doi:10.18637/jss.v100.i06>.
Author: Edgar Merkle [aut, cre] , Yves Rosseel [aut], Ben Goodrich [aut], Mauricio Garnier-Villarreal [ctb] , Terrence D. Jorgensen [ctb] , Huub Hoofs [ctb] , Rens van de Schoot [ctb] , Andrew Johnson [ctb] , Matthew Emery [ctb]
Maintainer: Edgar Merkle <merklee@missouri.edu>

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Sun, 14 Apr 2024

Package testex updated to version 0.2.0 with previous version 0.1.0 dated 2024-04-04

Title: Add Tests to Examples
Description: Add tests in-line in examples. Provides standalone functions for facilitating easier test writing in Rd files. However, a more familiar interface is provided using 'roxygen2' tags. Tools are also provided for facilitating package configuration and use with 'testthat'.
Author: Doug Kelkhoff [aut, cre]
Maintainer: Doug Kelkhoff <doug.kelkhoff@gmail.com>

Diff between testex versions 0.1.0 dated 2024-04-04 and 0.2.0 dated 2024-04-14

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Package smplot2 updated to version 0.2.0 with previous version 0.1.0 dated 2024-04-12

Title: Creating and Annotating a Composite Plot in 'ggplot2'
Description: Provides functions for creating and annotating a composite plot in 'ggplot2'. Offers background themes and shortcut plotting functions that produce figures that are appropriate for the format of scientific journals. Some methods are described in Min and Zhou (2021) <doi:10.3389/fgene.2021.802894>.
Author: Seung Hyun Min [aut, cre]
Maintainer: Seung Hyun (Sam) Min <seung.min@mail.mcgill.ca>

Diff between smplot2 versions 0.1.0 dated 2024-04-12 and 0.2.0 dated 2024-04-14

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New package longreadvqs with initial version 0.1.2
Package: longreadvqs
Title: Viral Quasispecies Comparison from Long-Read Sequencing Data
Version: 0.1.2
Description: Performs variety of viral quasispecies diversity analyses [see Gregori et al. (2016) <doi:10.1016/j.virol.2016.03.017>] based on long-read sequence alignment. Main functions include 1) sequencing error minimization and read sampling, 2) Single nucleotide variant (SNV) profiles comparison, and 3) viral quasispecies profiles comparison and visualization.
License: GPL-3
URL: https://github.com/NakarinP/longreadvqs
BugReports: https://github.com/NakarinP/longreadvqs/issues
Encoding: UTF-8
Imports: ape, Biostrings, cowplot, dplyr, ggplot2, ggpubr, grDevices, magrittr, plyr, purrr, QSutils, RColorBrewer, reshape2, scales, seqinr, stats, stringdist, stringr, tibble, tidyr
Depends: R (>= 2.10)
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-04-12 18:53:33 UTC; nakarinpamornchainavakul
Author: Nakarin Pamornchainavakul [aut, cre]
Maintainer: Nakarin Pamornchainavakul <pamornakarin@gmail.com>
Repository: CRAN
Date/Publication: 2024-04-14 19:20:02 UTC

More information about longreadvqs at CRAN
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New package ElevDistr with initial version 1.0.8
Package: ElevDistr
Title: Calculate the Distance to the Nearest Local Treeline
Version: 1.0.8
Description: A method to calculate the distance to the climatic tree line for large data sets of coordinates (World Geodetic System 1984) with geographical uncertainty. The default thresholds and the treeline definition is based on Paulsen and Körner (2014) <doi:10.1007/s00035-014-0124-0>, users are free to decide what climate layers they would like to use.
License: MIT + file LICENSE
URL: https://github.com/LivioBaetscher/ElevDistr
BugReports: https://github.com/LivioBaetscher/ElevDistr/issues
Depends: R (>= 3.5.0)
Imports: ggmap, ggplot2, RANN, terra
Suggests: knitr, rgbif, rmarkdown, testthat (>= 3.0.0), tidyverse
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2024-04-12 16:30:12 UTC; Livio
Author: Livio Baetscher [aut, cre] , Jurriaan M. de Vos [aut]
Maintainer: Livio Baetscher <livio_999@hotmail.com>
Repository: CRAN
Date/Publication: 2024-04-14 19:10:02 UTC

More information about ElevDistr at CRAN
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Package rzmq updated to version 0.9.13 with previous version 0.9.12 dated 2023-12-04

Title: R Bindings for 'ZeroMQ'
Description: Interface to the 'ZeroMQ' lightweight messaging kernel (see <https://zeromq.org/> for more information).
Author: Whit Armstrong [aut], Michael Schubert [ctb], Jeroen Ooms [aut, cre]
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between rzmq versions 0.9.12 dated 2023-12-04 and 0.9.13 dated 2024-04-14

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Package paran updated to version 1.5.3 with previous version 1.5.2 dated 2018-10-14

Title: Horn's Test of Principal Components/Factors
Description: An implementation of Horn's technique for numerically and graphically evaluating the components or factors retained in a principle components analysis (PCA) or common factor analysis (FA). Horn's method contrasts eigenvalues produced through a PCA or FA on a number of random data sets of uncorrelated variables with the same number of variables and observations as the experimental or observational data set to produce eigenvalues for components or factors that are adjusted for the sample error-induced inflation. Components with adjusted eigenvalues greater than one are retained. paran may also be used to conduct parallel analysis following Glorfeld's (1995) suggestions to reduce the likelihood of over-retention.
Author: Alexis Dinno
Maintainer: Alexis Dinno <alexis.dinno@pdx.edu>

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Package opGMMassessment updated to version 0.4 with previous version 0.3.6 dated 2024-03-28

Title: Optimized Automated Gaussian Mixture Assessment
Description: Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) <doi:10.1016/j.imu.2022.101113>.
Author: Jorn Lotsch [aut,cre] , Sebastian Malkusch [aut] , Martin Maechler [ctb], Peter Rousseeuw [ctb], Anja Struyf [ctb], Mia Hubert [ctb], Kurt Hornik [ctb]
Maintainer: Jorn Lotsch <j.lotsch@em.uni-frankfurt.de>

Diff between opGMMassessment versions 0.3.6 dated 2024-03-28 and 0.4 dated 2024-04-14

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Package ntsDists updated to version 2.1.0 with previous version 2.0.0 dated 2023-12-01

Title: Neutrosophic Distributions
Description: Computes the pdf, cdf, quantile function and generating random numbers for neutrosophic distributions. This family have been developed by different authors in the recent years. See Patro and Smarandache (2016) <doi:10.5281/zenodo.571153> and Rao et al (2023) <doi:10.5281/zenodo.7832786>.
Author: Danial Mazarei [aut, cre] , Mina Norouzirad [aut] , Amin Roshani [aut] , Gadde Srinivasa Rao [ctb] , Foad Esmaeili [ctb] , FCT, I.P. [fnd] )
Maintainer: Danial Mazarei <danial.mazarei@gmail.com>

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Package ldamatch updated to version 1.0.3 with previous version 1.0.2 dated 2021-05-23

Title: Selection of Statistically Similar Research Groups
Description: Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.
Author: Kyle Gorman [aut, cre], Geza Kiss [aut]
Maintainer: Kyle Gorman <kylebgorman@gmail.com>

Diff between ldamatch versions 1.0.2 dated 2021-05-23 and 1.0.3 dated 2024-04-14

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Package EDOtrans updated to version 0.2.5 with previous version 0.2.2 dated 2022-12-18

Title: Euclidean Distance-Optimized Data Transformation
Description: A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance.
Author: Jorn Lotsch[aut,cre], Alfred Ultsch[aut]
Maintainer: Jorn Lotsch <j.lotsch@em.uni-frankfurt.de>

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Package betaSandwich updated to version 1.0.7 with previous version 1.0.6 dated 2023-10-14

Title: Robust Confidence Intervals for Standardized Regression Coefficients
Description: Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>. The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.
Author: Ivan Jacob Agaloos Pesigan [aut, cre, cph]
Maintainer: Ivan Jacob Agaloos Pesigan <r.jeksterslab@gmail.com>

Diff between betaSandwich versions 1.0.6 dated 2023-10-14 and 1.0.7 dated 2024-04-14

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Package betaNB updated to version 1.0.4 with previous version 1.0.3 dated 2023-10-14

Title: Bootstrap for Regression Effect Sizes
Description: Generates nonparametric bootstrap confidence intervals (Efron & Tibshirani, 1993: <doi:10.1201/9780429246593>) for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm().
Author: Ivan Jacob Agaloos Pesigan [aut, cre, cph]
Maintainer: Ivan Jacob Agaloos Pesigan <r.jeksterslab@gmail.com>

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Package betaMC updated to version 1.3.2 with previous version 1.3.1 dated 2023-10-14

Title: Monte Carlo for Regression Effect Sizes
Description: Generates Monte Carlo confidence intervals for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm(). 'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect (Pesigan and Cheung, 2023 <doi:10.3758/s13428-023-02114-4>) and the sampling covariance matrix of regression coefficients (Dudgeon, 2017 <doi:10.1007/s11336-017-9563-z>) to generate confidence intervals effect sizes in regression.
Author: Ivan Jacob Agaloos Pesigan [aut, cre, cph]
Maintainer: Ivan Jacob Agaloos Pesigan <r.jeksterslab@gmail.com>

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Package betaDelta updated to version 1.0.5 with previous version 1.0.4 dated 2023-11-03

Title: Confidence Intervals for Standardized Regression Coefficients
Description: Generates confidence intervals for standardized regression coefficients using delta method standard errors for models fitted by lm() as described in Yuan and Chan (2011) <doi:10.1007/s11336-011-9224-6> and Jones and Waller (2015) <doi:10.1007/s11336-013-9380-y>. The package can also be used to generate confidence intervals for differences of standardized regression coefficients and as a general approach to performing the delta method. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.
Author: Ivan Jacob Agaloos Pesigan [aut, cre, cph]
Maintainer: Ivan Jacob Agaloos Pesigan <r.jeksterslab@gmail.com>

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Package tinycodet updated to version 0.4.6 with previous version 0.4.5 dated 2024-03-02

Title: Functions to Help in your Coding Etiquette
Description: Adds some functions to help in your coding etiquette. 'tinycodet' primarily focuses on 4 aspects. 1) Safer decimal (in)equality testing, safer atomic typecasting, standard-evaluated alternatives to with() and aes(), and other functions for safer coding. 2) A new package import system, that attempts to combine the benefits of using a package without attaching it, with the benefits of attaching a package. 3) Extending the string manipulation capabilities of the 'stringi' R package. 4) Reducing repetitive code. Besides linking to 'Rcpp', 'tinycodet' has only one other dependency, namely 'stringi'.
Author: Tony Wilkes [aut, cre, cph]
Maintainer: Tony Wilkes <tony_a_wilkes@outlook.com>

Diff between tinycodet versions 0.4.5 dated 2024-03-02 and 0.4.6 dated 2024-04-14

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Package ImpactEffectsize updated to version 0.7 with previous version 0.6.3 dated 2024-01-16

Title: Calculation and Visualization of the Impact Effect Size Measure
Description: A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups.
Author: Joern Loetsch [aut,cre] , Alfred Ultsch [aut]
Maintainer: Jorn Lotsch <j.loetsch@em.uni-frankfurt.de>

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Package opdisDownsampling updated to version 1.0 with previous version 0.8.3 dated 2023-12-13

Title: Optimal Distribution Preserving Down-Sampling of Bio-Medical Data
Description: An optimized method for distribution-preserving class-proportional down-sampling of bio-medical data.
Author: Jorn Lotsch [aut,cre] , Sebastian Malkusch [aut] , Alfred Ultsch [aut]
Maintainer: Jorn Lotsch <j.lotsch@em.uni-frankfurt.de>

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Package cancerscreening updated to version 1.1.0 with previous version 1.0.2 dated 2024-01-09

Title: Streamline Access to Cancer Screening Data
Description: Retrieve cancer screening data for cervical, breast and colorectal cancers from the Kenya Health Information System <https://hiskenya.org> in a consistent way.
Author: David Kariuki [aut, cre, cph]
Maintainer: David Kariuki <hello@damurka.com>

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Package Qval updated to version 0.1.6 with previous version 0.1.5 dated 2024-03-02

Title: The Q-Matrix Validation Methods Framework
Description: Provide a variety of Q-matrix validation methods for the generalized cognitive diagnosis models, including the method based on the generalized deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the step-wise Wald test method (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the Hull method by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the multiple logistic regression‑based Q‑matrix validation method (the MLR-B method) by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>. Different research methods during Q-matrix validating are available.
Author: Haijiang Qin [aut, cre, cph], Lei Guo [aut, cph]
Maintainer: Haijiang Qin <haijiang133@outlook.com>

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Package khisr updated to version 1.0.2 with previous version 1.0.1 dated 2024-02-05

Title: Retrieve Data from Kenya Health Information System (KHIS)
Description: Simplify data retrieval from Kenya's health system with a powerful interface for efficient data retrieval from the Kenya Health Information System (KHIS) <https://hiskenya.org>. Empower researchers, analysts, and healthcare professionals to access critical health data efficiently.
Author: David Kariuki [aut, cre, cph]
Maintainer: David Kariuki <hello@damurka.com>

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Package MRZero updated to version 0.2.0 with previous version 0.1.0 dated 2023-08-16

Title: Diet Mendelian Randomization
Description: Encodes several methods for performing Mendelian randomization analyses with summarized data. Similar to the 'MendelianRandomization' package, but with fewer bells and whistles, and less frequent updates. As described in Yavorska (2017) <doi:10.1093/ije/dyx034> and Broadbent (2020) <doi:10.12688/wellcomeopenres.16374.2>.
Author: Stephen Burgess [aut, cre] , Olena Yavorska [aut], James Staley [ctb] , Fernando Hartwig [ctb] , Jim Broadbent [ctb] , Christopher Foley [ctb] , Andrew Grant [ctb], Amy Mason [ctb] , Ting Ye [ctb] , Haoran Xue [ctb] , Zhaotong Lin [ctb] , Siqi Xu [ct [...truncated...]
Maintainer: Stephen Burgess <sb452@medschl.cam.ac.uk>

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Package zendown (with last version 0.0.1) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2024-04-12 0.0.1

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Sat, 13 Apr 2024

Package testthat updated to version 3.2.1.1 with previous version 3.2.1 dated 2023-12-02

Title: Unit Testing for R
Description: Software testing is important, but, in part because it is frustrating and boring, many of us avoid it. 'testthat' is a testing framework for R that is easy to learn and use, and integrates with your existing 'workflow'.
Author: Hadley Wickham [aut, cre], Posit Software, PBC [cph, fnd], R Core team [ctb] )
Maintainer: Hadley Wickham <hadley@posit.co>

Diff between testthat versions 3.2.1 dated 2023-12-02 and 3.2.1.1 dated 2024-04-13

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Package fanyi updated to version 0.0.7 with previous version 0.0.6 dated 2024-01-08

Title: Translate Words or Sentences via Online Translators
Description: Useful functions to translate text for multiple languages using online translators. For example, by translating error messages and descriptive analysis results into a language familiar to the user, it enables a better understanding of the information, thereby reducing the barriers caused by language. It offers several helper functions to query gene information to help interpretation of interested genes (e.g., marker genes, differential expression genes), and provides utilities to translate 'ggplot' graphics. This package is not affiliated with any of the online translators. The developers do not take responsibility for the invoice it incurs when using this package, especially for exceeding the free quota.
Author: Guangchuang Yu [aut, cre, cph] , Guannan Chen [ctb], Difei Wang [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

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Package regcensus (with last version 1.0.1) was removed from CRAN

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Package gdalBindings (with last version 0.1.17) was removed from CRAN

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2024-01-17 0.1.17
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Package rBayesianOptimization updated to version 1.2.1 with previous version 1.2.0 dated 2021-06-17

Title: Bayesian Optimization of Hyperparameters
Description: A Pure R implementation of Bayesian Global Optimization with Gaussian Processes.
Author: Yachen Yan [aut, cre]
Maintainer: Yachen Yan <yanyachen21@gmail.com>

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Package MLmetrics updated to version 1.1.3 with previous version 1.1.1 dated 2016-05-13

Title: Machine Learning Evaluation Metrics
Description: A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.
Author: Yachen Yan [aut, cre]
Maintainer: Yachen Yan <yanyachen21@gmail.com>

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Package LPM updated to version 3.1 with previous version 3.0 dated 2024-03-10

Title: Linear Parametric Models Applied to Hydrological Series
Description: Apply Univariate Long Memory Models, Apply Multivariate Short Memory Models To Hydrological Dataset, Estimate Intensity Duration Frequency curve to rainfall series. NEW -- Calculate the monthly water requirement for herbaceous and arboreal plants.
Author: Corrado Tallerini [aut, cre], Salvatore Grimaldi [aut]
Maintainer: Corrado Tallerini <corrado.tallerini@gmx.com>

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Package collapse updated to version 2.0.13 with previous version 2.0.12 dated 2024-04-01

Title: Advanced and Fast Data Transformation
Description: A C/C++ based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R. It further includes fast functions for common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It is well integrated with base R classes, 'dplyr'/' [...truncated...]
Author: Sebastian Krantz [aut, cre] , Matt Dowle [ctb], Arun Srinivasan [ctb], Morgan Jacob [ctb], Dirk Eddelbuettel [ctb], Laurent Berge [ctb], Kevin Tappe [ctb], R Core Team and contributors worldwide [ctb], Martyn Plummer [cph], 1999-2016 The R Core Team [...truncated...]
Maintainer: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>

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Package marginaleffects updated to version 0.19.0 with previous version 0.18.0 dated 2024-02-05

Title: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests
Description: Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference.
Author: Vincent Arel-Bundock [aut, cre, cph] , Noah Greifer [ctb] , Etienne Bacher [ctb]
Maintainer: Vincent Arel-Bundock <vincent.arel-bundock@umontreal.ca>

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Package fqar updated to version 0.5.3 with previous version 0.5.2 dated 2024-04-09

Title: Floristic Quality Assessment Tools for R
Description: Tools for downloading and analyzing floristic quality assessment data. See Freyman et al. (2015) <doi:10.1111/2041-210X.12491> for more information about floristic quality assessment and the associated database.
Author: Andrew Gard [aut, cre] , Alexia Myers [aut], Irene Luwabelwa [aut]
Maintainer: Andrew Gard <agard@lakeforest.edu>

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Package tables updated to version 0.9.25 with previous version 0.9.17 dated 2023-05-02

Title: Formula-Driven Table Generation
Description: Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.
Author: Duncan Murdoch
Maintainer: Duncan Murdoch <murdoch.duncan@gmail.com>

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Package mirai.promises updated to version 0.5.0 with previous version 0.4.1 dated 2023-12-05

Title: Make 'Mirai' 'Promises'
Description: Allows 'mirai' objects encapsulating asynchronous computations, from the 'mirai' package by Gao (2023) <doi:10.5281/zenodo.7912722>, to be used interchangeably with 'promise' objects from the 'promises' package by Cheng (2021) <https://CRAN.R-project.org/package=promises>. This facilitates their use with packages 'plumber' by Schloerke and Allen (2022) <https://CRAN.R-project.org/package=plumber> and 'shiny' by Cheng, Allaire, Sievert, Schloerke, Xie, Allen, McPherson, Dipert and Borges (2022) <https://CRAN.R-project.org/package=shiny>.
Author: Charlie Gao [aut, cre] , Hibiki AI Limited [cph]
Maintainer: Charlie Gao <charlie.gao@shikokuchuo.net>

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Package yatah updated to version 1.0.0 with previous version 0.2.1 dated 2022-11-12

Title: Yet Another TAxonomy Handler
Description: Provides functions to manage taxonomy when lineages are described with strings and ranks separated with special patterns like "|*__" or ";*__".
Author: Antoine Bichat [aut, cre]
Maintainer: Antoine Bichat <antoine.bichat@proton.me>

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Package CausalGPS updated to version 0.4.2 with previous version 0.4.1 dated 2023-09-29

Title: Matching on Generalized Propensity Scores with Continuous Exposures
Description: Provides a framework for estimating causal effects of a continuous exposure using observational data, and implementing matching and weighting on the generalized propensity score. Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. Matching on generalized propensity scores with continuous exposures. Journal of the American Statistical Association, pp.1-29.
Author: Naeem Khoshnevis [aut, cre] , Xiao Wu [aut] , Danielle Braun [aut]
Maintainer: Naeem Khoshnevis <nkhoshnevis@g.harvard.edu>

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Package SDPDmod updated to version 0.0.5 with previous version 0.0.4 dated 2024-03-04

Title: Spatial Dynamic Panel Data Modeling
Description: Spatial model calculation for static and dynamic panel data models, weights matrix creation and Bayesian model comparison. Bayesian model comparison methods were described by 'LeSage' (2014) <doi:10.1016/j.spasta.2014.02.002>. The 'Lee'-'Yu' transformation approach is described in 'Yu', 'De Jong' and 'Lee' (2008) <doi:10.1016/j.jeconom.2008.08.002>, 'Lee' and 'Yu' (2010) <doi:10.1016/j.jeconom.2009.08.001> and 'Lee' and 'Yu' (2010) <doi:10.1017/S0266466609100099>.
Author: Rozeta Simonovska [aut, cre]
Maintainer: Rozeta Simonovska <simonovska.r@gmail.com>

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Package atime updated to version 2024.4.12 with previous version 2024.3.5 dated 2024-03-08

Title: Asymptotic Timing
Description: Computing and visualizing comparative asymptotic timings of different algorithms and code versions. Also includes functionality for comparing empirical timings with expected references such as linear or quadratic, <https://en.wikipedia.org/wiki/Asymptotic_computational_complexity> Also includes functionality for measuring asymptotic memory and other quantities.
Author: Toby Hocking [aut, cre]
Maintainer: Toby Hocking <toby.hocking@r-project.org>

Diff between atime versions 2024.3.5 dated 2024-03-08 and 2024.4.12 dated 2024-04-13

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Package kerastuneR updated to version 0.1.0.7 with previous version 0.1.0.6 dated 2023-09-03

Title: Interface to 'Keras Tuner'
Description: 'Keras Tuner' <https://keras-team.github.io/keras-tuner/> is a hypertuning framework made for humans. It aims at making the life of AI practitioners, hypertuner algorithm creators and model designers as simple as possible by providing them with a clean and easy to use API for hypertuning. 'Keras Tuner' makes moving from a base model to a hypertuned one quick and easy by only requiring you to change a few lines of code.
Author: Turgut Abdullayev [aut, cre], Google Inc. [cph]
Maintainer: Turgut Abdullayev <turqut.a.314@gmail.com>

Diff between kerastuneR versions 0.1.0.6 dated 2023-09-03 and 0.1.0.7 dated 2024-04-13

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Package forestploter updated to version 1.1.2 with previous version 1.1.1 dated 2023-09-23

Title: Create a Flexible Forest Plot
Description: Create a forest plot based on the layout of the data. Confidence intervals in multiple columns by groups can be done easily. Editing the plot, inserting/adding text, applying a theme to the plot, and much more.
Author: Alimu Dayimu [aut, cre]
Maintainer: Alimu Dayimu <ad938@cam.ac.uk>

Diff between forestploter versions 1.1.1 dated 2023-09-23 and 1.1.2 dated 2024-04-13

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Package wikiTools updated to version 1.2.4 with previous version 0.0.6 dated 2022-03-25

Title: Tools for Wikidata and Wikipedia
Description: A set of wrappers intended to check, read and download information from the Wikimedia sources. It is specifically created to work with names of celebrities, in which case their information and statistics can be downloaded. Additionally, it also builds links and snippets to use in combination with the function gallery() in netCoin package.
Author: Modesto Escobar [aut, cph, cre] , Angel Zazo [aut], Carlos Prieto [aut] , David Barrios [aut], Cristina Calvo [aut]
Maintainer: Modesto Escobar <modesto@usal.es>

Diff between wikiTools versions 0.0.6 dated 2022-03-25 and 1.2.4 dated 2024-04-13

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Permanent link

Package BPrinStratTTE updated to version 0.0.7 with previous version 0.0.2 dated 2023-12-20

Title: Causal Effects in Principal Strata Defined by Antidrug Antibodies
Description: Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event setting.
Author: Christian Stock [aut, cre] , Emma Torrini [aut], Boehringer Ingelheim Pharma GmbH & Co. KG [cph, fnd]
Maintainer: Christian Stock <christian.stock@boehringer-ingelheim.com>

Diff between BPrinStratTTE versions 0.0.2 dated 2023-12-20 and 0.0.7 dated 2024-04-13

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New package optimall with initial version 1.1.0
Package: optimall
Title: Allocate Samples Among Strata
Version: 1.1.0
Maintainer: Jasper Yang <jbyang@uw.edu>
Description: Functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) <doi:10.2307/2342192> or Wright (2012) <doi:10.1080/00031305.2012.733679> allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits.
License: GPL-3
URL: https://github.com/yangjasp/optimall
BugReports: https://github.com/yangjasp/optimall/issues
Depends: R (>= 3.5.0)
Imports: dplyr (>= 1.0.5), glue (>= 1.4.0), magrittr (>= 2.0.0), methods (>= 4.0.0), rlang (>= 0.2.2), stats (>= 4.0.2), tibble (>= 1.4.2), utils (>= 3.5.0),
Suggests: bslib (>= 0.2.4), DiagrammeR (>= 1.0.0), DT (>= 0.15), datasets, globals (>= 0.12), knitr (>= 1.28), rmarkdown (>= 2.7), shiny (>= 1.6.0), shinytest (>= 1.4.0), survey (>= 4.0), testthat (>= 3.0.2), webshot (>= 0.5)
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2024-04-12 18:28:50 UTC; jasper
Author: Jasper Yang [aut, cre], Pamela Shaw [aut], Bryan Shepherd [ctb], Thomas Lumley [ctb], Gustavo Amorim [rev]
Repository: CRAN
Date/Publication: 2024-04-13 07:50:02 UTC

More information about optimall at CRAN
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Package Families updated to version 2.0.2 with previous version 2.0.1 dated 2024-03-28

Title: Kinship Ties in (Virtual) Multi-Generation Populations
Description: Tools to study lineages, grandparenthood, loss of close relatives, kinship networks and other topics in multi-generation populations.
Author: Frans Willekens [aut, cre]
Maintainer: Frans Willekens <willekens@nidi.nl>

Diff between Families versions 2.0.1 dated 2024-03-28 and 2.0.2 dated 2024-04-13

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Package bqror updated to version 1.7.0 with previous version 1.6.1 dated 2023-05-06

Title: Bayesian Quantile Regression for Ordinal Models
Description: Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures – logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models. Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24 <doi: 10.1214/15-BA939>. Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” St [...truncated...]
Author: Mohammad Arshad Rahman Developer [aut], Prajual Maheshwari [cre]
Maintainer: Prajual Maheshwari <prajual1391@gmail.com>

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Package scorecard updated to version 0.4.4 with previous version 0.4.3 dated 2023-08-08

Title: Credit Risk Scorecard
Description: The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.
Author: Shichen Xie [aut, cre]
Maintainer: Shichen Xie <xie@shichen.name>

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Package mapmisc (with last version 2.0.9) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2024-04-11 2.0.9

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Package PDXpower (with last version 1.0.1) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2024-04-01 1.0.1
2024-03-11 1.0.0

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Package frailtypack (with last version 3.6.0) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2024-03-15 3.6.0
2023-11-25 3.5.1

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Package rtpcr updated to version 1.0.3 with previous version 1.0.2 dated 2024-04-10

Title: qPCR Data Analysis
Description: Various methods are employed for statistical analysis and graphical presentation of real-time PCR (quantitative PCR or qPCR) data. 'rtpcr' handles amplification efficiency calculation, statistical analysis and graphical representation of real-time PCR data based on up to two reference genes. By accounting for amplification efficiency values, 'rtpcr' was developed using a general calculation method described by Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5>, covering both the Livak and Pfaffl methods. Based on the experimental conditions, the functions of the 'rtpcr' package use t-test (for experiments with a two-level factor) or analysis of variance (for cases where more than two levels or factors exist) to calculate the fold change or relative expression. The functions also provide standard deviations and confidence limits for means and apply statistical mean comparisons. To facilitate using 'rtpcr', different datasets have been employed in the examples and the outputs ar [...truncated...]
Author: Ghader Mirzaghaderi [aut, cre, cph]
Maintainer: Ghader Mirzaghaderi <gh.mirzaghaderi@uok.ac.ir>

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Package KEPTED updated to version 0.2.0 with previous version 0.1.1 dated 2024-04-11

Title: Kernel-Embedding-of-Probability Test for Elliptical Distribution
Description: Provides an implementation of a kernel-embedding of probability test for elliptical distribution. This is an asymptotic test for elliptical distribution under general alternatives, and the location and shape parameters are assumed to be unknown. Some side-products are posted, including the transformation between rectangular and polar coordinates and two product-type kernel functions. See Tang and Li (2024) <doi:10.48550/arXiv.2306.10594> for details.
Author: Yin Tang [aut, cre], Bing Li [cph, aut]
Maintainer: Yin Tang <yqt5219@psu.edu>

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Fri, 12 Apr 2024

Package psborrow2 updated to version 0.0.3.3 with previous version 0.0.3.2 dated 2024-04-03

Title: Bayesian Dynamic Borrowing Analysis and Simulation
Description: Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and a [...truncated...]
Author: Matt Secrest [aut, cre] , Isaac Gravestock [aut], Craig Gower-Page [ctb], Manoj Khanal [ctb], Mingyang Shan [ctb], Kexin Jin [ctb], Zhi Yang [ctb], Genentech, Inc. [cph, fnd]
Maintainer: Matt Secrest <secrestm@gene.com>

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New package konfound with initial version 0.5.1
Package: konfound
Title: Quantify the Robustness of Causal Inferences
Version: 0.5.1
Description: Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al. (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with null hypothesis cases to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such [...truncated...]
License: MIT + file LICENSE
URL: https://github.com/konfound-project/konfound
BugReports: https://github.com/konfound-project/konfound/issues
Depends: R (>= 2.10)
Imports: broom, broom.mixed, crayon, dplyr, ggplot2, lavaan, purrr, rlang, tidyr, lme4 (>= 1.1-35.1), tibble, ggrepel, pbkrtest
Suggests: covr, devtools, forcats, knitr, rmarkdown, mice, roxygen2, testthat, Matrix (>= 1.6-2)
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2024-04-12 19:45:19 UTC; jrosenb8
Author: Joshua M Rosenberg [aut, cre], Ran Xu [ctb], Qinyun Lin [ctb], Spiro Maroulis [ctb], Sarah Narvaiz [ctb], Kenneth A Frank [ctb], Wei Wang [ctb]
Maintainer: Joshua M Rosenberg <jmrosen48@gmail.com>
Repository: CRAN
Date/Publication: 2024-04-12 21:10:09 UTC

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Package CIfinder updated to version 2.0.0 with previous version 1.0.2 dated 2023-10-30

Title: Estimate the Confidence Intervals for Predictive Values
Description: Computes confidence intervals for the positive predictive value (PPV) and negative predictive value (NPV) based on varied scenarios. In situations where the proportion of diseased subjects does not correspond to the disease prevalence (e.g. case-control studies), this package provides two types of solutions: 1) five methods for estimating confidence intervals for PPV and NPV via ratio of two binomial proportions including Gart & Nam (1988), Walter (1975), MOVER-J (Laud, 2017), Fieller (1954), and Bootstrap (Efron, 1979); 2) three direct methods that compute the confidence intervals including Pepe (2003), Zhou (2007), and Delta. In prospective studies where the proportion of diseased subjects is an unbiased estimate of the disease prevalence, this package provides several methods for calculating the confidence intervals for PPV and NPV including Clopper-Pearson, Wald, Wilson, Agresti-Coull, and Beta. See the Details and References sections in the corresponding functions.
Author: Dadong Zhang <dzhang4@illumina.com>, Jingye Wang <jwang16@illumina.com>, Suqin Cai <scai1@illumina.com>, Johan Surtihadi <jsurtihadi@illumina.com>
Maintainer: Dadong Zhang <dzhang4@illumina.com>

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Package ThresholdROC updated to version 2.9.4 with previous version 2.9.3 dated 2024-03-21

Title: Optimum Threshold Estimation
Description: Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) <doi:10.18637/jss.v082.i04> and in Skaltsa et al (2010, 2012) <doi:10.1002/bimj.200900294>, <doi:10.1002/sim.4369>.
Author: Sara Perez-Jaume [aut, cre], Natalia Pallares [aut], Konstantina Skaltsa [aut]
Maintainer: Sara Perez-Jaume <spjaume@gmail.com>

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Package ppitables updated to version 0.5.5 with previous version 0.5.4 dated 2020-10-25

Title: Lookup Tables to Generate Poverty Likelihoods and Rates using the Poverty Probability Index (PPI)
Description: The Poverty Probability Index (PPI) is a poverty measurement tool for organizations and businesses with a mission to serve the poor. The PPI is statistically-sound, yet simple to use: the answers to 10 questions about a household’s characteristics and asset ownership are scored to compute the likelihood that the household is living below the poverty line – or above by only a narrow margin. This package contains country-specific lookup data tables used as reference to determine the poverty likelihood of a household based on their score from the country-specific PPI questionnaire. These lookup tables have been extracted from documentation of the PPI found at <https://www.povertyindex.org> and managed by Innovations for Poverty Action <https://poverty-action.org/>.
Author: Ernest Guevarra [aut, cre]
Maintainer: Ernest Guevarra <ernestgmd@gmail.com>

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Package supportR updated to version 1.3.0 with previous version 1.2.0 dated 2023-08-22

Title: Support Functions for Wrangling and Visualization
Description: Suite of helper functions for data wrangling and visualization. The only theme for these functions is that they tend towards simple, short, and narrowly-scoped. These functions are built for tasks that often recur but are not large enough in scope to warrant an ecosystem of interdependent functions.
Author: Nicholas J Lyon [aut, cre, cph]
Maintainer: Nicholas J Lyon <njlyon@alumni.iastate.edu>

Diff between supportR versions 1.2.0 dated 2023-08-22 and 1.3.0 dated 2024-04-12

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Package gpindex updated to version 0.6.1 with previous version 0.6.0 dated 2023-11-15

Title: Generalized Price and Quantity Indexes
Description: Tools to build and work with bilateral generalized-mean price indexes (and by extension quantity indexes), and indexes composed of generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS). Covers the core mathematical machinery for making bilateral price indexes, computing price relatives, detecting outliers, and decomposing indexes, with wrappers for all common (and many uncommon) index-number formulas. Implements and extends many of the methods in Balk (2008, ISBN:978-1-107-40496-0), von der Lippe (2001, ISBN:3-8246-0638-0), and the CPI manual (2020, ISBN:978-1-51354-298-0).
Author: Steve Martin [aut, cre, cph]
Maintainer: Steve Martin <marberts@protonmail.com>

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Package dunn.test updated to version 1.3.6 with previous version 1.3.5 dated 2017-10-27

Title: Dunn's Test of Multiple Comparisons Using Rank Sums
Description: Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for 0th-order stochastic dominance among k groups (Kruskal and Wallis, 1952). 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference and for mean difference. 'dunn.test' accounts for tied ranks.
Author: Alexis Dinno
Maintainer: Alexis Dinno <alexis.dinno@pdx.edu>

Diff between dunn.test versions 1.3.5 dated 2017-10-27 and 1.3.6 dated 2024-04-12

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Package survstan updated to version 0.0.7.1 with previous version 0.0.7 dated 2024-03-20

Title: Fitting Survival Regression Models via 'Stan'
Description: Parametric survival regression models under the maximum likelihood approach via 'Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) <ISBN:9780471372158>; Bennett (1982) <doi:10.1002/sim.4780020223>; Chen and Wang(2000) <doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink (2021) <doi:10.1214/20-BJPS471>.
Author: Fabio Demarqui [aut, cre, cph] , Andrew Johnson [ctb]
Maintainer: Fabio Demarqui <fndemarqui@est.ufmg.br>

Diff between survstan versions 0.0.7 dated 2024-03-20 and 0.0.7.1 dated 2024-04-12

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New package zendown with initial version 0.0.1
Package: zendown
Title: Access Files from 'Zenodo' Deposits
Version: 0.0.1
Description: Access, download and locally cache files deposited on 'Zenodo' <https://zenodo.org>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: checkmate, cli, curl, fs, glue, httr2, rappdirs, RCurl, tibble, tidyr, tools, utils
Suggests: testthat (>= 3.0.0)
URL: https://rfsaldanha.github.io/zendown/
NeedsCompilation: no
Packaged: 2024-04-12 14:28:30 UTC; raphaelsaldanha
Author: Raphael Saldanha [aut, cre]
Maintainer: Raphael Saldanha <raphael.de-freitas-saldanha@inria.fr>
Repository: CRAN
Date/Publication: 2024-04-12 15:30:02 UTC

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New package Toothnroll with initial version 1.11
Package: Toothnroll
Title: Dental Tissues Landmarking Measuring and Mapping
Version: 1.11
Description: Two- and three-dimensional morphometric maps of enamel and dentine thickness and multivariate analysis. Volume calculation of dental materials. Principal component analysis of thickness maps with associated morphometric map variations.
Imports: Arothron (>= 1.0), alphashape3d (>= 1.0), compositions(>= 2.0), morphomap (>= 1.0), stringr (>= 1.0), vegan,lattice (>= 0.2), mgcv (>= 1.8), Rvcg (>= 0.18), Morpho (>= 2.0), oce (>= 1.1), rgl (>= 0.1), geometry (>= 0.4.0), colorRamps (>= 2.3), DescTools (>= 0.99), grDevices (>= 3.5), graphics (>= 3.5)
License: GPL-2
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-04-12 09:27:42 UTC; anton
Author: Antonio Profico [cre, aut], Mathilde Augoyard [aut]
Maintainer: Antonio Profico <antonio.profico@gmail.com>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2024-04-12 15:20:22 UTC

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New package relcircle with initial version 1.0
Package: relcircle
Title: Draw Regulatory Relationships Between Genes
Version: 1.0
Date: 2024-04-07
Description: According to the order of the loci on the chromosome, the loci can be connected according to the interrelationship between them and classified according to different locus types.
License: GPL (>= 3)
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2024-04-12 11:17:36 UTC; admin
Author: Ruilin Li [aut, cre]
Maintainer: Ruilin Li <2837471734@qq.com>
Repository: CRAN
Date/Publication: 2024-04-12 15:20:05 UTC

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Package rasterpic updated to version 0.2.5 with previous version 0.2.4 dated 2024-01-18

Title: Convert Digital Images into 'SpatRaster' Objects
Description: Generate 'SpatRaster' objects, as defined by the 'terra' package, from digital images, using a specified spatial object as a geographical reference.
Author: Diego Hernangomez [aut, cre, cph]
Maintainer: Diego Hernangomez <diego.hernangomezherrero@gmail.com>

Diff between rasterpic versions 0.2.4 dated 2024-01-18 and 0.2.5 dated 2024-04-12

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New package heck with initial version 0.1.0
Package: heck
Title: Highly Performant String Case Converter
Version: 0.1.0
Description: Provides a case conversion between common cases like CamelCase and snake_case. Using the 'rust crate heck' <https://github.com/withoutboats/heck> as the backend for a highly performant case conversion for 'R'.
License: MIT + file LICENSE
Encoding: UTF-8
SystemRequirements: Cargo (Rust's package manager), rustc
Suggests: spelling, testthat (>= 3.0.0)
Language: en-US
NeedsCompilation: yes
Packaged: 2024-04-12 09:15:56 UTC; dyfanjones
Author: Josiah Parry [aut] , Dyfan Jones [cre]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>
Repository: CRAN
Date/Publication: 2024-04-12 15:20:10 UTC

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New package basksim with initial version 1.0.0
Package: basksim
Title: Simulation-Based Calculation of Basket Trial Operating Characteristics
Version: 1.0.0
Description: Provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988>, Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>, Berry et al. (2020) <doi:10.1177/1740774513497539>, Neuenschwander et al. (2016) <doi:10.1002/pst.1730> and Psioda et al. (2021) <doi:10.1093/biostatistics/kxz014>. For the latter three designs, the functions are mostly wrappers for functions provided by the packages 'bhmbasket' and 'bmabasket'.
License: GPL (>= 3)
Encoding: UTF-8
Imports: arrangements, bhmbasket, bmabasket, doFuture, extraDistr, foreach, HDInterval, progressr
Suggests: covr, testthat (>= 3.0.0)
URL: https://github.com/lbau7/basksim
BugReports: https://github.com/lbau7/basksim/issues
NeedsCompilation: no
Packaged: 2024-04-12 13:08:31 UTC; uy240
Author: Lukas Baumann [aut, cre] , Lukas Sauer [ctb]
Maintainer: Lukas Baumann <baumann@imbi.uni-heidelberg.de>
Repository: CRAN
Date/Publication: 2024-04-12 15:20:14 UTC

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New package avotrex with initial version 1.0.0
Package: avotrex
Version: 1.0.0
Title: A Global Dataset of Anthropogenic Extinct Birds and their Traits: Phylogeny Builder
Description: Grafts the extinct bird species from the 'Avotrex' database (Sayol et al., in review) on to the 'BirdTree' phylogenies <https://birdtree.org>, using a set of different commands.
Encoding: UTF-8
Depends: R (>= 4.0.0)
LazyData: true
Imports: ape, doParallel, doSNOW, dplyr, foreach, parallel, phytools, snow, stringr, tidytree (>= 0.4.6), utils
License: GPL-3
Suggests: testthat, markdown, knitr, rmarkdown, covr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-04-12 14:55:35 UTC; Joe
Author: Joseph Wayman [aut, cre] , Thomas J. Matthews [aut] , Paul Dufour [ctb], Ferran Sayol [ctb]
Maintainer: Joseph Wayman <j.wayman@bham.ac.uk>
Repository: CRAN
Date/Publication: 2024-04-12 15:30:05 UTC

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New package amregtest with initial version 1.0.3
Package: amregtest
Title: Runs Allelematch Regression Tests
Version: 1.0.3
Description: Automates regression testing of package 'allelematch'. Over 2500 tests covers all functions in 'allelematch', reproduces the examples from the documentation and includes negative tests. The implementation is based on 'testthat'.
License: MIT + file LICENSE
Depends: R (>= 2.10)
Imports: allelematch, digest, remotes, testthat (>= 3.0.0), utils, withr
Encoding: UTF-8
Language: en-US
LazyData: true
NeedsCompilation: no
Packaged: 2024-04-12 13:58:14 UTC; Torva
Author: Department of Wildlife, Fish and Environmental Studies at Swedish University of Agricultural Sciences [cph], Goeran Spong [cph] , Paul Galpern [ctb] , Torvald Staxler [aut, cre]
Maintainer: Torvald Staxler <torvald.staxler@telia.com>
Repository: CRAN
Date/Publication: 2024-04-12 15:30:09 UTC

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Package NormData updated to version 1.1 with previous version 1.0 dated 2024-02-06

Title: Derivation of Regression-Based Normative Data
Description: Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.
Author: Wim Van der Elst [aut, cre]
Maintainer: Wim Van der Elst <Wim.vanderelst@gmail.com>

Diff between NormData versions 1.0 dated 2024-02-06 and 1.1 dated 2024-04-12

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Package RadData updated to version 1.0.2 with previous version 1.0.1 dated 2021-04-13

Title: Nuclear Decay Data for Dosimetric Calculations - ICRP 107
Description: Nuclear Decay Data for Dosimetric Calculations from the International Commission on Radiological Protection from ICRP Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 <doi:10.1016/j.icrp.2008.10.004> <https://www.icrp.org/publication.asp?id=ICRP%20Publication%20107>. This is a database of the physical data needed in calculations of radionuclide-specific protection and operational quantities. The data is prescribed by the ICRP, the international authority on radiation dose standards, for estimating dose from the intake of or exposure to radionuclides in the workplace and the environment. The database contains information on the half-lives, decay chains, and yields and energies of radiations emitted in nuclear transformations of 1252 radionuclides of 97 elements.
Author: Mark Hogue [aut, cre], KF Eckerman [dtc, cph], A Endo [dtc, cph]
Maintainer: Mark Hogue <mark.hogue.chp@gmail.com>

Diff between RadData versions 1.0.1 dated 2021-04-13 and 1.0.2 dated 2024-04-12

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Package glmtoolbox updated to version 0.1.11 with previous version 0.1.10 dated 2024-01-13

Title: Set of Tools to Data Analysis using Generalized Linear Models
Description: Set of tools for the statistical analysis of data using: (1) normal linear models; (2) generalized linear models; (3) negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion; (4) beta-binomial and random-clumped binomial regression models as alternative to the binomial regression models under the presence of overdispersion; (5) Zero-inflated and zero-altered regression models to deal with zero-excess in count data; (6) generalized nonlinear models; (7) generalized estimating equations for cluster correlated data.
Author: Luis Hernando Vanegas [aut, cre], Luz Marina Rondon [aut], Gilberto A. Paula [aut]
Maintainer: Luis Hernando Vanegas <lhvanegasp@unal.edu.co>

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Package mcboost updated to version 0.4.3 with previous version 0.4.2 dated 2022-08-18

Title: Multi-Calibration Boosting
Description: Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and 'Multi-Accuracy Boosting' (2019) <doi:10.48550/arXiv.1805.12317> for the multi-calibration of a machine learning model's prediction. 'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.
Author: Florian Pfisterer [aut] , Susanne Dandl [ctb] , Christoph Kern [ctb] , Carolin Becker [ctb], Bernd Bischl [ctb] , Sebastian Fischer [ctb, cre]
Maintainer: Sebastian Fischer <sebf.fischer@gmail.com>

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Package cheapr updated to version 0.8.0 with previous version 0.5.0 dated 2024-04-05

Title: Simple Functions to Save Time and Memory
Description: Fast and memory-efficient (or 'cheap') tools to facilitate efficient programming, saving time and memory. It aims to provide 'cheaper' alternatives to common base R functions, as well as some additional functions.
Author: Nick Christofides [aut, cre]
Maintainer: Nick Christofides <nick.christofides.r@gmail.com>

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Package CatastRo updated to version 0.3.1 with previous version 0.3.0 dated 2024-01-18

Title: Interface to the API 'Sede Electronica Del Catastro'
Description: Access public spatial data available under the 'INSPIRE' directive. Tools for downloading references and addresses of properties, as well as map images.
Author: Angel Delgado Panadero [aut, cph] , Inaki Ucar [ctb] , Diego Hernangomez [aut, cre]
Maintainer: Diego Hernangomez <diego.hernangomezherrero@gmail.com>

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Package MAMS updated to version 2.0.2 with previous version 2.0.1 dated 2023-04-18

Title: Designing Multi-Arm Multi-Stage Studies
Description: Designing multi-arm multi-stage studies with (asymptotically) normal endpoints and known variance.
Author: Thomas Jaki [aut, cre] , Dominique-Laurent Couturier [aut] , Dominic Magirr [aut], Nikita Mozgunov [aut] , Philip Pallmann [aut]
Maintainer: Thomas Jaki <thomas.jaki@pm.me>

Diff between MAMS versions 2.0.1 dated 2023-04-18 and 2.0.2 dated 2024-04-12

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Package sparsebnUtils (with last version 0.0.8) was removed from CRAN

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2021-01-27 0.0.8
2019-05-29 0.0.7
2018-03-16 0.0.6
2017-09-09 0.0.5
2017-03-12 0.0.4
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Package margins (with last version 0.3.26.1) was removed from CRAN

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2024-03-24 0.3.26.1
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Package ensurer (with last version 1.1) was removed from CRAN

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Package discretecdAlgorithm (with last version 0.0.7) was removed from CRAN

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2019-03-15 0.1-0

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Package konfound (with last version 0.5.0) was removed from CRAN

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Package prediction (with last version 0.3.14) was removed from CRAN

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2018-05-22 0.3.6
2018-04-12 0.3.2
2017-04-19 0.2.0
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Package assertive.sets (with last version 0.0-3) was removed from CRAN

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2012-03-13 1.0-20
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Package mmstat4 updated to version 0.2.0 with previous version 0.1.6 dated 2023-09-29

Title: Access to Teaching Materials from a ZIP File or GitHub
Description: Provides access to teaching materials for various statistics courses, including R and Python programs, Shiny apps, data, and PDF/HTML documents. These materials are stored on the Internet as a ZIP file (e.g., in a GitHub repository) and can be downloaded and displayed or run locally. The content of the ZIP file is temporarily or permanently stored. By default, the package uses the GitHub repository 'sigbertklinke/mmstat4.data.' Additionally, the package includes 'association_measures.R' from the archived package 'ryouready' by Mark Heckman and some auxiliary functions.
Author: Sigbert Klinke [aut, cre] , Jekaterina Zukovska [ctb]
Maintainer: Sigbert Klinke <sigbert@hu-berlin.de>

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Package MendelianRandomization updated to version 0.10.0 with previous version 0.9.0 dated 2023-08-08

Title: Mendelian Randomization Package
Description: Encodes several methods for performing Mendelian randomization analyses with summarized data. Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods.
Author: Stephen Burgess [aut, cre] , Olena Yavorska [aut], James Staley [ctb] , Fernando Hartwig [ctb] , Jim Broadbent [ctb] , Christopher Foley [ctb] , Andrew Grant [ctb], Amy Mason [ctb] , Ting Ye [ctb] , Haoran Xue [ctb] , Zhaotong Lin [ctb] , Siqi Xu [ct [...truncated...]
Maintainer: Stephen Burgess <sb452@medschl.cam.ac.uk>

Diff between MendelianRandomization versions 0.9.0 dated 2023-08-08 and 0.10.0 dated 2024-04-12

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Package dexter updated to version 1.4.2 with previous version 1.4.0 dated 2024-01-19

Title: Data Management and Analysis of Tests
Description: A system for the management, assessment, and psychometric analysis of data from educational and psychological tests.
Author: Gunter Maris [aut], Timo Bechger [aut], Jesse Koops [aut, cre], Ivailo Partchev [aut]
Maintainer: Jesse Koops <jesse.koops@cito.nl>

Diff between dexter versions 1.4.0 dated 2024-01-19 and 1.4.2 dated 2024-04-12

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New package smplot2 with initial version 0.1.0
Package: smplot2
Title: Creating and Annotating a Composite Plot in 'ggplot2'
Version: 0.1.0
Maintainer: Seung Hyun (Sam) Min <seung.min@mail.mcgill.ca>
Description: Provides functions for creating and annotating a composite plot in 'ggplot2'. Offers background themes and shortcut plotting functions that produce figures that are appropriate for the format of scientific journals. Some methods are described in Min and Zhou (2021) <doi:10.3389/fgene.2021.802894>.
Encoding: UTF-8
License: GPL-2
Imports: ggplot2, ggpubr, cowplot, tibble, dplyr, gghalves, magrittr, utils, pwr, sdamr, stats, Hmisc, ggstance, zoo, patchwork
URL: https://smin95.github.io/dataviz/
BugReports: https://github.com/smin95/smplot2/issues
NeedsCompilation: no
Packaged: 2024-04-12 00:28:04 UTC; XPS
Author: Seung Hyun Min [aut, cre]
Repository: CRAN
Date/Publication: 2024-04-12 09:00:02 UTC

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Package gspcr updated to version 0.9.5 with previous version 0.9.4.1 dated 2023-11-21

Title: Generalized Supervised Principal Component Regression
Description: Generalization of supervised principal component regression (SPCR; Bair et al., 2006, <doi:10.1198/016214505000000628>) to support continuous, binary, and discrete variables as outcomes and predictors (inspired by the 'superpc' R package <https://cran.r-project.org/package=superpc>).
Author: Edoardo Costantini [aut, cre]
Maintainer: Edoardo Costantini <costantini.edoardo@yahoo.com>

Diff between gspcr versions 0.9.4.1 dated 2023-11-21 and 0.9.5 dated 2024-04-12

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Package FunnelPlotR updated to version 0.5.0 with previous version 0.4.2 dated 2023-06-01

Title: Funnel Plots for Comparing Institutional Performance
Description: An implementation of methods presented by Spiegelhalter (2005) <doi:10.1002/sim.1970> Funnel plots for comparing institutional performance, for standardised ratios, ratios of counts and proportions with additive overdispersion adjustment.
Author: Chris Mainey [aut, cre] , Andrew Johnson [ctb], Matthew Bass [ctb], NHS-R Community [cph]
Maintainer: Chris Mainey <c.mainey1@nhs.net>

Diff between FunnelPlotR versions 0.4.2 dated 2023-06-01 and 0.5.0 dated 2024-04-12

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New package DIFM with initial version 1.0
Package: DIFM
Title: Dynamic ICAR Spatiotemporal Factor Models
Version: 1.0
Date: 2024-04-07
Description: Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.10), Matrix, LaplacesDemon, spdep, gridExtra, sp
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
Depends: R (>= 2.10)
LazyData: true
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2024-04-11 21:02:56 UTC; Hwasoo Shin
Author: Hwasoo Shin [aut, cre], Marco Ferreira [aut]
Maintainer: Hwasoo Shin <shwasoo@vt.edu>
Repository: CRAN
Date/Publication: 2024-04-12 08:50:03 UTC

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Package campsis updated to version 1.5.2 with previous version 1.5.1 dated 2024-02-16

Title: Generic PK/PD Simulation Platform CAMPSIS
Description: A generic, easy-to-use and intuitive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on R packages 'rxode2', 'RxODE' and 'mrgsolve'. CAMPSIS provides an abstraction layer over the underlying processes of writing a PK/PD model, assembling a custom dataset and running a simulation. CAMPSIS has a strong dependency to the R package 'campsismod', which allows to read/write a model from/to files and adapt it further on the fly in the R environment. Package 'campsis' allows the user to assemble a dataset in an intuitive manner. Once the user’s dataset is ready, the package is in charge of preparing the simulation, calling 'rxode2', 'RxODE' or 'mrgsolve' (at the user's choice) and returning the results, for the given model, dataset and desired simulation settings.
Author: Nicolas Luyckx [aut, cre]
Maintainer: Nicolas Luyckx <nicolas.luyckx@calvagone.com>

Diff between campsis versions 1.5.1 dated 2024-02-16 and 1.5.2 dated 2024-04-12

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Package SpaDES.core (with last version 2.0.3) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2023-11-10 2.0.3
2023-07-06 2.0.2

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Package SpaDES (with last version 2.0.10) was removed from CRAN

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2024-04-05 2.0.10

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Package SpaDES.tools (with last version 2.0.5) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2023-11-10 2.0.5
2023-09-01 2.0.4
2023-05-23 2.0.0
2023-01-05 1.0.1

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Package dsfa (with last version 2.0.2) was removed from CRAN

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2023-07-19 2.0.2
2023-05-09 2.0.1
2023-02-03 2.0.0
2022-11-04 1.0.1
2022-09-26 1.0.0
2022-09-07 0.0.3

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Package reproducible (with last version 2.0.11) was removed from CRAN

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2024-03-30 2.0.11
2023-11-21 2.0.10
2023-11-09 2.0.9
2023-08-23 2.0.8
2023-08-07 2.0.7
2023-07-03 2.0.5
2023-05-31 2.0.4
2023-05-07 2.0.2
2022-12-22 1.2.16

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Package fastcpd (with last version 0.13.1) was removed from CRAN

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2024-04-01 0.13.1
2024-03-19 0.12.2
2024-03-12 0.12.1
2024-01-24 0.10.3
2024-01-09 0.10.1
2023-12-21 0.10.0
2023-12-11 0.9.9
2023-10-19 0.9.0
2023-09-23 0.7.2
2023-09-21 0.7.0
2023-09-14 0.6.2

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Package NonlinearRDD (with last version 0.0.4) was removed from CRAN

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2023-11-26 0.0.4
2023-10-27 0.0.3

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Package optimall (with last version 0.1.5) was removed from CRAN

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2024-02-15 0.1.5
2024-01-12 0.1.4
2023-09-06 0.1.3
2022-11-09 0.1.2
2022-02-09 0.1.1

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Package spOccupancy (with last version 0.7.3) was removed from CRAN

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2024-03-28 0.7.3
2023-11-01 0.7.2
2023-08-31 0.7.1
2023-08-16 0.7.0
2023-03-11 0.6.1
2023-03-03 0.6.0
2022-12-21 0.5.2
2022-12-08 0.5.1
2022-11-16 0.5.0
2022-07-13 0.4.0
2022-05-23 0.3.2
2022-04-13 0.3.1
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2022-01-07 0.2.1
2021-12-19 0.2.0
2021-11-25 0.1.3
2021-11-11 0.1.2

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Package GWalkR updated to version 0.1.4 with previous version 0.1.3 dated 2023-10-03

Title: Interactive Exploratory Data Analysis Tool
Description: Simplify your R data analysis and data visualization workflow by turning your data frame into an interactive 'Tableau'-like interface, leveraging the 'graphic-walker' JavaScript library and the 'htmlwidgets' package.
Author: Yue Yu [aut, cre] , Kanaries Data Inc. [cph, fnd]
Maintainer: Yue Yu <yue.yu@connect.ust.hk>

Diff between GWalkR versions 0.1.3 dated 2023-10-03 and 0.1.4 dated 2024-04-12

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Package washdata updated to version 0.1.4 with previous version 0.1.3 dated 2020-09-29

Title: Urban Water and Sanitation Survey Dataset
Description: Urban water and sanitation survey dataset collected by Water and Sanitation for the Urban Poor (WSUP) with technical support from Valid International. These citywide surveys have been collecting data allowing water and sanitation service levels across the entire city to be characterised, while also allowing more detailed data to be collected in areas of the city of particular interest. These surveys are intended to generate useful information for others working in the water and sanitation sector. Current release version includes datasets collected from a survey conducted in Dhaka, Bangladesh in March 2017. This survey in Dhaka is one of a series of surveys to be conducted by WSUP in various cities in which they operate including Accra, Ghana; Nakuru, Kenya; Antananarivo, Madagascar; Maputo, Mozambique; and, Lusaka, Zambia. This package will be updated once the surveys in other cities are completed and datasets have been made available.
Author: Ernest Guevarra [aut, cre], Valid International [cph], Water and Sanitation for the Urban Poor [cph]
Maintainer: Ernest Guevarra <ernestgmd@gmail.com>

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Package CGNM updated to version 0.8.0 with previous version 0.6.7 dated 2024-01-18

Title: Cluster Gauss-Newton Method
Description: Find multiple solutions of a nonlinear least squares problem. Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) <doi:10.1007/s11081-020-09571-2>. Cluster Gauss–Newton method. Optimization and Engineering, 1-31. Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) <doi:10.1002/psp4.13055>. Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67.
Author: Yasunori Aoki
Maintainer: Yasunori Aoki <yaoki@uwaterloo.ca>

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Package melt updated to version 1.11.3 with previous version 1.11.2 dated 2024-03-20

Title: Multiple Empirical Likelihood Tests
Description: Performs multiple empirical likelihood tests. It offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the 'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for parallel computation. Details of the testing procedures are provided in Kim, MacEachern, and Peruggia (2023) <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim, MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is available for further information. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.
Author: Eunseop Kim [aut, cph, cre], Steven MacEachern [ctb, ths], Mario Peruggia [ctb, ths], Pierre Chausse [rev], Alex Stringer [rev]
Maintainer: Eunseop Kim <markean@pm.me>

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Package galah updated to version 2.0.2 with previous version 2.0.1 dated 2024-02-05

Title: Biodiversity Data from the GBIF Node Network
Description: The Global Biodiversity Information Facility ('GBIF', <https://www.gbif.org>) sources data from an international network of data providers, known as 'nodes'. Several of these nodes - the "living atlases" (<https://living-atlases.gbif.org>) - maintain their own web services using software originally developed by the Atlas of Living Australia ('ALA', <https://www.ala.org.au>). 'galah' enables the R community to directly access data and resources hosted by 'GBIF' and its partner nodes.
Author: Martin Westgate [aut, cre], Matilda Stevenson [aut], Dax Kellie [aut], Peggy Newman [aut]
Maintainer: Martin Westgate <martin.westgate@csiro.au>

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Thu, 11 Apr 2024

Package logrx updated to version 0.3.1 with previous version 0.3.0 dated 2023-10-17

Title: A Logging Utility Focus on Clinical Trial Programming Workflows
Description: A utility to facilitate the logging and review of R programs in clinical trial programming workflows.
Author: Nathan Kosiba [aut, cre], Thomas Bermudez [aut], Ben Straub [aut], Michael Rimler [aut], Nicholas Masel [aut], Sam Parmar [aut], GSK/Atorus JPT [cph, fnd]
Maintainer: Nathan Kosiba <nhkosiba@gmail.com>

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Package renv updated to version 1.0.7 with previous version 1.0.6 dated 2024-04-11

Title: Project Environments
Description: A dependency management toolkit for R. Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.
Author: Kevin Ushey [aut, cre] , Hadley Wickham [aut] , Posit Software, PBC [cph, fnd]
Maintainer: Kevin Ushey <kevin@rstudio.com>

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Package KEPTED updated to version 0.1.1 with previous version 0.1.0 dated 2024-03-27

Title: Kernel-Embedding-of-Probability Test for Elliptical Distribution
Description: Provides an implementation of a kernel-embedding of probability test for elliptical distribution. This is an asymptotic test for elliptical distribution under general alternatives, and the location and shape parameters are assumed to be unknown. Some side-products are posted, including the transformation between rectangular and polar coordinates and two product-type kernel functions. See Tang and Li (2024) <arXiv:2306.10594> for details.
Author: Yin Tang [aut, cre], Bing Li [cph, aut]
Maintainer: Yin Tang <yqt5219@psu.edu>

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Package goldfish updated to version 1.6.8 with previous version 1.6.4 dated 2022-08-24

Title: Statistical Network Models for Dynamic Network Data
Description: Tools for fitting statistical network models to dynamic network data. Can be used for fitting both dynamic network actor models ('DyNAMs') and relational event models ('REMs'). Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>, Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>, Stadtfeld and Block (2017) <doi:10.15195/v4.a14>, Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.
Author: James Hollway [aut, dtc] , Christoph Stadtfeld [aut, dtc], Marion Hoffman [aut], Alvaro Uzaheta [cre, aut] , Mirko Reul [ctb], Timon Elmer [ctb], Kieran Mepham [ctb], Per Block [ctb], Xiaolei Zhang [ctb], Weigutian Ou [ctb], Emily Garvin [ctb], Siwei [...truncated...]
Maintainer: Alvaro Uzaheta <ualvaro@ethz.ch>

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Package GeDS updated to version 0.2.1 with previous version 0.2.0 dated 2024-03-24

Title: Geometrically Designed Spline Regression
Description: Spline Regression, Generalized Additive Models, and Component-wise Gradient Boosting, utilizing Geometrically Designed (GeD) Splines. GeDS regression is a non-parametric method inspired by geometric principles, for fitting spline regression models with variable knots in one or two independent variables. It efficiently estimates the number of knots and their positions, as well as the spline order, assuming the response variable follows a distribution from the exponential family. GeDS models integrate the broader category of Generalized (Non-)Linear Models, offering a flexible approach to modeling complex relationships. A description of the method can be found in Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023) <doi:10.1016/j.amc.2022.127493>. Further extending its capabilities, GeDS's implementation includes Generalized Additive Models (GAM) and Functional Gradient Boosting (FGB), enabling versatile multivariate predictor modeling, as discuss [...truncated...]
Author: Dimitrina S. Dimitrova <D.Dimitrova@city.ac.uk>, Emilio S. Guillen <Emilio.Saenz-Guillen@bayes.city.ac.uk>, Vladimir K. Kaishev <V.Kaishev@city.ac.uk>, Andrea Lattuada <andrea.lattuada@hotmail.com> and Richard J. Verrall <R.J.Verrall@city.ac.uk>
Maintainer: Emilio S. Guillen <Emilio.Saenz-Guillen@bayes.city.ac.uk>

Diff between GeDS versions 0.2.0 dated 2024-03-24 and 0.2.1 dated 2024-04-11

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Package gasanalyzer updated to version 0.4.0 with previous version 0.3.4 dated 2024-01-10

Title: Import, Recompute and Analyze Data from Portable Gas Analyzers
Description: Provides functions to import data from several instruments commonly used by plant physiologists to measure characteristics related to photosynthesis. It provides a standardized list of variable names, and several sets of equations to calculate additional variables based on the measurements. These equations have been described by von Caemmerer and Farquhar (1981) <doi:10.1007/BF00384257>, Busch et al. (2020) <doi:10.1038/s41477-020-0606-6> and Márquez et al. (2021) <doi:10.1038/s41477-021-00861-w>. In addition, this package facilitates performing sensitivity analyses on variables or assumptions used in the calculations.
Author: Danny Tholen [aut, cre]
Maintainer: Danny Tholen <thalecress+p@gmail.com>

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New package mapmisc with initial version 2.0.9
Package: mapmisc
Title: Utilities for Producing Maps
Version: 2.0.9
Date: 2024-04-10
Depends: terra, R (>= 3.5.0)
Imports: methods, grDevices, stats, utils, graphics, geosphere
Suggests: RColorBrewer, geonames, classInt, knitr
Enhances: XML, RCurl
Description: Provides a minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections. See Brown (2016) <doi:10.32614/RJ-2016-005>.
License: GPL
LazyData: true
LazyDataCompression: xz
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-04-11 14:23:51 UTC; patrick
Author: Patrick Brown [aut, cre, cph]
Maintainer: Patrick Brown <patrick.brown@utoronto.ca>
Repository: CRAN
Date/Publication: 2024-04-11 20:30:02 UTC

More information about mapmisc at CRAN
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New package AcceptReject with initial version 0.1.0
Package: AcceptReject
Title: Acceptance-Rejection Method for Generating Pseudo-Random Observations
Version: 0.1.0
Description: Provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. The function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) <https://www.jstor.org/stable/4356322>, NEAL, Radford M. (2003) <https://www.jstor.org/stable/3448413> and Bishop, Christopher M. (2006, ISBN: 978-0387310732).
License: GPL (>= 3)
Encoding: UTF-8
URL: https://prdm0.github.io/AcceptReject/
BugReports: https://github.com/prdm0/AcceptReject/issues/
VignetteBuilder: knitr
Imports: cli, ggplot2, glue, lbfgs, numDeriv, pbmcapply, purrr, rlang, scales
Suggests: knitr, rmarkdown, patchwork
NeedsCompilation: no
Packaged: 2024-04-11 13:02:02 UTC; prdm0
Author: Pedro Rafael D. Marinho [aut, cre] , Vera Lucia Damasceno Tomazella [aut]
Maintainer: Pedro Rafael D. Marinho <pedro.rafael.marinho@gmail.com>
Repository: CRAN
Date/Publication: 2024-04-11 20:10:02 UTC

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Package GetTDData updated to version 1.5.5 with previous version 1.5.4 dated 2023-05-15

Title: Get Data for Brazilian Bonds (Tesouro Direto)
Description: Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto <https://www.tesourodireto.com.br/>.
Author: Marcelo Perlin [aut, cre]
Maintainer: Marcelo Perlin <marceloperlin@gmail.com>

Diff between GetTDData versions 1.5.4 dated 2023-05-15 and 1.5.5 dated 2024-04-11

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Package psychotree updated to version 0.16-1 with previous version 0.16-0 dated 2022-05-06

Title: Recursive Partitioning Based on Psychometric Models
Description: Recursive partitioning based on psychometric models, employing the general MOB algorithm (from package partykit) to obtain Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and MPT trees, trees for 1PL, 2PL, 3PL and 4PL models and generalized partial credit models.
Author: Achim Zeileis [aut, cre] , Carolin Strobl [aut] , Florian Wickelmaier [aut], Basil Komboz [aut], Julia Kopf [aut], Lennart Schneider [aut] , David Dreifuss [aut], Rudolf Debelak [aut]
Maintainer: Achim Zeileis <Achim.Zeileis@R-project.org>

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Package gpboost updated to version 1.4.0 with previous version 1.3.1 dated 2024-03-28

Title: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models
Description: An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist (2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the methodology.
Author: Fabio Sigrist [aut, cre], Tim Gyger [aut], Pascal Kuendig [aut], Benoit Jacob [cph], Gael Guennebaud [cph], Nicolas Carre [cph], Pierre Zoppitelli [cph], Gauthier Brun [cph], Jean Ceccato [cph], Jitse Niesen [cph], Other authors of Eigen for the incl [...truncated...]
Maintainer: Fabio Sigrist <fabiosigrist@gmail.com>

Diff between gpboost versions 1.3.1 dated 2024-03-28 and 1.4.0 dated 2024-04-11

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Package traveltimeR updated to version 1.2.0 with previous version 1.1.5 dated 2024-04-10

Title: Interface to 'Travel Time' API
Description: 'Travel Time' API <https://docs.traveltime.com/api/overview/introduction> helps users find locations by journey time rather than using ‘as the crow flies’ distance. Time-based searching gives users more opportunities for personalisation and delivers a more relevant search.
Author: TravelTime [aut, cre]
Maintainer: TravelTime <frontend@traveltime.com>

Diff between traveltimeR versions 1.1.5 dated 2024-04-10 and 1.2.0 dated 2024-04-11

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Package offsetreg updated to version 1.1.0 with previous version 1.0.0 dated 2024-01-23

Title: An Extension of 'Tidymodels' Supporting Offset Terms
Description: Extend the 'tidymodels' ecosystem <https://www.tidymodels.org/> to enable the creation of predictive models with offset terms. Models with offsets are most useful when working with count data or when fitting an adjustment model on top of an existing model with a prior expectation. The former situation is common in insurance where data is often weighted by exposures. The latter is common in life insurance where industry mortality tables are often used as a starting point for setting assumptions.
Author: Matt Heaphy [aut, cre, cph]
Maintainer: Matt Heaphy <mattrmattrs@gmail.com>

Diff between offsetreg versions 1.0.0 dated 2024-01-23 and 1.1.0 dated 2024-04-11

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 offsetreg-1.1.0/offsetreg/man/poisson_reg_offset.Rd     |    2 
 offsetreg-1.1.0/offsetreg/man/reexports.Rd              |    3 -
 offsetreg-1.1.0/offsetreg/man/rpart_exposure.Rd         |only
 offsetreg-1.1.0/offsetreg/man/xgb_train_offset.Rd       |only
 offsetreg-1.1.0/offsetreg/tests/testthat/test-poisson.R |   13 ++++
 offsetreg-1.1.0/offsetreg/tests/testthat/test-rpart.R   |only
 offsetreg-1.1.0/offsetreg/tests/testthat/test-xgboost.R |only
 offsetreg-1.1.0/offsetreg/vignettes                     |only
 32 files changed, 168 insertions(+), 70 deletions(-)

More information about offsetreg at CRAN
Permanent link

Package fastai updated to version 2.2.2 with previous version 2.2.1 dated 2023-03-11

Title: Interface to 'fastai'
Description: The 'fastai' <https://docs.fast.ai/index.html> library simplifies training fast and accurate neural networks using modern best practices. It is based on research in to deep learning best practices undertaken at 'fast.ai', including 'out of the box' support for vision, text, tabular, audio, time series, and collaborative filtering models.
Author: Turgut Abdullayev [ctb, cre, cph, aut]
Maintainer: Turgut Abdullayev <turqut.a.314@gmail.com>

Diff between fastai versions 2.2.1 dated 2023-03-11 and 2.2.2 dated 2024-04-11

 DESCRIPTION                                                                               |    8 
 MD5                                                                                       | 2347 +--
 NAMESPACE                                                                                 | 1926 +--
 NEWS.md                                                                                   |  144 
 R/GAN.R                                                                                   | 1666 +-
 R/Learner_fits.R                                                                          |  596 -
 R/Loss.R                                                                                  |  504 
 R/audio_aug_preprocess.R                                                                  |   94 
 R/audio_aug_signal.R                                                                      |  374 
 R/audio_aug_spectrogram.R                                                                 |  296 
 R/audio_core_config.R                                                                     |  668 -
 R/audio_core_signal.R                                                                     |  336 
 R/audio_core_spectrogram.R                                                                |  206 
 R/audio_download_tar.R                                                                    |  128 
 R/blocks.R                                                                                |  882 -
 R/blurr_hugging_face.R                                                                    | 1730 +-
 R/bs_finder.R                                                                             |  122 
 R/callbacks.R                                                                             |  786 -
 R/callbacks_data.R                                                                        |  402 
 R/callbacks_hook.R                                                                        |  524 
 R/callbacks_mixup_preds_rnn.R                                                             |  232 
 R/callbacks_schedule.R                                                                    |  252 
 R/callbacks_training.R                                                                    |   80 
 R/cnn_learner.R                                                                           |  546 
 R/collab.R                                                                                |  682 -
 R/custom_modules.R                                                                        |  222 
 R/cycleGAN_models.R                                                                       | 1092 -
 R/dataloader.R                                                                            |   96 
 R/download_tar.R                                                                          | 2114 +--
 R/experimental.R                                                                          |  254 
 R/fastaibuilt.R                                                                           |  192 
 R/fastinference.R                                                                         |  456 
 R/freeze.R                                                                                |   84 
 R/generics_image.R                                                                        |   44 
 R/generics_mask.R                                                                         |  746 -
 R/generics_tensor.R                                                                       |  932 -
 R/icevision_albumentations.R                                                              | 5935 ++++------
 R/icevision_dataloaders.R                                                                 |  516 
 R/icevision_dataset.R                                                                     |  128 
 R/icevision_datasplitters.R                                                               |  168 
 R/icevision_efficentdet.R                                                                 |  102 
 R/icevision_faster_rcnn.R                                                                 |  100 
 R/icevision_mask_rcnn.R                                                                   |  116 
 R/icevision_metrics.R                                                                     |   84 
 R/icevision_models.R                                                                      |  338 
 R/icevision_parsers.R                                                                     |  364 
 R/icevision_utils.R                                                                       |  308 
 R/image_loaders.R                                                                         | 1160 -
 R/image_ops.R                                                                             |  548 
 R/import_modules.R                                                                        |  626 -
 R/install_fastai.R                                                                        |  376 
 R/kaggle_wrappers.R                                                                       |  336 
 R/layers.R                                                                                | 1848 +--
 R/losses.R                                                                                |   74 
 R/medical.R                                                                               |  636 -
 R/metric.R                                                                                | 2644 ++--
 R/optimizers.R                                                                            | 1482 +-
 R/package.R                                                                               |  544 
 R/predictions.R                                                                           |   80 
 R/reexports.R                                                                             |  102 
 R/splitters.R                                                                             |   82 
 R/stats.R                                                                                 |  468 
 R/tabular.R                                                                               |  430 
 R/tabular_model.R                                                                         |  256 
 R/tabular_transform.R                                                                     |  436 
 R/test.R                                                                                  | 1160 -
 R/text_core.R                                                                             | 1254 +-
 R/text_data.R                                                                             | 1646 +-
 R/text_learner.R                                                                          |  812 -
 R/text_models.R                                                                           | 1210 +-
 R/time_series_core.R                                                                      |  184 
 R/time_series_data.R                                                                      |  248 
 R/time_series_model.R                                                                     |  336 
 R/time_series_tabular.R                                                                   |  406 
 R/timm_loader.R                                                                           |   70 
 R/unet.R                                                                                  |  514 
 R/vision_augment.R                                                                        | 2042 +--
 R/vision_core.R                                                                           | 1478 +-
 R/vision_data.R                                                                           |  458 
 R/vision_learner.R                                                                        |  198 
 R/vision_models.R                                                                         | 1892 +--
 README.md                                                                                 | 3806 +++---
 build/vignette.rds                                                                        |binary
 inst/doc/Basic_Tabular.R                                                                  |  114 
 inst/doc/Basic_Tabular.Rmd                                                                |  426 
 inst/doc/Basic_Tabular.html                                                               | 1032 -
 inst/doc/audio.R                                                                          |  146 
 inst/doc/audio.Rmd                                                                        |  340 
 inst/doc/audio.html                                                                       |  932 -
 inst/doc/audio2tf.R                                                                       |  100 
 inst/doc/audio2tf.Rmd                                                                     |  270 
 inst/doc/audio2tf.html                                                                    |  874 -
 inst/doc/basic_img_class.R                                                                |  100 
 inst/doc/basic_img_class.Rmd                                                              |  384 
 inst/doc/basic_img_class.html                                                             | 1002 -
 inst/doc/bayes_opt.R                                                                      |  130 
 inst/doc/bayes_opt.Rmd                                                                    |  176 
 inst/doc/bayes_opt.html                                                                   |  868 -
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 inst/doc/callbacks.html                                                                   |  894 -
 inst/doc/catalyst.R                                                                       |   46 
 inst/doc/catalyst.Rmd                                                                     |  140 
 inst/doc/catalyst.html                                                                    |  800 -
 inst/doc/custom_img.R                                                                     |  172 
 inst/doc/custom_img.Rmd                                                                   |  332 
 inst/doc/custom_img.html                                                                  |  948 -
 inst/doc/data_aug.R                                                                       |  200 
 inst/doc/data_aug.Rmd                                                                     |  462 
 inst/doc/data_aug.html                                                                    | 1048 -
 inst/doc/gpt.R                                                                            |  148 
 inst/doc/gpt.Rmd                                                                          |  284 
 inst/doc/gpt.html                                                                         |  948 -
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 inst/doc/low.Rmd                                                                          |  688 -
 inst/doc/low.html                                                                         | 1240 +-
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 inst/doc/question_answering.html                                                          | 1236 +-
 inst/doc/super_res_gan.R                                                                  |  122 
 inst/doc/super_res_gan.Rmd                                                                |  250 
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 inst/doc/textclassification.R                                                             |  106 
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 inst/doc/textsummarize.R                                                                  |  166 
 inst/doc/textsummarize.Rmd                                                                |  574 
 inst/doc/textsummarize.html                                                               | 1202 +-
 inst/doc/time_series.R                                                                    |  104 
 inst/doc/time_series.Rmd                                                                  |  578 
 inst/doc/time_series.html                                                                 | 1198 +-
 inst/python/fastaibuilt/Module_test.py                                                    |   20 
 inst/python/fastaibuilt/Transformer.py                                                    |   42 
 inst/python/fastaibuilt/bs_finder.py                                                      |  320 
 inst/python/fastaibuilt/crappify.py                                                       |   46 
 inst/python/fastaibuilt/custom_loss.py                                                    |   22 
 inst/python/fastaibuilt/migrating_catalyst.py                                             |   76 
 inst/python/fastaibuilt/migrating_ignite.py                                               |  186 
 inst/python/fastaibuilt/migrating_lightning.py                                            |   88 
 inst/python/fastaibuilt/migrating_pytorch.py                                              |  136 
 inst/python/fastaibuilt/pretrained_timm_models.py                                         |  164 
 inst/python/fastaibuilt/retinanet/inference.py                                            |  132 
 inst/python/fastaibuilt/train_imagenette.py                                               |  172 
 inst/python/fastaibuilt/train_imdbclassifier.py                                           |  108 
 inst/python/fastaibuilt/train_tabular.py                                                  |  100 
 man/APScoreBinary.Rd                                                                      |   56 
 man/APScoreMulti.Rd                                                                       |   56 
 man/AWD_LSTM.Rd                                                                           |   92 
 man/AWD_QRNN.Rd                                                                           |   92 
 man/AccumMetric.Rd                                                                        |   80 
 man/Adam.Rd                                                                               |   34 
 man/AdaptiveAvgPool.Rd                                                                    |   32 
 man/AdaptiveConcatPool1d.Rd                                                               |   34 
 man/AdaptiveConcatPool2d.Rd                                                               |   34 
 man/AdaptiveGANSwitcher.Rd                                                                |   38 
 man/AdaptiveLoss.Rd                                                                       |   34 
 man/AddChannels.Rd                                                                        |   28 
 man/AddNoise.Rd                                                                           |   38 
 man/AffineCoordTfm.Rd                                                                     |   74 
 man/AudioBlock.Rd                                                                         |   56 
 man/AudioBlock_from_folder.Rd                                                             |   56 
 man/AudioGetter.Rd                                                                        |   48 
 man/AudioPadType.Rd                                                                       |   28 
 man/AudioSpectrogram.Rd                                                                   |   28 
 man/AudioTensor.Rd                                                                        |   38 
 man/AudioTensor_create.Rd                                                                 |   68 
 man/AudioToMFCC.Rd                                                                        |   68 
 man/AudioToMFCC_from_cfg.Rd                                                               |   34 
 man/AudioToSpec_from_cfg.Rd                                                               |   34 
 man/AutoConfig.Rd                                                                         |   28 
 man/AvgLoss.Rd                                                                            |   34 
 man/AvgPool.Rd                                                                            |   50 
 man/AvgSmoothLoss.Rd                                                                      |   34 
 man/BBoxBlock.Rd                                                                          |   28 
 man/BBoxLabeler.Rd                                                                        |   46 
 man/BBoxLblBlock.Rd                                                                       |  116 
 man/BCELossFlat.Rd                                                                        |   34 
 man/BCEWithLogitsLossFlat.Rd                                                              |   34 
 man/BalancedAccuracy.Rd                                                                   |   42 
 man/BaseLoss.Rd                                                                           |   34 
 man/BaseTokenizer.Rd                                                                      |   34 
 man/BasicMFCC.Rd                                                                          |   68 
 man/BasicMelSpectrogram.Rd                                                                |  116 
 man/BasicSpectrogram.Rd                                                                   |   92 
 man/BatchNorm.Rd                                                                          |   74 
 man/BatchNorm1dFlat.Rd                                                                    |   62 
 man/BrierScore.Rd                                                                         |   42 
 man/BrierScoreMulti.Rd                                                                    |   56 
 man/COCOMetric.Rd                                                                         |   56 
 man/COCOMetricType.Rd                                                                     |   28 
 man/CSVLogger.Rd                                                                          |   80 
 man/Callback.Rd                                                                           |   28 
 man/Cat.Rd                                                                                |   38 
 man/Categorify.Rd                                                                         |   38 
 man/CategoryBlock.Rd                                                                      |   42 
 man/ChangeVolume.Rd                                                                       |   42 
 man/ClassificationInterpretation_from_learner.Rd                                          |   56 
 man/CohenKappa.Rd                                                                         |   46 
 man/ColReader.Rd                                                                          |   46 
 man/ColSplitter.Rd                                                                        |   34 
 man/CollabDataLoaders_from_dblock.Rd                                                      |   74 
 man/CollabDataLoaders_from_df.Rd                                                          |  132 
 man/CollectDataCallback.Rd                                                                |   42 
 man/Contrast.Rd                                                                           |   46 
 man/ConvLayer.Rd                                                                          |  134 
 man/CorpusBLEUMetric.Rd                                                                   |   38 
 man/CropPad.Rd                                                                            |   42 
 man/CropTime.Rd                                                                           |   38 
 man/CrossEntropyLossFlat.Rd                                                               |   34 
 man/CudaCallback.Rd                                                                       |   34 
 man/CutMix.Rd                                                                             |   34 
 man/CycleGAN.Rd                                                                           |   88 
 man/CycleGANLoss.Rd                                                                       |   74 
 man/CycleGANTrainer.Rd                                                                    |   34 
 man/DataBlock.Rd                                                                          |   76 
 man/Data_Loaders.Rd                                                                       |   62 
 man/Datasets.Rd                                                                           |  104 
 man/Debugger.Rd                                                                           |   34 
 man/Delta.Rd                                                                              |   36 
 man/DenseResBlock.Rd                                                                      |  128 
 man/DeterministicDihedral.Rd                                                              |   56 
 man/DeterministicDraw.Rd                                                                  |   34 
 man/DeterministicFlip.Rd                                                                  |   62 
 man/Dice.Rd                                                                               |   34 
 man/Dicom.Rd                                                                              |   28 
 man/Dihedral.Rd                                                                           |  102 
 man/DihedralItem.Rd                                                                       |   42 
 man/DownmixMono.Rd                                                                        |   46 
 man/DynamicUnet.Rd                                                                        |  104 
 man/EarlyStoppingCallback.Rd                                                              |   34 
 man/Embedding.Rd                                                                          |   38 
 man/EmbeddingDropout.Rd                                                                   |   38 
 man/ExplainedVariance.Rd                                                                  |   34 
 man/F1Score.Rd                                                                            |   62 
 man/F1ScoreMulti.Rd                                                                       |   68 
 man/FBeta.Rd                                                                              |   68 
 man/FBetaMulti.Rd                                                                         |   74 
 man/FetchPredsCallback.Rd                                                                 |   68 
 man/FileSplitter.Rd                                                                       |   34 
 man/FillMissing.Rd                                                                        |   80 
 man/FillStrategy_COMMON.Rd                                                                |   28 
 man/FillStrategy_CONSTANT.Rd                                                              |   28 
 man/FillStrategy_MEDIAN.Rd                                                                |   28 
 man/FixedGANSwitcher.Rd                                                                   |   38 
 man/Flatten.Rd                                                                            |   28 
 man/Flip.Rd                                                                               |   74 
 man/FlipItem.Rd                                                                           |   34 
 man/FolderDataset.Rd                                                                      |   44 
 man/ForgetMultGPU.Rd                                                                      |   34 
 man/FuncSplitter.Rd                                                                       |   34 
 man/GANDiscriminativeLR.Rd                                                                |   28 
 man/GANLearner_from_learners.Rd                                                           |  152 
 man/GANLearner_wgan.Rd                                                                    |  162 
 man/GANLoss.Rd                                                                            |   42 
 man/GANModule.Rd                                                                          |   42 
 man/GANTrainer.Rd                                                                         |   62 
 man/GatherPredsCallback.Rd                                                                |   62 
 man/GradientAccumulation.Rd                                                               |   34 
 man/GrandparentSplitter.Rd                                                                |   38 
 man/HF_ARCHITECTURES.Rd                                                                   |   28 
 man/HF_BaseInput.Rd                                                                       |   42 
 man/HF_BaseModelCallback.Rd                                                               |   34 
 man/HF_BaseModelWrapper.Rd                                                                |   56 
 man/HF_BeforeBatchTransform.Rd                                                            |   82 
 man/HF_CausalLMBeforeBatchTransform.Rd                                                    |   94 
 man/HF_QABatchTransform.Rd                                                                |   94 
 man/HF_QABeforeBatchTransform.Rd                                                          |   88 
 man/HF_QstAndAnsModelCallback.Rd                                                          |   34 
 man/HF_QuestionAnswerInput.Rd                                                             |   34 
 man/HF_SummarizationBeforeBatchTransform.Rd                                               |   88 
 man/HF_SummarizationInput.Rd                                                              |   28 
 man/HF_SummarizationModelCallback.Rd                                                      |   50 
 man/HF_TASKS_ALL.Rd                                                                       |   28 
 man/HF_TASKS_AUTO.Rd                                                                      |   28 
 man/HF_Text2TextAfterBatchTransform.Rd                                                    |   44 
 man/HF_Text2TextBlock.Rd                                                                  |   34 
 man/HF_TextBlock.Rd                                                                       |   34 
 man/HF_TokenCategorize.Rd                                                                 |   42 
 man/HF_TokenCategoryBlock.Rd                                                              |   50 
 man/HF_TokenClassBeforeBatchTransform.Rd                                                  |   94 
 man/HF_TokenClassInput.Rd                                                                 |   28 
 man/HF_TokenTensorCategory.Rd                                                             |   28 
 man/HF_load_dataset.Rd                                                                    |  140 
 man/HammingLoss.Rd                                                                        |   50 
 man/HammingLossMulti.Rd                                                                   |   56 
 man/Hook.Rd                                                                               |   78 
 man/HookCallback.Rd                                                                       |   94 
 man/Hooks.Rd                                                                              |   50 
 man/Hue.Rd                                                                                |   46 
 man/IDMap.Rd                                                                              |   34 
 man/Image.Rd                                                                              |   34 
 man/ImageBW_create.Rd                                                                     |   34 
 man/ImageBlock.Rd                                                                         |   34 
 man/ImageDataLoaders_from_csv.Rd                                                          |  158 
 man/ImageDataLoaders_from_dblock.Rd                                                       |   80 
 man/ImageDataLoaders_from_df.Rd                                                           |  140 
 man/ImageDataLoaders_from_folder.Rd                                                       |  110 
 man/ImageDataLoaders_from_lists.Rd                                                        |  110 
 man/ImageDataLoaders_from_name_re.Rd                                                      |  132 
 man/ImageDataLoaders_from_path_func.Rd                                                    |  104 
 man/ImageDataLoaders_from_path_re.Rd                                                      |  104 
 man/Image_create.Rd                                                                       |   34 
 man/Image_open.Rd                                                                         |   38 
 man/Image_resize.Rd                                                                       |   50 
 man/InceptionModule.Rd                                                                    |   62 
 man/IndexSplitter.Rd                                                                      |   34 
 man/InstanceNorm.Rd                                                                       |   74 
 man/IntToFloatTensor.Rd                                                                   |   38 
 man/InvisibleTensor.Rd                                                                    |   34 
 man/Jaccard.Rd                                                                            |   62 
 man/JaccardCoeff.Rd                                                                       |   34 
 man/JaccardMulti.Rd                                                                       |   68 
 man/L.Rd                                                                                  |   28 
 man/L1LossFlat.Rd                                                                         |   34 
 man/LMDataLoader.Rd                                                                       |  128 
 man/LMLearner.Rd                                                                          |  174 
 man/LMLearner_predict.Rd                                                                  |   80 
 man/LabelSmoothingCrossEntropy.Rd                                                         |   38 
 man/LabelSmoothingCrossEntropyFlat.Rd                                                     |   34 
 man/LabeledBBox.Rd                                                                        |   34 
 man/Lamb.Rd                                                                               |   34 
 man/Lambda.Rd                                                                             |   34 
 man/Larc.Rd                                                                               |   34 
 man/Learner.Rd                                                                            |   60 
 man/LightingTfm.Rd                                                                        |   38 
 man/LinBnDrop.Rd                                                                          |   54 
 man/LinearDecoder.Rd                                                                      |   50 
 man/LitModel.Rd                                                                           |   28 
 man/Lookahead.Rd                                                                          |   34 
 man/LossMetric.Rd                                                                         |   38 
 man/MCDropoutCallback.Rd                                                                  |   38 
 man/MSELossFlat.Rd                                                                        |   34 
 man/MaskBlock.Rd                                                                          |   34 
 man/MaskFreq.Rd                                                                           |   46 
 man/MaskTime.Rd                                                                           |   46 
 man/Mask_create.Rd                                                                        |   46 
 man/MatthewsCorrCoef.Rd                                                                   |   34 
 man/MatthewsCorrCoefMulti.Rd                                                              |   42 
 man/MaxPool.Rd                                                                            |   50 
 man/MergeLayer.Rd                                                                         |   34 
 man/MishJitAutoFn.Rd                                                                      |   34 
 man/Mish_.Rd                                                                              |   34 
 man/MixHandler.Rd                                                                         |   34 
 man/MixUp.Rd                                                                              |   34 
 man/ModelResetter.Rd                                                                      |   34 
 man/Module.Rd                                                                             |   28 
 man/Module_test.Rd                                                                        |   28 
 man/MultiCategorize.Rd                                                                    |   38 
 man/MultiCategoryBlock.Rd                                                                 |   42 
 man/MultiTargetLoss.Rd                                                                    |   34 
 man/Net.Rd                                                                                |   46 
 man/NoiseColor.Rd                                                                         |   28 
 man/NoneReduce.Rd                                                                         |   34 
 man/Normalize.Rd                                                                          |   38 
 man/NormalizeTS.Rd                                                                        |   46 
 man/Normalize_from_stats.Rd                                                               |   50 
 man/Numericalize.Rd                                                                       |   62 
 man/OldRandomCrop.Rd                                                                      |   42 
 man/OpenAudio.Rd                                                                          |   34 
 man/OptimWrapper.Rd                                                                       |   34 
 man/Optimizer.Rd                                                                          |   34 
 man/ParamScheduler.Rd                                                                     |   34 
 man/PartialDL.Rd                                                                          |  122 
 man/PartialLambda.Rd                                                                      |   34 
 man/PearsonCorrCoef.Rd                                                                    |   68 
 man/Perplexity.Rd                                                                         |   34 
 man/Pipeline.Rd                                                                           |   38 
 man/PixelShuffle_ICNR.Rd                                                                  |   68 
 man/PointBlock.Rd                                                                         |   28 
 man/PointScaler.Rd                                                                        |   38 
 man/PoolFlatten.Rd                                                                        |   34 
 man/PooledSelfAttention2d.Rd                                                              |   34 
 man/PoolingLinearClassifier.Rd                                                            |   46 
 man/Precision.Rd                                                                          |   62 
 man/PrecisionMulti.Rd                                                                     |   68 
 man/PreprocessAudio.Rd                                                                    |   48 
 man/QHAdam.Rd                                                                             |   34 
 man/QRNN.Rd                                                                               |   92 
 man/QRNNLayer.Rd                                                                          |   80 
 man/R2Score.Rd                                                                            |   34 
 man/RAdam.Rd                                                                              |   34 
 man/RMSProp.Rd                                                                            |   34 
 man/RNNDropout.Rd                                                                         |   34 
 man/RNNRegularizer.Rd                                                                     |   38 
 man/RandPair.Rd                                                                           |   34 
 man/RandTransform.Rd                                                                      |   46 
 man/RandomCrop.Rd                                                                         |   38 
 man/RandomErasing.Rd                                                                      |   50 
 man/RandomResizedCrop.Rd                                                                  |   62 
 man/RandomResizedCropGPU.Rd                                                               |   62 
 man/RandomSplitter.Rd                                                                     |   38 
 man/RatioResize.Rd                                                                        |   42 
 man/ReadTSBatch.Rd                                                                        |   34 
 man/Recall.Rd                                                                             |   62 
 man/RecallMulti.Rd                                                                        |   68 
 man/ReduceLROnPlateau.Rd                                                                  |   76 
 man/RegressionBlock.Rd                                                                    |   34 
 man/RemoveSilence.Rd                                                                      |   50 
 man/RemoveType.Rd                                                                         |   28 
 man/ResBlock.Rd                                                                           |  194 
 man/ResNet.Rd                                                                             |   74 
 man/Resample.Rd                                                                           |   34 
 man/Resize.Rd                                                                             |   46 
 man/ResizeBatch.Rd                                                                        |   34 
 man/ResizeSignal.Rd                                                                       |   38 
 man/ResnetBlock.Rd                                                                        |   62 
 man/RetinaNet.Rd                                                                          |   54 
 man/RetinaNetFocalLoss.Rd                                                                 |   44 
 man/RocAuc.Rd                                                                             |   64 
 man/RocAucBinary.Rd                                                                       |   88 
 man/RocAucMulti.Rd                                                                        |   56 
 man/Rotate.Rd                                                                             |   80 
 man/SEBlock.Rd                                                                            |   54 
 man/SEModule.Rd                                                                           |   42 
 man/SEResNeXtBlock.Rd                                                                     |   74 
 man/SGD.Rd                                                                                |   34 
 man/SGRoll.Rd                                                                             |   38 
 man/Saturation.Rd                                                                         |   46 
 man/SaveModelCallback.Rd                                                                  |   34 
 man/SchedCos.Rd                                                                           |   38 
 man/SchedExp.Rd                                                                           |   38 
 man/SchedLin.Rd                                                                           |   38 
 man/SchedNo.Rd                                                                            |   38 
 man/SchedPoly.Rd                                                                          |   42 
 man/SegmentationDataLoaders_from_label_func.Rd                                            |  104 
 man/SelfAttention.Rd                                                                      |   34 
 man/SentenceEncoder.Rd                                                                    |   46 
 man/SentencePieceTokenizer.Rd                                                             |   80 
 man/SeparableBlock.Rd                                                                     |   54 
 man/SequentialEx.Rd                                                                       |   34 
 man/SequentialRNN.Rd                                                                      |   34 
 man/ShapInterpretation.Rd                                                                 |   80 
 man/ShortEpochCallback.Rd                                                                 |   38 
 man/Shortcut.Rd                                                                           |   42 
 man/ShowCycleGANImgsCallback.Rd                                                           |   42 
 man/ShowGraphCallback.Rd                                                                  |   34 
 man/SigmoidRange.Rd                                                                       |   38 
 man/SignalCutout.Rd                                                                       |   38 
 man/SignalLoss.Rd                                                                         |   38 
 man/SignalShifter.Rd                                                                      |   68 
 man/SimpleCNN.Rd                                                                          |   46 
 man/SimpleSelfAttention.Rd                                                                |   42 
 man/SortedDL.Rd                                                                           |  122 
 man/SpacyTokenizer.Rd                                                                     |   42 
 man/SpearmanCorrCoef.Rd                                                                   |   80 
 man/SpectrogramTransformer.Rd                                                             |   44 
 man/SqueezeNet.Rd                                                                         |   48 
 man/Swish_.Rd                                                                             |   34 
 man/TSBlock.Rd                                                                            |   34 
 man/TSDataLoaders_from_dfs.Rd                                                             |  104 
 man/TSDataTable.Rd                                                                        |   86 
 man/TSeries.Rd                                                                            |   34 
 man/TSeries_create.Rd                                                                     |   60 
 man/TabularDataTable.Rd                                                                   |  104 
 man/TabularModel.Rd                                                                       |   98 
 man/TabularTS.Rd                                                                          |   86 
 man/TabularTSDataloader.Rd                                                                |  116 
 man/TensorBBox.Rd                                                                         |   34 
 man/TensorBBox_create.Rd                                                                  |   38 
 man/TensorImage.Rd                                                                        |   34 
 man/TensorImageBW.Rd                                                                      |   34 
 man/TensorMultiCategory.Rd                                                                |   34 
 man/TensorPoint.Rd                                                                        |   34 
 man/TensorPoint_create.Rd                                                                 |   34 
 man/TerminateOnNaNCallback.Rd                                                             |   34 
 man/TextBlock.Rd                                                                          |   86 
 man/TextBlock_from_df.Rd                                                                  |  110 
 man/TextBlock_from_folder.Rd                                                              |  128 
 man/TextDataLoaders_from_csv.Rd                                                           |  152 
 man/TextDataLoaders_from_df.Rd                                                            |  142 
 man/TextDataLoaders_from_folder.Rd                                                        |  122 
 man/TextLearner.Rd                                                                        |  128 
 man/TextLearner_load_encoder.Rd                                                           |   38 
 man/TextLearner_load_pretrained.Rd                                                        |   42 
 man/TextLearner_save_encoder.Rd                                                           |   34 
 man/TfmResize.Rd                                                                          |   38 
 man/TfmdDL.Rd                                                                             |  122 
 man/TfmdLists.Rd                                                                          |   28 
 man/ToTensor.Rd                                                                           |   46 
 man/TokenizeWithRules.Rd                                                                  |   42 
 man/Tokenizer.Rd                                                                          |   68 
 man/Tokenizer_from_df.Rd                                                                  |   74 
 man/TrackerCallback.Rd                                                                    |   42 
 man/TrainEvalCallback.Rd                                                                  |   34 
 man/Transform.Rd                                                                          |   46 
 man/TransformBlock.Rd                                                                     |   62 
 man/TransformersDropOutput.Rd                                                             |   28 
 man/TransformersTokenizer.Rd                                                              |   34 
 man/URLs_ADULT_SAMPLE.Rd                                                                  |   54 
 man/URLs_AG_NEWS.Rd                                                                       |   54 
 man/URLs_AMAZON_REVIEWSAMAZON_REVIEWS.Rd                                                  |   44 
 man/URLs_AMAZON_REVIEWS_POLARITY.Rd                                                       |   44 
 man/URLs_BIWI_HEAD_POSE.Rd                                                                |   38 
 man/URLs_CALTECH_101.Rd                                                                   |   38 
 man/URLs_CAMVID.Rd                                                                        |   38 
 man/URLs_CAMVID_TINY.Rd                                                                   |   38 
 man/URLs_CARS.Rd                                                                          |   38 
 man/URLs_CIFAR.Rd                                                                         |   38 
 man/URLs_CIFAR_100.Rd                                                                     |   38 
 man/URLs_COCO_TINY.Rd                                                                     |   38 
 man/URLs_CUB_200_2011.Rd                                                                  |   38 
 man/URLs_DBPEDIA.Rd                                                                       |   38 
 man/URLs_DOGS.Rd                                                                          |   38 
 man/URLs_FLOWERS.Rd                                                                       |   38 
 man/URLs_FOOD.Rd                                                                          |   38 
 man/URLs_HORSE_2_ZEBRA.Rd                                                                 |   38 
 man/URLs_HUMAN_NUMBERS.Rd                                                                 |   38 
 man/URLs_IMAGENETTE.Rd                                                                    |   38 
 man/URLs_IMAGENETTE_160.Rd                                                                |   38 
 man/URLs_IMAGENETTE_320.Rd                                                                |   38 
 man/URLs_IMAGEWOOF.Rd                                                                     |   38 
 man/URLs_IMAGEWOOF_160.Rd                                                                 |   38 
 man/URLs_IMAGEWOOF_320.Rd                                                                 |   38 
 man/URLs_IMDB.Rd                                                                          |   38 
 man/URLs_IMDB_SAMPLE.Rd                                                                   |   38 
 man/URLs_LSUN_BEDROOMS.Rd                                                                 |   38 
 man/URLs_ML_SAMPLE.Rd                                                                     |   38 
 man/URLs_MNIST.Rd                                                                         |   38 
 man/URLs_MNIST_SAMPLE.Rd                                                                  |   38 
 man/URLs_MNIST_TINY.Rd                                                                    |   38 
 man/URLs_MNIST_VAR_SIZE_TINY.Rd                                                           |   38 
 man/URLs_MOVIE_LENS_ML_100k.Rd                                                            |   38 
 man/URLs_MT_ENG_FRA.Rd                                                                    |   38 
 man/URLs_OPENAI_TRANSFORMER.Rd                                                            |   38 
 man/URLs_PASCAL_2007.Rd                                                                   |   38 
 man/URLs_PASCAL_2012.Rd                                                                   |   38 
 man/URLs_PETS.Rd                                                                          |   38 
 man/URLs_PLANET_SAMPLE.Rd                                                                 |   38 
 man/URLs_PLANET_TINY.Rd                                                                   |   38 
 man/URLs_S3_COCO.Rd                                                                       |   38 
 man/URLs_S3_IMAGE.Rd                                                                      |   38 
 man/URLs_S3_IMAGELOC.Rd                                                                   |   38 
 man/URLs_S3_MODEL.Rd                                                                      |   38 
 man/URLs_S3_NLP.Rd                                                                        |   38 
 man/URLs_SIIM_SMALL.Rd                                                                    |   38 
 man/URLs_SKIN_LESION.Rd                                                                   |   38 
 man/URLs_SOGOU_NEWS.Rd                                                                    |   38 
 man/URLs_SPEAKERS10.Rd                                                                    |   54 
 man/URLs_SPEECHCOMMANDS.Rd                                                                |   54 
 man/URLs_WIKITEXT.Rd                                                                      |   38 
 man/URLs_WIKITEXT_TINY.Rd                                                                 |   38 
 man/URLs_WT103_BWD.Rd                                                                     |   38 
 man/URLs_WT103_FWD.Rd                                                                     |   38 
 man/URLs_YAHOO_ANSWERS.Rd                                                                 |   38 
 man/URLs_YELP_REVIEWS.Rd                                                                  |   38 
 man/URLs_YELP_REVIEWS_POLARITY.Rd                                                         |   38 
 man/UnetBlock.Rd                                                                          |  158 
 man/Voice.Rd                                                                              |  116 
 man/WandbCallback.Rd                                                                      |   94 
 man/Warp.Rd                                                                               |   86 
 man/WeightDropout.Rd                                                                      |   42 
 man/WeightedDL.Rd                                                                         |  122 
 man/XResNet.Rd                                                                            |   48 
 man/Zoom_.Rd                                                                              |   98 
 man/abs.Rd                                                                                |   36 
 man/abs.fastai.torch_core.TensorMask.Rd                                                   |   34 
 man/accuracy.Rd                                                                           |   42 
 man/accuracy_multi.Rd                                                                     |   46 
 man/accuracy_thresh_expand.Rd                                                             |   46 
 man/adam_step.Rd                                                                          |   66 
 man/adaptive_pool.Rd                                                                      |   34 
 man/add.Rd                                                                                |   58 
 man/add_cyclic_datepart.Rd                                                                |   68 
 man/add_datepart.Rd                                                                       |   50 
 man/affine_coord.Rd                                                                       |   80 
 man/affine_mat.Rd                                                                         |   34 
 man/alexnet.Rd                                                                            |   62 
 man/and-.fastai.torch_core.TensorMask.Rd                                                  |   38 
 man/apply_perspective.Rd                                                                  |   38 
 man/as_array.Rd                                                                           |   34 
 man/aspect.Rd                                                                             |   34 
 man/audio_extensions.Rd                                                                   |   28 
 man/aug_transforms.Rd                                                                     |  178 
 man/average_grad.Rd                                                                       |   50 
 man/average_sqr_grad.Rd                                                                   |   50 
 man/awd_lstm_clas_split.Rd                                                                |   34 
 man/awd_lstm_lm_split.Rd                                                                  |   34 
 man/basic_critic.Rd                                                                       |   60 
 man/basic_generator.Rd                                                                    |   58 
 man/bb_pad.Rd                                                                             |   38 
 man/blurr.Rd                                                                              |   40 
 man/bs_find.Rd                                                                            |   70 
 man/bs_finder.Rd                                                                          |   28 
 man/bt.Rd                                                                                 |   28 
 man/calculate_rouge.Rd                                                                    |   56 
 man/catalyst.Rd                                                                           |   28 
 man/catalyst_model.Rd                                                                     |   28 
 man/ceiling.fastai.torch_core.TensorMask.Rd                                               |   34 
 man/ceiling_.Rd                                                                           |   36 
 man/children_and_parameters.Rd                                                            |   34 
 man/clean_raw_keys.Rd                                                                     |   34 
 man/clip_remove_empty.Rd                                                                  |   38 
 man/cm.Rd                                                                                 |   28 
 man/cnn_config.Rd                                                                         |   98 
 man/cnn_learner.Rd                                                                        |  190 
 man/collab.Rd                                                                             |   28 
 man/collab_learner.Rd                                                                     |  186 
 man/colors.Rd                                                                             |   28 
 man/combined_flat_anneal.Rd                                                               |   44 
 man/competition_download_file.Rd                                                          |  104 
 man/competition_download_files.Rd                                                         |   62 
 man/competition_leaderboard_download.Rd                                                   |   42 
 man/competition_list_files.Rd                                                             |   54 
 man/competition_submit.Rd                                                                 |   46 
 man/competitions_list.Rd                                                                  |   62 
 man/convT_norm_relu.Rd                                                                    |   54 
 man/conv_norm_lr.Rd                                                                       |   98 
 man/cos.fastai.torch_core.TensorMask.Rd                                                   |   34 
 man/cos_.Rd                                                                               |   36 
 man/cosh.fastai.torch_core.TensorMask.Rd                                                  |   34 
 man/cosh_.Rd                                                                              |   36 
 man/crap.Rd                                                                               |   28 
 man/crappifier.Rd                                                                         |   56 
 man/create_body.Rd                                                                        |   52 
 man/create_cnn_model.Rd                                                                   |  110 
 man/create_fcn.Rd                                                                         |   50 
 man/create_head.Rd                                                                        |   80 
 man/create_inception.Rd                                                                   |   74 
 man/create_mlp.Rd                                                                         |   44 
 man/create_resnet.Rd                                                                      |   62 
 man/create_unet_model.Rd                                                                  |  122 
 man/custom_loss.Rd                                                                        |   28 
 man/cutout_gaussian.Rd                                                                    |   38 
 man/cycle_learner.Rd                                                                      |   90 
 man/dataloaders.Rd                                                                        |   52 
 man/dcmread.Rd                                                                            |   58 
 man/debias.Rd                                                                             |   42 
 man/decision_plot.Rd                                                                      |   56 
 man/decode_spec_tokens.Rd                                                                 |   34 
 man/default_split.Rd                                                                      |   34 
 man/denormalize_imagenet.Rd                                                               |   34 
 man/densenet121.Rd                                                                        |   44 
 man/densenet161.Rd                                                                        |   44 
 man/densenet169.Rd                                                                        |   44 
 man/densenet201.Rd                                                                        |   44 
 man/dependence_plot.Rd                                                                    |   58 
 man/detuplify_pg.Rd                                                                       |   34 
 man/dicom_windows.Rd                                                                      |   28 
 man/dihedral_mat.Rd                                                                       |   46 
 man/dim.Rd                                                                                |   36 
 man/dim.fastai.torch_core.TensorMask.Rd                                                   |   34 
 man/discriminator.Rd                                                                      |   56 
 man/div.Rd                                                                                |   40 
 man/dropout_mask.Rd                                                                       |   42 
 man/dummy_eval.Rd                                                                         |   38 
 man/efficientdet_infer_dl.Rd                                                              |   42 
 man/efficientdet_learner.Rd                                                               |   48 
 man/efficientdet_model.Rd                                                                 |   46 
 man/efficientdet_predict_dl.Rd                                                            |   42 
 man/efficientdet_train_dl.Rd                                                              |   42 
 man/efficientdet_valid_dl.Rd                                                              |   42 
 man/emb_sz_rule.Rd                                                                        |   34 
 man/error_rate.Rd                                                                         |   64 
 man/exp.Rd                                                                                |   36 
 man/exp.fastai.torch_core.TensorMask.Rd                                                   |   34 
 man/exp_rmspe.Rd                                                                          |   38 
 man/expm1.Rd                                                                              |   36 
 man/expm1.fastai.torch_core.TensorMask.Rd                                                 |   34 
 man/export_generator.Rd                                                                   |   56 
 man/fView.Rd                                                                              |   34 
 man/fa_collate.Rd                                                                         |   34 
 man/fa_convert.Rd                                                                         |   34 
 man/fastai_version.Rd                                                                     |   28 
 man/fastaudio.Rd                                                                          |   28 
 man/faster_rcnn_infer_dl.Rd                                                               |   42 
 man/faster_rcnn_learner.Rd                                                                |   48 
 man/faster_rcnn_model.Rd                                                                  |   56 
 man/faster_rcnn_predict_dl.Rd                                                             |   42 
 man/faster_rcnn_train_dl.Rd                                                               |   42 
 man/faster_rcnn_valid_dl.Rd                                                               |   42 
 man/fastinf.Rd                                                                            |   28 
 man/find_coeffs.Rd                                                                        |   38 
 man/fine_tune.Rd                                                                          |   82 
 man/fit.fastai.learner.Learner.Rd                                                         |   40 
 man/fit.fastai.tabular.learner.TabularLearner.Rd                                          |   38 
 man/fit.fastai.vision.gan.GANLearner.Rd                                                   |   54 
 man/fit_flat_cos.Rd                                                                       |   80 
 man/fit_flat_lin.Rd                                                                       |   90 
 man/fit_one_cycle.Rd                                                                      |   38 
 man/fit_sgdr.Rd                                                                           |   80 
 man/fix_fit.Rd                                                                            |   34 
 man/fix_html.Rd                                                                           |   34 
 man/flatten_check.Rd                                                                      |   38 
 man/flatten_model.Rd                                                                      |   34 
 man/flip_mat.Rd                                                                           |   46 
 man/float.Rd                                                                              |   34 
 man/floor.fastai.torch_core.TensorMask.Rd                                                 |   34 
 man/floor_.Rd                                                                             |   36 
 man/floor_div.Rd                                                                          |   40 
 man/floor_mod.Rd                                                                          |   40 
 man/fmodule.Rd                                                                            |   40 
 man/force_plot.Rd                                                                         |   46 
 man/foreground_acc.Rd                                                                     |   46 
 man/forget_mult_CPU.Rd                                                                    |   50 
 man/freeze.Rd                                                                             |   48 
 man/gan_critic.Rd                                                                         |   46 
 man/gan_loss_from_func.Rd                                                                 |   42 
 man/gauss_blur2d.Rd                                                                       |   38 
 man/generate_noise.Rd                                                                     |   54 
 man/get_annotations.Rd                                                                    |   38 
 man/get_audio_files.Rd                                                                    |   42 
 man/get_bias.Rd                                                                           |   64 
 man/get_c.Rd                                                                              |   50 
 man/get_confusion_matrix.Rd                                                               |   52 
 man/get_data_loaders.Rd                                                                   |   38 
 man/get_dcm_matrix.Rd                                                                     |   70 
 man/get_dicom_files.Rd                                                                    |   62 
 man/get_dls.Rd                                                                            |   88 
 man/get_emb_sz.Rd                                                                         |   38 
 man/get_files.Rd                                                                          |   62 
 man/get_grid.Rd                                                                           |   86 
 man/get_hf_objects.Rd                                                                     |   44 
 man/get_image_files.Rd                                                                    |   70 
 man/get_language_model.Rd                                                                 |   46 
 man/get_preds_cyclegan.Rd                                                                 |   70 
 man/get_text_classifier.Rd                                                                |   98 
 man/get_text_files.Rd                                                                     |   42 
 man/get_weights.Rd                                                                        |   64 
 man/grapes-f-grapes.Rd                                                                    |   38 
 man/grapes-grapes-.fastai.torch_core.TensorMask.Rd                                        |   38 
 man/grapes-slash-grapes-.fastai.torch_core.TensorMask.Rd                                  |   38 
 man/grayscale.Rd                                                                          |   34 
 man/greater-than-.fastai.torch_core.TensorMask.Rd                                         |   38 
 man/greater-than-equals-.fastai.torch_core.TensorMask.Rd                                  |   38 
 man/greater.Rd                                                                            |   40 
 man/greater_or_equal.Rd                                                                   |   40 
 man/has_params.Rd                                                                         |   34 
 man/has_pool_type.Rd                                                                      |   34 
 man/helper.Rd                                                                             |   28 
 man/hf_splitter.Rd                                                                        |   34 
 man/hook_output.Rd                                                                        |   46 
 man/hook_outputs.Rd                                                                       |   46 
 man/hsv2rgb.Rd                                                                            |   34 
 man/hug.Rd                                                                                |   28 
 man/icevision.Rd                                                                          |   28 
 man/icevision_Adapter.Rd                                                                  |   34 
 man/icevision_BasicIAATransform.Rd                                                        |   38 
 man/icevision_BasicTransform.Rd                                                           |   38 
 man/icevision_Blur.Rd                                                                     |   62 
 man/icevision_CLAHE.Rd                                                                    |   76 
 man/icevision_ChannelDropout.Rd                                                           |   70 
 man/icevision_ChannelShuffle.Rd                                                           |   58 
 man/icevision_ClassMap.Rd                                                                 |   38 
 man/icevision_CoarseDropout.Rd                                                            |  122 
 man/icevision_ColorJitter.Rd                                                              |   78 
 man/icevision_Compose.Rd                                                                  |   62 
 man/icevision_Crop.Rd                                                                     |   82 
 man/icevision_CropNonEmptyMaskIfExists.Rd                                                 |   88 
 man/icevision_Cutout.Rd                                                                   |   98 
 man/icevision_Dataset.Rd                                                                  |   50 
 man/icevision_Dataset_from_images.Rd                                                      |   42 
 man/icevision_Downscale.Rd                                                                |   82 
 man/icevision_DualIAATransform.Rd                                                         |   38 
 man/icevision_DualTransform.Rd                                                            |   38 
 man/icevision_ElasticTransform.Rd                                                         |  118 
 man/icevision_Equalize.Rd                                                                 |   66 
 man/icevision_FDA.Rd                                                                      |  128 
 man/icevision_FancyPCA.Rd                                                                 |   78 
 man/icevision_FixedSplitter.Rd                                                            |   34 
 man/icevision_Flip.Rd                                                                     |   58 
 man/icevision_FromFloat.Rd                                                                |   84 
 man/icevision_GaussNoise.Rd                                                               |   76 
 man/icevision_GaussianBlur.Rd                                                             |   76 
 man/icevision_GlassBlur.Rd                                                                |   98 
 man/icevision_GridDistortion.Rd                                                           |  106 
 man/icevision_GridDropout.Rd                                                              |  134 
 man/icevision_HistogramMatching.Rd                                                        |  104 
 man/icevision_HorizontalFlip.Rd                                                           |   58 
 man/icevision_HueSaturationValue.Rd                                                       |   82 
 man/icevision_IAAAdditiveGaussianNoise.Rd                                                 |   72 
 man/icevision_IAAAffine.Rd                                                                |  111 
 man/icevision_IAACropAndPad.Rd                                                            |   68 
 man/icevision_IAAEmboss.Rd                                                                |   66 
 man/icevision_IAAFliplr.Rd                                                                |   38 
 man/icevision_IAAFlipud.Rd                                                                |   38 
 man/icevision_IAAPerspective.Rd                                                           |   71 
 man/icevision_IAAPiecewiseAffine.Rd                                                       |   95 
 man/icevision_IAASharpen.Rd                                                               |   66 
 man/icevision_IAASuperpixels.Rd                                                           |   72 
 man/icevision_ISONoise.Rd                                                                 |   76 
 man/icevision_ImageCompression.Rd                                                         |   82 
 man/icevision_ImageOnlyIAATransform.Rd                                                    |   38 
 man/icevision_ImageOnlyTransform.Rd                                                       |   38 
 man/icevision_InvertImg.Rd                                                                |   58 
 man/icevision_JpegCompression.Rd                                                          |   76 
 man/icevision_LongestMaxSize.Rd                                                           |   76 
 man/icevision_MaskDropout.Rd                                                              |   70 
 man/icevision_MedianBlur.Rd                                                               |   62 
 man/icevision_MotionBlur.Rd                                                               |   62 
 man/icevision_MultiplicativeNoise.Rd                                                      |   82 
 man/icevision_Normalize.Rd                                                                |   82 
 man/icevision_OpticalDistortion.Rd                                                        |  106 
 man/icevision_PadIfNeeded.Rd                                                              |  100 
 man/icevision_Posterize.Rd                                                                |   62 
 man/icevision_RGBShift.Rd                                                                 |  118 
 man/icevision_RandomBrightnessContrast.Rd                                                 |   82 
 man/icevision_RandomContrast.Rd                                                           |   62 
 man/icevision_RandomCrop.Rd                                                               |   66 
 man/icevision_RandomCropNearBBox.Rd                                                       |   62 
 man/icevision_RandomFog.Rd                                                                |   88 
 man/icevision_RandomGamma.Rd                                                              |   82 
 man/icevision_RandomGridShuffle.Rd                                                        |   62 
 man/icevision_RandomRain.Rd                                                               |  118 
 man/icevision_RandomResizedCrop.Rd                                                        |   94 
 man/icevision_RandomRotate90.Rd                                                           |   58 
 man/icevision_RandomScale.Rd                                                              |   76 
 man/icevision_RandomShadow.Rd                                                             |   94 
 man/icevision_RandomSizedBBoxSafeCrop.Rd                                                  |  126 
 man/icevision_RandomSizedCrop.Rd                                                          |   94 
 man/icevision_RandomSnow.Rd                                                               |   88 
 man/icevision_RandomSplitter.Rd                                                           |   40 
 man/icevision_RandomSunFlare.Rd                                                           |  112 
 man/icevision_Resize.Rd                                                                   |   70 
 man/icevision_Rotate.Rd                                                                   |   94 
 man/icevision_ShiftScaleRotate.Rd                                                         |  166 
 man/icevision_SingleSplitSplitter.Rd                                                      |   34 
 man/icevision_SmallestMaxSize.Rd                                                          |   76 
 man/icevision_Solarize.Rd                                                                 |   62 
 man/icevision_ToFloat.Rd                                                                  |   80 
 man/icevision_ToGray.Rd                                                                   |   60 
 man/icevision_ToSepia.Rd                                                                  |   58 
 man/icevision_Transpose.Rd                                                                |   58 
 man/icevision_VerticalFlip.Rd                                                             |   58 
 man/icevision_aug_tfms.Rd                                                                 |   98 
 man/icevision_parse.Rd                                                                    |   62 
 man/icevision_read_bgr_image.Rd                                                           |   34 
 man/icevision_read_rgb_image.Rd                                                           |   34 
 man/icevision_resize_and_pad.Rd                                                           |   44 
 man/icnr_init.Rd                                                                          |   42 
 man/image2tensor.Rd                                                                       |   34 
 man/imagenet_stats.Rd                                                                     |   48 
 man/in_channels.Rd                                                                        |   34 
 man/init.Rd                                                                               |   46 
 man/init_default.Rd                                                                       |   38 
 man/init_linear.Rd                                                                        |   46 
 man/install_fastai.Rd                                                                     |   68 
 man/is_rmarkdown.Rd                                                                       |   28 
 man/kg.Rd                                                                                 |   28 
 man/l2_reg.Rd                                                                             |   96 
 man/lamb_step.Rd                                                                          |   66 
 man/language_model_learner.Rd                                                             |  140 
 man/larc_layer_lr.Rd                                                                      |   58 
 man/larc_step.Rd                                                                          |   46 
 man/layer_info.Rd                                                                         |   38 
 man/length.Rd                                                                             |   36 
 man/length.fastai.torch_core.TensorMask.Rd                                                |   34 
 man/less-than-.fastai.torch_core.TensorMask.Rd                                            |   38 
 man/less-than-equals-.fastai.torch_core.TensorMask.Rd                                     |   38 
 man/less.Rd                                                                               |   40 
 man/less_or_equal.Rd                                                                      |   40 
 man/load_dataset.Rd                                                                       |   42 
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 man/load_image.Rd                                                                         |   38 
 man/load_learner.Rd                                                                       |   38 
 man/load_model_text.Rd                                                                    |   68 
 man/load_pre_models.Rd                                                                    |   28 
 man/load_tokenized_csv.Rd                                                                 |   34 
 man/loaders.Rd                                                                            |   48 
 man/log.Rd                                                                                |   40 
 man/log.fastai.torch_core.TensorMask.Rd                                                   |   38 
 man/log1p.Rd                                                                              |   36 
 man/log1p.fastai.torch_core.TensorMask.Rd                                                 |   34 
 man/logical_and.Rd                                                                        |   40 
 man/logical_not_.Rd                                                                       |   36 
 man/logical_or.Rd                                                                         |   40 
 man/login.Rd                                                                              |   50 
 man/lr_find.Rd                                                                            |   88 
 man/mae.Rd                                                                                |   38 
 man/make_vocab.Rd                                                                         |   46 
 man/mask2bbox.Rd                                                                          |   38 
 man/mask_eq.Rd                                                                            |   38 
 man/mask_from_blur.Rd                                                                     |   44 
 man/mask_rcnn_infer_dl.Rd                                                                 |   42 
 man/mask_rcnn_learner.Rd                                                                  |   48 
 man/mask_rcnn_model.Rd                                                                    |   56 
 man/mask_rcnn_predict_dl.Rd                                                               |   42 
 man/mask_rcnn_train_dl.Rd                                                                 |   42 
 man/mask_rcnn_valid_dl.Rd                                                                 |   42 
 man/mask_tensor.Rd                                                                        |   46 
 man/masked_concat_pool.Rd                                                                 |   42 
 man/match_embeds.Rd                                                                       |   42 
 man/max.Rd                                                                                |   44 
 man/max.fastai.torch_core.TensorMask.Rd                                                   |   42 
 man/maybe_unsqueeze.Rd                                                                    |   34 
 man/mean.fastai.torch_core.TensorMask.Rd                                                  |   38 
 man/mean.torch.Tensor.Rd                                                                  |   38 
 man/medical.Rd                                                                            |   28 
 man/metrics.Rd                                                                            |   28 
 man/migrating_ignite.Rd                                                                   |   28 
 man/migrating_lightning.Rd                                                                |   28 
 man/migrating_pytorch.Rd                                                                  |   28 
 man/min.Rd                                                                                |   44 
 man/min.fastai.torch_core.TensorMask.Rd                                                   |   42 
 man/mish.Rd                                                                               |   34 
 man/model_sizes.Rd                                                                        |   38 
 man/momentum_step.Rd                                                                      |   46 
 man/most_confused.Rd                                                                      |   40 
 man/mse.Rd                                                                                |   58 
 man/msle.Rd                                                                               |   38 
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 man/n_px.Rd                                                                               |   34 
 man/narrow.Rd                                                                             |   38 
 man/nn.Rd                                                                                 |   28 
 man/nn_loss.Rd                                                                            |   38 
 man/nn_module.Rd                                                                          |   42 
 man/noop.Rd                                                                               |   34 
 man/norm_apply_denorm.Rd                                                                  |   42 
 man/not__mask.Rd                                                                          |   36 
 man/not_equal_to.Rd                                                                       |   40 
 man/not_equal_to_mask_.Rd                                                                 |   40 
 man/num_features_model.Rd                                                                 |   34 
 man/one_batch.Rd                                                                          |   62 
 man/optim_metric.Rd                                                                       |   54 
 man/or_mask.Rd                                                                            |   40 
 man/os.Rd                                                                                 |   28 
 man/os_environ_tpu.Rd                                                                     |   34 
 man/pad_conv_norm_relu.Rd                                                                 |   98 
 man/pad_input.Rd                                                                          |   62 
 man/pad_input_chunk.Rd                                                                    |   46 
 man/parallel.Rd                                                                           |   42 
 man/parallel_tokenize.Rd                                                                  |   46 
 man/params.Rd                                                                             |   34 
 man/parent_label.Rd                                                                       |   34 
 man/parsers_AreasMixin.Rd                                                                 |   34 
 man/parsers_BBoxesMixin.Rd                                                                |   34 
 man/parsers_FasterRCNN.Rd                                                                 |   34 
 man/parsers_FilepathMixin.Rd                                                              |   34 
 man/parsers_ImageidMixin.Rd                                                               |   34 
 man/parsers_IsCrowdsMixin.Rd                                                              |   34 
 man/parsers_LabelsMixin.Rd                                                                |   34 
 man/parsers_MaskRCNN.Rd                                                                   |   34 
 man/parsers_MasksMixin.Rd                                                                 |   34 
 man/parsers_SizeMixin.Rd                                                                  |   34 
 man/parsers_voc.Rd                                                                        |   46 
 man/partial.Rd                                                                            |   56 
 man/pca.Rd                                                                                |   42 
 man/plot.Rd                                                                               |   46 
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 man/pow.Rd                                                                                |   40 
 man/pre_process_squad.Rd                                                                  |   42 
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 man/preplexity.Rd                                                                         |   34 
 man/preprocess_audio_folder.Rd                                                            |   68 
 man/print.fastai.learner.Learner.Rd                                                       |   38 
 man/print.fastai.tabular.learner.TabularLearner.Rd                                        |   38 
 man/print.pydicom.dataset.FileDataset.Rd                                                  |   38 
 man/py_apply.Rd                                                                           |   38 
 man/python_path.Rd                                                                        |   28 
 man/qhadam_step.Rd                                                                        |   74 
 man/radam_step.Rd                                                                         |   70 
 man/ranger.Rd                                                                             |   80 
 man/reexports.Rd                                                                          |  108 
 man/replace_all_caps.Rd                                                                   |   34 
 man/replace_maj.Rd                                                                        |   34 
 man/replace_rep.Rd                                                                        |   34 
 man/replace_wrep.Rd                                                                       |   34 
 man/res_block_1d.Rd                                                                       |   38 
 man/reshape.Rd                                                                            |   46 
 man/resize_max.Rd                                                                         |   50 
 man/resnet101.Rd                                                                          |   44 
 man/resnet152.Rd                                                                          |   44 
 man/resnet18.Rd                                                                           |   44 
 man/resnet34.Rd                                                                           |   44 
 man/resnet50.Rd                                                                           |   44 
 man/resnet_generator.Rd                                                                   |   74 
 man/retinanet_.Rd                                                                         |   28 
 man/reverse_text.Rd                                                                       |   34 
 man/rgb2hsv.Rd                                                                            |   40 
 man/rm_useless_spaces.Rd                                                                  |   52 
 man/rms_prop_step.Rd                                                                      |   54 
 man/rmse.Rd                                                                               |   58 
 man/rotate_mat.Rd                                                                         |   50 
 man/round.Rd                                                                              |   40 
 man/round.fastai.torch_core.TensorMask.Rd                                                 |   38 
 man/sequential.Rd                                                                         |   34 
 man/set_freeze_model.Rd                                                                   |   38 
 man/set_item_pg.Rd                                                                        |   42 
 man/setup_aug_tfms.Rd                                                                     |   34 
 man/sgd_step.Rd                                                                           |   88 
 man/shap.Rd                                                                               |   28 
 man/shape.Rd                                                                              |   34 
 man/show.Rd                                                                               |   46 
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 man/show_batch.Rd                                                                         |  104 
 man/show_image.Rd                                                                         |  110 
 man/show_images.Rd                                                                        |   74 
 man/show_preds.Rd                                                                         |   98 
 man/show_results.Rd                                                                       |   74 
 man/show_samples.Rd                                                                       |   98 
 man/sigmoid.Rd                                                                            |   38 
 man/sigmoid_.Rd                                                                           |   38 
 man/sigmoid_range.Rd                                                                      |   42 
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 man/sin_.Rd                                                                               |   36 
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 man/slice.Rd                                                                              |   40 
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 man/step_stat.Rd                                                                          |   42 
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 man/sub_mask.Rd                                                                           |   40 
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 man/swish.Rd                                                                              |   38 
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 man/tar_extract_at_filename.Rd                                                            |   38 
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 man/tensor_eq.Rd                                                                          |   38 
 man/tensor_eq_img.Rd                                                                      |   38 
 man/test_loader.Rd                                                                        |   38 
 man/text.Rd                                                                               |   28 
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 man/times-.fastai.torch_core.TensorMask.Rd                                                |   38 
 man/timm.Rd                                                                               |   28 
 man/timm_learner.Rd                                                                       |   42 
 man/timm_list_models.Rd                                                                   |   34 
 man/tms.Rd                                                                                |   28 
 man/to_bytes_format.Rd                                                                    |   38 
 man/to_image.Rd                                                                           |   34 
 man/to_matrix.Rd                                                                          |   32 
 man/to_thumb.Rd                                                                           |   42 
 man/to_xla.Rd                                                                             |   34 
 man/tokenize1.Rd                                                                          |   46 
 man/tokenize_csv.Rd                                                                       |   86 
 man/tokenize_df.Rd                                                                        |   74 
 man/tokenize_files.Rd                                                                     |   86 
 man/tokenize_folder.Rd                                                                    |   92 
 man/tokenize_texts.Rd                                                                     |   46 
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 man/torch.Rd                                                                              |   28 
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 man/train_loader.Rd                                                                       |   38 
 man/trainable_params.Rd                                                                   |   34 
 man/transformers.Rd                                                                       |   28 
 man/trunc_normal_.Rd                                                                      |   42 
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 man/unet_learner.Rd                                                                       |   42 
 man/unfreeze.Rd                                                                           |   50 
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 man/upit.Rd                                                                               |   28 
 man/vgg11_bn.Rd                                                                           |   44 
 man/vgg13_bn.Rd                                                                           |   44 
 man/vgg16_bn.Rd                                                                           |   44 
 man/vgg19_bn.Rd                                                                           |   44 
 man/vision.Rd                                                                             |   28 
 man/vleaky_relu.Rd                                                                        |   38 
 man/wandb.Rd                                                                              |   28 
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 man/win_brain.Rd                                                                          |   28 
 man/win_brain_bone.Rd                                                                     |   28 
 man/win_brain_soft.Rd                                                                     |   28 
 man/win_liver.Rd                                                                          |   28 
 man/win_lungs.Rd                                                                          |   28 
 man/win_mediastinum.Rd                                                                    |   28 
 man/win_spine_bone.Rd                                                                     |   28 
 man/win_spine_soft.Rd                                                                     |   28 
 man/win_stroke.Rd                                                                         |   28 
 man/win_subdural.Rd                                                                       |   28 
 man/xla.Rd                                                                                |   28 
 man/xresnet101.Rd                                                                         |   34 
 man/xresnet152.Rd                                                                         |   34 
 man/xresnet18.Rd                                                                          |   34 
 man/xresnet18_deep.Rd                                                                     |   34 
 man/xresnet18_deeper.Rd                                                                   |   34 
 man/xresnet34.Rd                                                                          |   34 
 man/xresnet34_deep.Rd                                                                     |   34 
 man/xresnet34_deeper.Rd                                                                   |   34 
 man/xresnet50.Rd                                                                          |   34 
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 man/xresnet50_deeper.Rd                                                                   |   34 
 man/xresnext101.Rd                                                                        |   34 
 man/xresnext18.Rd                                                                         |   34 
 man/xresnext34.Rd                                                                         |   34 
 man/xresnext50.Rd                                                                         |   34 
 man/xse_resnet101.Rd                                                                      |   34 
 man/xse_resnet152.Rd                                                                      |   34 
 man/xse_resnet18.Rd                                                                       |   34 
 man/xse_resnet34.Rd                                                                       |   34 
 man/xse_resnet50.Rd                                                                       |   34 
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 man/xse_resnext18.Rd                                                                      |   34 
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 man/xsenet154.Rd                                                                          |   34 
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 man/zoom_mat.Rd                                                                           |   80 
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Package DanielBiostatistics10th updated to version 0.2.0 with previous version 0.1.11 dated 2024-04-03

Title: Functions for Wayne W. Daniel's Biostatistics, Tenth Edition
Description: Functions to accompany Wayne W. Daniel's Biostatistics: A Foundation for Analysis in the Health Sciences, Tenth Edition.
Author: Tingting Zhan [aut, cre, cph]
Maintainer: Tingting Zhan <tingtingzhan@gmail.com>

Diff between DanielBiostatistics10th versions 0.1.11 dated 2024-04-03 and 0.2.0 dated 2024-04-11

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Package r5r updated to version 2.0 with previous version 1.1.0 dated 2023-08-08

Title: Rapid Realistic Routing with 'R5'
Description: Rapid realistic routing on multimodal transport networks (walk, bike, public transport and car) using 'R5', the Rapid Realistic Routing on Real-world and Reimagined networks engine <https://github.com/conveyal/r5>. The package allows users to generate detailed routing analysis or calculate travel time matrices using seamless parallel computing on top of the R5 Java machine. While R5 is developed by Conveyal, the package r5r is independently developed by a team at the Institute for Applied Economic Research (Ipea) with contributions from collaborators. Apart from the documentation in this package, users will find additional information on R5 documentation at <https://docs.conveyal.com/>. Although we try to keep new releases of r5r in synchrony with R5, the development of R5 follows Conveyal's independent update process. Hence, users should confirm the R5 version implied by the Conveyal user manual (see <https://docs.conveyal.com/changelog>) corresponds with the R5 ver [...truncated...]
Author: Marcus Saraiva [aut] , Rafael H. M. Pereira [aut, cre] , Daniel Herszenhut [aut] , Carlos Kaue Vieira Braga [aut] , Matthew Wigginton Bhagat-Conway [aut] , Luyu Liu [ctb] , Ipea - Institute for Applied Economic Research [cph, fnd]
Maintainer: Rafael H. M. Pereira <rafa.pereira.br@gmail.com>

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Package PatientProfiles updated to version 0.8.0 with previous version 0.7.0 dated 2024-03-28

Title: Identify Characteristics of Patients in the OMOP Common Data Model
Description: Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model.
Author: Marti Catala [aut, cre] , Yuchen Guo [aut] , Mike Du [aut] , Kim Lopez-Guell [aut] , Edward Burn [aut] , Nuria Mercade-Besora [ctb] , Xintong Li [ctb]
Maintainer: Marti Catala <marti.catalasabate@ndorms.ox.ac.uk>

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Package Directional updated to version 6.6 with previous version 6.5 dated 2024-02-29

Title: A Collection of Functions for Directional Data Analysis
Description: A collection of functions for directional data (including massive data, with millions of observations) analysis. Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000). Other references include a) Phillip J. Paine, Simon P. Preston Michail Tsagris and Andrew T. A. Wood (2018). "An elliptically symmetric angular Gaussian distribution". Statistics and Computing 28(3): 689-697. <doi:10.1007/s11222-017-9756-4>. b) Tsagris M. and Alenazi A. (2019). "Comparison of discriminant analysis methods on the sphere". Communications in Statistics: Case Studies, Data Analysis and Applications 5(4):467--491. <doi:10.1080/23737484.2019.1684854>. c) P. J. Paine, S. P. Preston, M. Tsagris and Andrew T. A. Wood (2020). "Spherical regression models with general covariates and anisotropic errors". Statistics and Computing 30(1): 153--165. <d [...truncated...]
Author: Michail Tsagris, Giorgos Athineou, Christos Adam, Anamul Sajib, Eli Amson, Micah J. Waldstein
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

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Package asremlPlus updated to version 4.4.32 with previous version 4.4.27 dated 2024-02-05

Title: Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences
Description: Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The [...truncated...]
Author: Chris Brien [aut, cre]
Maintainer: Chris Brien <chris.brien@adelaide.edu.au>

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Package ape updated to version 5.8 with previous version 5.7-1 dated 2023-03-13

Title: Analyses of Phylogenetics and Evolution
Description: Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results [...truncated...]
Author: Emmanuel Paradis [aut, cre, cph] , Simon Blomberg [aut, cph] , Ben Bolker [aut, cph] , Joseph Brown [aut, cph] , Santiago Claramunt [aut, cph] , Julien Claude [aut, cph] , Hoa Sien Cuong [aut, cph], Richard Desper [aut, cph], Gilles Didier [aut, cph] [...truncated...]
Maintainer: Emmanuel Paradis <Emmanuel.Paradis@ird.fr>

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More information about ape at CRAN
Permanent link

New package sgboost with initial version 0.1.0
Package: sgboost
Title: Sparse-Group Boosting
Version: 0.1.0
Description: Sparse-group boosting to be used in conjunction with the 'mboost' for modeling grouped data. Applicable to all sparse-group lasso type problems where within-group and between-group sparsity is desired. Interprets and visualizes individual variables and groups.
Imports: dplyr, mboost, stringr, rlang, tibble, ggplot2, ggforce
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/FabianObster/sgboost
BugReports: https://github.com/FabianObster/sgboost/issues
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-04-10 20:03:03 UTC; fabia
Author: Fabian Obster [aut, cre, cph]
Maintainer: Fabian Obster <fabian.obster@unibw.de>
Repository: CRAN
Date/Publication: 2024-04-11 11:20:02 UTC

More information about sgboost at CRAN
Permanent link

New package SBI with initial version 0.1.0
Package: SBI
Title: Simple Blinding Index for Randomized Controlled Trials
Version: 0.1.0
Description: Computes a simple blinding index for randomized controlled trials introduced in the paper "A simple blinding index for randomized controlled trials" by Petroff, Bacak, Dagres, Dilk and Wachter, which has been submitted for publication.
License: GPL-3
Encoding: UTF-8
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2024-04-11 06:37:21 UTC; mir8629bac
Author: David Petroff [aut, cre, cph], Miroslav Bacak [aut, cph]
Maintainer: David Petroff <david.petroff@zks.uni-leipzig.de>
Repository: CRAN
Date/Publication: 2024-04-11 11:30:03 UTC

More information about SBI at CRAN
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Package GeoModels updated to version 2.0.1 with previous version 2.0.0 dated 2024-04-09

Title: Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis
Description: Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. Se [...truncated...]
Author: Moreno Bevilacqua [aut, cre] , Victor Morales-Onate [aut] , Christian Caamano-Carrillo [aut]
Maintainer: Moreno Bevilacqua <moreno.bevilacqua89@gmail.com>

Diff between GeoModels versions 2.0.0 dated 2024-04-09 and 2.0.1 dated 2024-04-11

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New package alien with initial version 1.0.1
Package: alien
Title: Estimate Invasive and Alien Species (IAS) Introduction Rates
Version: 1.0.1
Description: Easily estimate the introduction rates of alien species given first records data. It specializes in addressing the role of sampling on the pattern of discoveries, thus providing better estimates than using Generalized Linear Models which assume perfect immediate detection of newly introduced species.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: cli, dplyr, ggplot2, HelpersMG, rlang, stats, tidyr
Depends: R (>= 3.4.0)
LazyData: true
Suggests: BH, knitr, purrr, Rcpp, RcppEigen, RcppParallel, rmarkdown, rstan, testthat (>= 3.0.0), tibble
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-04-11 10:45:40 UTC; eze36
Author: Yehezkel Buba [aut, cre, cph]
Maintainer: Yehezkel Buba <hezibuba@mail.tau.ac.il>
Repository: CRAN
Date/Publication: 2024-04-11 11:50:02 UTC

More information about alien at CRAN
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Package evsim updated to version 1.5.0 with previous version 1.4.0 dated 2024-03-14

Title: Electric Vehicle Charging Sessions Simulation
Description: Simulation of Electric Vehicles charging sessions using Gaussian models, together with time-series power demand calculations. The simulation methodology is published in Cañigueral et al. (2023, ISBN:0957-4174) <doi:10.1016/j.eswa.2023.120318>.
Author: Marc Canigueral [aut, cre]
Maintainer: Marc Canigueral <marc.canigueral@udg.edu>

Diff between evsim versions 1.4.0 dated 2024-03-14 and 1.5.0 dated 2024-04-11

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Package butcher updated to version 0.3.4 with previous version 0.3.3 dated 2023-08-22

Title: Model Butcher
Description: Provides a set of S3 generics to axe components of fitted model objects and help reduce the size of model objects saved to disk.
Author: Joyce Cahoon [aut] , Davis Vaughan [aut], Max Kuhn [aut], Alex Hayes [aut], Julia Silge [cre, aut] , Posit, PBC [cph, fnd]
Maintainer: Julia Silge <julia.silge@posit.co>

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Package rhub updated to version 2.0.0 with previous version 1.1.2 dated 2022-08-31

Title: Tools for R Package Developers
Description: R-hub v2 uses GitHub Actions to run 'R CMD check' and similar package checks. The 'rhub' package helps you set up R-hub v2 for your R package, and start running checks.
Author: Gabor Csardi [aut, cre], Maelle Salmon [aut] , R Consortium [fnd]
Maintainer: Gabor Csardi <csardi.gabor@gmail.com>

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Package text2map updated to version 0.2.0 with previous version 0.1.9 dated 2024-03-12

Title: R Tools for Text Matrices, Embeddings, and Networks
Description: This is a collection of functions optimized for working with with various kinds of text matrices. Focusing on the text matrix as the primary object - represented either as a base R dense matrix or a 'Matrix' package sparse matrix - allows for a consistent and intuitive interface that stays close to the underlying mathematical foundation of computational text analysis. In particular, the package includes functions for working with word embeddings, text networks, and document-term matrices. Methods developed in Stoltz and Taylor (2019) <doi:10.1007/s42001-019-00048-6>, Taylor and Stoltz (2020) <doi:10.1007/s42001-020-00075-8>, Taylor and Stoltz (2020) <doi:10.15195/v7.a23>, and Stoltz and Taylor (2021) <doi:10.1016/j.poetic.2021.101567>.
Author: Dustin Stoltz [aut, cre] , Marshall Taylor [aut]
Maintainer: Dustin Stoltz <dss219@lehigh.edu>

Diff between text2map versions 0.1.9 dated 2024-03-12 and 0.2.0 dated 2024-04-11

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Package SticsRFiles updated to version 1.3.0 with previous version 1.2.0 dated 2024-02-23

Title: Read and Modify 'STICS' Input/Output Files
Description: Manipulating input and output files of the 'STICS' crop model. Files are either 'JavaSTICS' XML files or text files used by the model 'fortran' executable. Most basic functionalities are reading or writing parameter names and values in both XML or text input files, and getting data from output files. Advanced functionalities include XML files generation from XML templates and/or spreadsheets, or text files generation from XML files by using 'xslt' transformation.
Author: Patrice Lecharpentier [aut, cre] , Remi Vezy [aut] , Samuel Buis [aut] , Michel Giner [aut] , Timothee Flutre [ctb], Thomas Robine [ctb], Amine Barkaoui [ctb], Patrick Chabrier [ctb], Julie Constantin [rev], Dominique Ripoche [rev], Marie Launay [rev [...truncated...]
Maintainer: Patrice Lecharpentier <patrice.lecharpentier@inrae.fr>

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Package stevedata updated to version 1.2.0 with previous version 1.1.0 dated 2023-10-25

Title: Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics
Description: This is a collection of various kinds of data with broad uses for teaching. My students, and academics like me who teach the same topics I teach, should find this useful if their teaching workflow is also built around the R programming language. The applications are multiple but mostly cluster on topics of statistical methodology, international relations, and political economy.
Author: Steve Miller [aut, cre]
Maintainer: Steve Miller <steve@svmiller.com>

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Package SimDesign updated to version 2.15 with previous version 2.14 dated 2024-01-09

Title: Structure for Organizing Monte Carlo Simulation Designs
Description: Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.
Author: Phil Chalmers [aut, cre] , Matthew Sigal [ctb], Ogreden Oguzhan [ctb], Mikko Ronkko [ctb]
Maintainer: Phil Chalmers <rphilip.chalmers@gmail.com>

Diff between SimDesign versions 2.14 dated 2024-01-09 and 2.15 dated 2024-04-11

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Package seededlda updated to version 1.2.1 with previous version 1.2.0 dated 2024-04-10

Title: Seeded Sequential LDA for Topic Modeling
Description: Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>. Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.
Author: Kohei Watanabe [aut, cre, cph], Phan Xuan-Hieu [aut, cph]
Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com>

Diff between seededlda versions 1.2.0 dated 2024-04-10 and 1.2.1 dated 2024-04-11

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Package quanteda.textmodels updated to version 0.9.7 with previous version 0.9.6 dated 2023-03-22

Title: Scaling Models and Classifiers for Textual Data
Description: Scaling models and classifiers for sparse matrix objects representing textual data in the form of a document-feature matrix. Includes original implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>, 'Wordscores' model, the Perry and 'Benoit' (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model, and the 'Slapin' and 'Proksch' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish' model, as well as methods for correspondence analysis, latent semantic analysis, and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.
Author: Kenneth Benoit [cre, aut, cph] , Kohei Watanabe [aut] , Haiyan Wang [aut] , Patrick O. Perry [aut] , Benjamin Lauderdale [aut] , Johannes Gruber [aut] , William Lowe [aut] , Vikas Sindhwani [cph] , European Research Council [fnd]
Maintainer: Kenneth Benoit <kbenoit@lse.ac.uk>

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Package matrixStats updated to version 1.3.0 with previous version 1.2.0 dated 2023-12-11

Title: Functions that Apply to Rows and Columns of Matrices (and to Vectors)
Description: High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().
Author: Henrik Bengtsson [aut, cre, cph], Constantin Ahlmann-Eltze [ctb], Hector Corrada Bravo [ctb], Robert Gentleman [ctb], Jan Gleixner [ctb], Peter Hickey [ctb], Ola Hossjer [ctb], Harris Jaffee [ctb], Dongcan Jiang [ctb], Peter Langfelder [ctb], Brian M [...truncated...]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package mirai updated to version 0.13.2 with previous version 0.12.1 dated 2024-02-02

Title: Minimalist Async Evaluation Framework for R
Description: Lightweight parallel code execution and distributed computing. Designed for simplicity, a 'mirai' evaluates an R expression asynchronously, on local or network resources, resolving automatically upon completion. State of the art networking and concurrency via 'nanonext' and 'NNG' (Nanomsg Next Gen) offers reliable and efficient scheduling over fast inter-process communications or TCP/IP secured by TLS.
Author: Charlie Gao [aut, cre] , Hibiki AI Limited [cph]
Maintainer: Charlie Gao <charlie.gao@shikokuchuo.net>

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Package renv updated to version 1.0.6 with previous version 1.0.5 dated 2024-02-28

Title: Project Environments
Description: A dependency management toolkit for R. Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.
Author: Kevin Ushey [aut, cre] , Hadley Wickham [aut] , Posit Software, PBC [cph, fnd]
Maintainer: Kevin Ushey <kevin@rstudio.com>

Diff between renv versions 1.0.5 dated 2024-02-28 and 1.0.6 dated 2024-04-11

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Wed, 10 Apr 2024

Package umx updated to version 4.20.0 with previous version 4.19.0 dated 2023-11-16

Title: Structural Equation Modeling and Twin Modeling in R
Description: Quickly create, run, and report structural equation models, and twin models. See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS. Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.
Author: Timothy C. Bates [aut, cre] , Nathan Gillespie [wit], Hermine Maes [ctb], Michael C. Neale [ctb], Joshua N. Pritikin [ctb], Luis De Araujo [ctb], Brenton Wiernik [ctb], Michael Zakharin [wit]
Maintainer: Timothy C. Bates <timothy.c.bates@gmail.com>

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Package prismatic updated to version 1.1.2 with previous version 1.1.1 dated 2022-08-15

Title: Color Manipulation Tools
Description: Manipulate and visualize colors in a intuitive, low-dependency and functional way.
Author: Emil Hvitfeldt [aut, cre]
Maintainer: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>

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Package pressuRe updated to version 0.2.4 with previous version 0.2.3 dated 2023-10-05

Title: Imports, Processes, and Visualizes Biomechanical Pressure Data
Description: Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) <doi:10.1038/s41598-022-19814-0>, Lee et al., (2014) <doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014) <doi:10.1186/1757-1146-7-20>, Najafi et al., (2010) <doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers (1987) <doi:10.1016/0021-9290(87)90255-7>.
Author: Scott Telfer [aut, cre, cph] , Ellen Li [aut]
Maintainer: Scott Telfer <scott.telfer@gmail.com>

Diff between pressuRe versions 0.2.3 dated 2023-10-05 and 0.2.4 dated 2024-04-10

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Package diptest updated to version 0.77-1 with previous version 0.77-0 dated 2023-11-27

Title: Hartigan's Dip Test Statistic for Unimodality - Corrected
Description: Compute Hartigan's dip test statistic for unimodality / multimodality and provide a test with simulation based p-values, where the original public code has been corrected.
Author: Martin Maechler
Maintainer: Martin Maechler <maechler@stat.math.ethz.ch>

Diff between diptest versions 0.77-0 dated 2023-11-27 and 0.77-1 dated 2024-04-10

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New package HDXBoxeR with initial version 0.0.1
Package: HDXBoxeR
Title: Analysis of Hydrogen-Deuterium Exchange Mass-Spectrometry Data
Version: 0.0.1
Author: Maria K. Janowska [aut, cre] , Katherine Reiter [ctb], Pearl Magala [ctb], Miklos Guttman [ctb], Rachel E. Klevit [ctb]
Maintainer: Maria K. Janowska <mka.janowska@gmail.com>
Description: A protocol that facilitates the processing and analysis of Hydrogen-Deuterium Exchange Mass Spectrometry data using p-value statistics and Critical Interval analysis. It provides a pipeline for analyzing data from 'HDXExaminer' (Sierra Analytics, Trajan Scientific), automating matching and comparison of protein states through Welch's T-test and the Critical Interval statistical framework. Additionally, it simplifies data export, generates 'Pymol' scripts, and ensures calculations meet publication standards. 'HDXBoxeR' assists in various aspects of hydrogen-deuterium exchange data analysis, including reprocessing data, calculating parameters, identifying significant peptides, generating plots, and facilitating comparison between protein states. For details check papers by Hageman and Weis (2019) <doi:10.1021/acs.analchem.9b01325> and Masson et al. (2019) <doi:10.1038/s41592-019-0459-y>.
License: GPL (>= 2)
Imports: dplyr, graphics, grDevices, RColorBrewer, stats, stringr, tidyr, utils, methods, wrapr
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr, rmarkdown
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-04-10 18:36:44 UTC; mkaja
Repository: CRAN
Date/Publication: 2024-04-10 20:10:02 UTC

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New package WayFindR with initial version 0.1.2
Package: WayFindR
Version: 0.1.2
Date: 2024-04-09
Title: Computing Graph Structures on WikiPathways
Description: Converts pathways from 'WikiPathways' GPML format into 'igraph' objects. Includes tools to find all cycles in the resulting graphs and determine which ones involve negative feedback (inhibition).
Depends: R (>= 3.5)
Imports: graphics, utils, XML, igraph
Suggests: knitr, rmarkdown, Polychrome
License: Artistic-2.0
URL: http://oompa.r-forge.r-project.org/
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-04-09 20:36:30 UTC; kevin
Author: Kevin R. Coombes [aut, cre], Polina Bombina [aut]
Maintainer: Kevin R. Coombes <krc@silicovore.com>
Repository: CRAN
Date/Publication: 2024-04-10 19:50:06 UTC

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New package surveyPrev with initial version 1.0.0
Package: surveyPrev
Title: Mapping the Prevalence of Binary Indicators using Survey Data in Small Areas
Version: 1.0.0
Description: Provides a pipeline to perform small area estimation and prevalence mapping of binary indicators using health and demographic survey data, described in Fuglstad et al. (2022) <doi:10.48550/arXiv.2110.09576> and Wakefield et al. (2020) <doi:10.1111/insr.12400>.
URL: https://github.com/richardli/surveyPrev
BugReports: https://github.com/richardli/surveyPrev/issues
Depends: R (>= 3.5)
License: GPL (>= 2)
Imports: survey, stats, ggplot2, rdhs, SUMMER, dplyr, labelled, sjlabelled, naniar, raster, sp, spdep, stringr, tidyverse, data.table, sf, matrixStats
Encoding: UTF-8
LazyData: true
Additional_repositories: https://inla.r-inla-download.org/R/testing/
Suggests: INLA, knitr, rmarkdown, R.rsp, kableExtra, geodata, patchwork, tidyr
VignetteBuilder: R.rsp, knitr
NeedsCompilation: no
Packaged: 2024-04-09 17:25:12 UTC; qianyu
Author: Qianyu Dong [cre, aut], Zehang R Li [aut], Yunhan Wu [aut], Andrea Boskovic [aut], Jon Wakefield [aut]
Maintainer: Qianyu Dong <qdong14@ucsc.edu>
Repository: CRAN
Date/Publication: 2024-04-10 19:50:02 UTC

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New package SmoothHazard with initial version 2024.04.10
Package: SmoothHazard
Title: Estimation of Smooth Hazard Models for Interval-Censored Data
Version: 2024.04.10
Description: Estimation of two-state (survival) models and irreversible illness- death models with possibly interval-censored, left-truncated and right-censored data. Proportional intensities regression models can be specified to allow for covariates effects separately for each transition. We use either a parametric approach with Weibull baseline intensities or a semi-parametric approach with M-splines approximation of baseline intensities in order to obtain smooth estimates of the hazard functions. Parameter estimates are obtained by maximum likelihood in the parametric approach and by penalized maximum likelihood in the semi-parametric approach.
Encoding: UTF-8
Copyright: See inst/COPYRIGHTS
Depends: R (>= 1.9.1), prodlim (>= 1.4.9)
Imports: lava (>= 1.4.1), mvtnorm (>= 1.0-3)
Maintainer: Thomas Alexander Gerds <tag@biostat.ku.dk>
License: GPL (>= 2)
Packaged: 2024-04-10 14:10:36 UTC; tag
Suggests: testthat (>= 3.0.0)
NeedsCompilation: yes
Author: Celia Touraine [aut], Thomas Alexander Gerds [aut, cre], Pierre Joly [aut] , Cecile Proust-Lima [aut] , Helene Jacqmin-Gadda [aut] , Amadou Diakite [aut] , W.D. Cody [aut] , A.H. Morris [aut] , B.W. Brown [aut] , Robin Genuer [ctb]
Repository: CRAN
Date/Publication: 2024-04-10 20:00:08 UTC

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Package lpcde updated to version 0.1.4 with previous version 0.1.3 dated 2024-03-28

Title: Boundary Adaptive Local Polynomial Conditional Density Estimator
Description: Tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.48550/arXiv.2204.10359>.
Author: Rajita Chandak [aut, cre]
Maintainer: Rajita Chandak <rchandak@princeton.edu>

Diff between lpcde versions 0.1.3 dated 2024-03-28 and 0.1.4 dated 2024-04-10

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New package IRR2FPR with initial version 0.1
Package: IRR2FPR
Title: Computing False Positive Rate from Inter-Rater Reliability
Version: 0.1
Maintainer: Frantisek Bartos <f.bartos96@gmail.com>
Description: Implements a 'Shiny Item Analysis' module and functions for computing false positive rate and other binary classification metrics from inter-rater reliability based on Bartoš & Martinková (2022) <doi:10.48550/arXiv.2207.09101>.
URL: https://github.com/FBartos/IRR2FPR
BugReports: https://github.com/FBartos/IRR2FPR/issues
License: GPL-3
Encoding: UTF-8
Imports: shiny, mvtnorm
NeedsCompilation: no
Packaged: 2024-04-10 08:42:13 UTC; fbart
Author: Frantisek Bartos [aut, cre]
Repository: CRAN
Date/Publication: 2024-04-10 19:50:09 UTC

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New package iNEXT.4steps with initial version 1.0.0
Package: iNEXT.4steps
Title: Four-Step Biodiversity Analysis Based on 'iNEXT'
Version: 1.0.0
Author: Anne Chao [aut, cre], KaiHsiang Hu [ctb]
Maintainer: Anne Chao <chao@stat.nthu.edu.tw>
URL: https://sites.google.com/view/chao-lab-website/software/inext-4steps/
Description: Expands 'iNEXT' to include the estimation of sample completeness and evenness. The package provides simple functions to perform the following four-step biodiversity analysis: STEP 1: Assessment of sample completeness profiles. STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to determine whether the asymptotic diversity can be accurately estimated. STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves. STEP 4: Assessment of evenness profiles. The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous 'iNEXT' package. Refer to the 'iNEXT' package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) <doi:10.1111/1440-1703.12102> for statistical background.
License: GPL (>= 3)
Depends: R (>= 4.0)
Imports: ggplot2, reshape2, dplyr, stats, ggpubr, purrr, iNEXT.3D
Suggests: testthat, knitr, rmarkdown
Encoding: UTF-8
BugReports: https://github.com/KaiHsiangHu/iNEXT.4steps/issues
LazyData: true
VignetteBuilder: knitr
ByteCompile: true
NeedsCompilation: no
Packaged: 2024-04-10 15:07:43 UTC; stat-pc
Repository: CRAN
Date/Publication: 2024-04-10 20:00:05 UTC

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New package FedIRT with initial version 0.1.0
Package: FedIRT
Title: Federated Item Response Theory Models
Version: 0.1.0
Description: Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly 'shiny' application is included. For more details, see Biying Zhou, Feng Ji (2024) "'FedIRT': An R package and 'shiny' app for estimating federated item response theory models" <https://github.com/Feng-Ji-Lab/FedIRT/blob/main/paper/paper.pdf>.
License: MIT + file LICENSE
Depends: R (>= 3.5.0)
Imports: purrr, pracma, shiny, httr, callr, DT, ggplot2, shinyjs,
Encoding: UTF-8
Suggests: testthat (>= 3.0.0)
LazyData: true
NeedsCompilation: no
Packaged: 2024-04-10 16:50:56 UTC; zby15
Author: Biying Zhou [cre], Feng Ji [aut]
Maintainer: Biying Zhou <zby.zhou@mail.utoronto.ca>
Repository: CRAN
Date/Publication: 2024-04-10 20:00:12 UTC

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Package adjustedCurves updated to version 0.11.1 with previous version 0.11.0 dated 2024-02-13

Title: Confounder-Adjusted Survival Curves and Cumulative Incidence Functions
Description: Estimate and plot confounder-adjusted survival curves using either 'Direct Adjustment', 'Direct Adjustment with Pseudo-Values', various forms of 'Inverse Probability of Treatment Weighting', two forms of 'Augmented Inverse Probability of Treatment Weighting', 'Empirical Likelihood Estimation' or 'Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times. Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) <doi:10.1002/sim.9681>.
Author: Robin Denz [aut, cre]
Maintainer: Robin Denz <robin.denz@rub.de>

Diff between adjustedCurves versions 0.11.0 dated 2024-02-13 and 0.11.1 dated 2024-04-10

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 adjustedCurves-0.11.1/adjustedCurves/NAMESPACE                                                                           |    2 
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 adjustedCurves-0.11.1/adjustedCurves/R/adjusted_curve_diff.r                                                             |   16 
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 adjustedCurves-0.11.1/adjustedCurves/R/adjusted_surv_quantile.r                                                          |  330 ++
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More information about adjustedCurves at CRAN
Permanent link

Package shinyMatrix updated to version 0.8.0 with previous version 0.6.0 dated 2021-05-20

Title: Shiny Matrix Input Field
Description: Implements a custom matrix input field.
Author: Andreas Neudecker
Maintainer: Andreas Neudecker <andreas.neudecker@inwt-statistics.de>

Diff between shinyMatrix versions 0.6.0 dated 2021-05-20 and 0.8.0 dated 2024-04-10

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 inst/www/matrix-input.js  |   99 ++++++++++++++++++++++++++++++++++++----------
 man/matrixInput.Rd        |   17 ++++++-
 10 files changed, 145 insertions(+), 43 deletions(-)

More information about shinyMatrix at CRAN
Permanent link

New package pctax with initial version 0.1.1
Package: pctax
Title: Professional Comprehensive Omics Data Analysis
Version: 0.1.1
Description: Provides a comprehensive suite of tools for analyzing omics data. It includes functionalities for alpha diversity analysis, beta diversity analysis, differential abundance analysis, community assembly analysis, visualization of phylogenetic tree, and functional enrichment analysis. With a progressive approach, the package offers a range of analysis methods to explore and understand the complex communities. It is designed to support researchers and practitioners in conducting in-depth and professional omics data analysis.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.1.0)
LazyData: true
Imports: pcutils (>= 0.2.5), dplyr, ggplot2 (>= 3.2.0), vegan, magrittr, grDevices, RColorBrewer, ggrepel, reshape2, stats, tibble, utils, ggpubr, ggnewscale, ade4, scales
Suggests: picante, ggpmisc, NST, permute, aplot, pheatmap, MASS, Rtsne, mixOmics, geosphere, phyloseq, phyloseqGraphTest, plotly, umap, Hmisc, minpack.lm, bbmle, snow, foreach, doSNOW, patchwork, tidytree, ggtree, ggtreeExtra, vctrs, zoo, ape, DESeq2, limma, ALDEx2, Mfuzz, edgeR, methods, randomForest, knitr, rmarkdown, MetaNet, jsonlite, prettydoc, clipr, zetadiv, ggforce, readxl
VignetteBuilder: knitr
BugReports: https://github.com/Asa12138/pctax/issues
URL: https://github.com/Asa12138/pctax
ByteCompile: true
NeedsCompilation: no
Packaged: 2024-04-06 15:16:13 UTC; asa
Author: Chen Peng [aut, cre]
Maintainer: Chen Peng <pengchen2001@zju.edu.cn>
Repository: CRAN
Date/Publication: 2024-04-10 17:10:05 UTC

More information about pctax at CRAN
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Package mlr3filters updated to version 0.8.0 with previous version 0.7.1 dated 2023-02-15

Title: Filter Based Feature Selection for 'mlr3'
Description: Extends 'mlr3' with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported.
Author: Patrick Schratz [aut] , Michel Lang [cre, aut] , Bernd Bischl [aut] , Martin Binder [aut], John Zobolas [aut]
Maintainer: Michel Lang <michellang@gmail.com>

Diff between mlr3filters versions 0.7.1 dated 2023-02-15 and 0.8.0 dated 2024-04-10

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Package ipsRdbs updated to version 1.0.0 with previous version 0.2.6 dated 2023-07-05

Title: Introduction to Probability, Statistics and R for Data-Based Sciences
Description: Contains data sets, programmes and illustrations discussed in the book, "Introduction to Probability, Statistics and R: Foundations for Data-Based Sciences." Sahu (2024, isbn:9783031378645) describes the methods in detail.
Author: Sujit K. Sahu [aut, cre]
Maintainer: Sujit K. Sahu <S.K.Sahu@soton.ac.uk>

Diff between ipsRdbs versions 0.2.6 dated 2023-07-05 and 1.0.0 dated 2024-04-10

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Package ichimoku updated to version 1.5.1 with previous version 1.5.0 dated 2024-02-05

Title: Visualization and Tools for Ichimoku Kinko Hyo Strategies
Description: An implementation of 'Ichimoku Kinko Hyo', also commonly known as 'cloud charts'. Static and interactive visualizations with tools for creating, backtesting and development of quantitative 'ichimoku' strategies. As described in Sasaki (1996, ISBN:4925152009), the technique is a refinement on candlestick charting, originating from Japan and now in widespread use in technical analysis worldwide. Translating as 'one-glance equilibrium chart', it allows the price action and market structure of financial securities to be determined 'at-a-glance'. Incorporates an interface with the OANDA fxTrade API <https://developer.oanda.com/> for retrieving historical and live streaming price data for major currencies, metals, commodities, government bonds and stock indices.
Author: Charlie Gao [aut, cre] , Hibiki AI Limited [cph]
Maintainer: Charlie Gao <charlie.gao@shikokuchuo.net>

Diff between ichimoku versions 1.5.0 dated 2024-02-05 and 1.5.1 dated 2024-04-10

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New package gratis with initial version 1.0.7
Package: gratis
Title: Generating Time Series with Diverse and Controllable Characteristics
Version: 1.0.7
Description: Generates synthetic time series based on various univariate time series models including MAR and ARIMA processes. Kang, Y., Hyndman, R.J., Li, F.(2020) <doi:10.1002/sam.11461>.
License: GPL-3
URL: https://github.com/ykang/gratis
BugReports: https://github.com/ykang/gratis/issues/
Depends: R (>= 3.5.0)
Imports: doRNG, dplyr, feasts, fGarch, foreach, forecast (>= 8.16), GA, generics, magrittr, methods, mvtnorm, polynom, purrr, shiny, stats, tibble, tsfeatures, tsibble, utils
Suggests: knitr, rlang, rmarkdown, shinydashboard
VignetteBuilder: knitr
Encoding: UTF-8
LazyLoad: yes
NeedsCompilation: no
Repository: CRAN
Packaged: 2024-04-08 01:24:01 UTC; fli
Author: Yanfei Kang [aut] , Feng Li [aut, cre] , Rob Hyndman [aut] , Mitchell O'Hara-Wild [ctb] , Bocong Zhao [ctb]
Maintainer: Feng Li <feng.li@cufe.edu.cn>
Date/Publication: 2024-04-10 17:10:20 UTC

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New package BOSO with initial version 1.0.4
Package: BOSO
Title: Bilevel Optimization Selector Operator
Version: 1.0.4
Date: 2024-04-08
Description: A novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). The main contribution is the use a bilevel optimization problem to select the variables in the training problem that minimize the error in the validation set. Preprint available: [Valcarcel, L. V., San Jose-Eneriz, E., Cendoya, X., Rubio, A., Agirre, X., Prosper, F., & Planes, F. J. (2020). "BOSO: a novel feature selection algorithm for linear regression with high-dimensional data." bioRxiv. <doi:10.1101/2020.11.18.388579>]. In order to run the vignette, it is recommended to install the 'bestsubset' package, using the following command: devtools::install_github(repo="ryantibs/best-subset", subdir="bestsubset"). If you do not have gurobi, run devtools::install_github(repo="lvalcarcel/best-subset", subdir="bestsubset"). Moreover, to install cplexAPI you can check <https://github.com/lvalcarcel/cplexAPI>.
SystemRequirements: IBM ILOG CPLEX (>= 12.1)
Depends: R (>= 4.0)
Imports: Matrix, MASS, methods
Suggests: cplexAPI, testthat, glmnet, knitr, rmarkdown, ggplot2, ggpubr, dplyr, kableExtra, devtools, BiocStyle, bestsubset
License: GPL-3
URL: https://github.com/lvalcarcel/BOSO
BugReports: https://github.com/lvalcarcel/BOSO/issues
LazyData: true
Encoding: UTF-8
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-04-10 11:28:18 UTC; lvalcarcel
Author: Luis V. Valcarcel [aut, cre, ctb] , Edurne San Jose-Eneriz [aut] , Xabier Cendoya [aut, ctb] , Angel Rubio [aut, ctb] , Xabier Agirre [aut] , Felipe Prosper [aut] , Francisco J. Planes [aut, ctb]
Maintainer: Luis V. Valcarcel <lvalcarcel@alumni.unav.es>
Repository: CRAN
Date/Publication: 2024-04-10 17:10:23 UTC

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Package WH updated to version 1.1.1 with previous version 1.1.0 dated 2023-09-18

Title: Enhanced Implementation of Whittaker-Henderson Smoothing
Description: An enhanced implementation of Whittaker-Henderson smoothing for the gradation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2023) <doi:10.48550/arXiv.2306.06932>. Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.
Author: Guillaume Biessy [aut, cre, cph]
Maintainer: Guillaume Biessy <guillaume.biessy78@gmail.com>

Diff between WH versions 1.1.0 dated 2023-09-18 and 1.1.1 dated 2024-04-10

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New package sparseCov with initial version 0.0.1
Package: sparseCov
Title: Sparse Covariance Estimation Based on Thresholding
Version: 0.0.1
Description: A sparse covariance estimator based on different thresholding operators.
License: GPL (>= 2)
Encoding: UTF-8
Imports: Matrix, methods, mvnfast, Rfast, sparseMVN, stats
URL: https://github.com/chexjiang/sparseCov
NeedsCompilation: no
Packaged: 2024-04-10 11:43:58 UTC; chenxinjiang
Author: Chenxin Jiang [aut, cre]
Maintainer: Chenxin Jiang <cflorajiang@g.ucla.edu>
Repository: CRAN
Date/Publication: 2024-04-10 16:50:02 UTC

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Package precommit updated to version 0.4.2 with previous version 0.4.1 dated 2024-03-31

Title: Pre-Commit Hooks
Description: Useful git hooks for R building on top of the multi-language framework 'pre-commit' for hook management. This package provides git hooks for common tasks like formatting files with 'styler' or spell checking as well as wrapper functions to access the 'pre-commit' executable.
Author: Lorenz Walthert
Maintainer: Lorenz Walthert <lorenz.walthert@icloud.com>

Diff between precommit versions 0.4.1 dated 2024-03-31 and 0.4.2 dated 2024-04-10

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New package ntranova with initial version 0.0.1
Package: ntranova
Title: Two Way Neutrosophic ANOVA
Version: 0.0.1
Description: Dealing with neutrosophic data of the form N=D+I(where N is a Neutrosophic number ,D is the determinant part of the number and I is the indeterminacy part) using the neutrosophic two way anova test keeps the type I error low. This algorithm calculates the fisher statistics when we have a neutrosophic data, also tests two hypothesizes, first is to test differences between treatments, and second is to test differences between sectors. For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022) <https://www.americaspg.com/articleinfo/21/show/1058>.
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-04-10 13:06:59 UTC; HP
Author: Mohamad Taher Anan [aut, cre] , Mohamad Bisher Zeina [aut], Shaza Zubeadah [aut], Mahmoud Miari [aut]
Maintainer: Mohamad Taher Anan <mtanan200988@gmail.com>
Repository: CRAN
Date/Publication: 2024-04-10 17:00:02 UTC

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Package mlr3misc updated to version 0.15.0 with previous version 0.14.0 dated 2024-02-01

Title: Helper Functions for 'mlr3'
Description: Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'.
Author: Michel Lang [cre, aut] , Patrick Schratz [aut]
Maintainer: Michel Lang <michellang@gmail.com>

Diff between mlr3misc versions 0.14.0 dated 2024-02-01 and 0.15.0 dated 2024-04-10

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New package quadform with initial version 0.0-1
Package: quadform
Title: Efficient Evaluation of Quadratic Forms
Version: 0.0-1
Depends: R (>= 3.0.1)
Imports: mathjaxr
Suggests: testthat
Maintainer: Robin K. S. Hankin <hankin.robin@gmail.com>
Description: A range of quadratic forms are evaluated, using efficient methods. Unnecessary transposes are not performed. Complex values are handled consistently.
License: GPL
URL: https://github.com/RobinHankin/quadform
BugReports: https://github.com/RobinHankin/quadform/issues
NeedsCompilation: no
Packaged: 2024-04-09 18:16:01 UTC; rhankin
Author: Robin K. S. Hankin [aut, cre]
Repository: CRAN
Date/Publication: 2024-04-10 16:00:02 UTC

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Package rtpcr updated to version 1.0.2 with previous version 1.0.1 dated 2024-04-05

Title: qPCR Data Analysis
Description: Various methods are employed for statistical analysis and graphical presentation of real-time PCR (quantitative PCR or qPCR) data. 'rtpcr' handles amplification efficiency calculation, statistical analysis and graphical representation of real-time PCR data based on up to two reference genes. By accounting for amplification efficiency values, 'rtpcr' was developed using a general calculation method described by Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5>, covering both the Livak and Pfaffl methods. Based on the experimental conditions, the functions of the 'rtpcr' package use t-test (for experiments with a two-level factor) or analysis of variance (for cases where more than two levels or factors exist) to calculate the fold change or relative expression. The functions also provide standard deviations and confidence limits for means and apply statistical mean comparisons. To facilitate using 'rtpcr', different datasets have been employed in the examples and the outputs ar [...truncated...]
Author: Ghader Mirzaghaderi [aut, cre, cph]
Maintainer: Ghader Mirzaghaderi <gh.mirzaghaderi@uok.ac.ir>

Diff between rtpcr versions 1.0.1 dated 2024-04-05 and 1.0.2 dated 2024-04-10

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Package CausalQueries updated to version 1.1.0 with previous version 1.0.2 dated 2024-01-15

Title: Make, Update, and Query Binary Causal Models
Description: Users can declare binary causal models, update beliefs about causal types given data and calculate arbitrary estimands. Model definition makes use of 'dagitty' functionality. Updating is implemented in 'stan'. The approach used in 'CausalQueries' is a generalization of the 'biqq' models described in "Mixing Methods: A Bayesian Approach" (Humphreys and Jacobs, 2015, <DOI:10.1017/S0003055415000453>). The conceptual extension makes use of work on probabilistic causal models described in Pearl's Causality (Pearl, 2009, <DOI:10.1017/CBO9780511803161>).
Author: Clara Bicalho [ctb], Jasper Cooper [ctb], Macartan Humphreys [aut] , Till Tietz [aut, cre] , Alan Jacobs [aut], Merlin Heidemanns [ctb], Lily Medina [aut] , Julio Solis [ctb], Georgiy Syunyaev [aut]
Maintainer: Till Tietz <ttietz2014@gmail.com>

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Package yyjsonr updated to version 0.1.20 with previous version 0.1.18 dated 2024-01-22

Title: Fast 'JSON', 'NDJSON' and 'GeoJSON' Parser and Generator
Description: A fast 'JSON' parser, generator and validator which converts 'JSON', 'NDJSON' (Newline Delimited 'JSON') and 'GeoJSON' (Geographic 'JSON') data to/from R objects. The standard R data types are supported (e.g. logical, numeric, integer) with configurable handling of NULL and NA values. Data frames, atomic vectors and lists are all supported as data containers translated to/from 'JSON'. 'GeoJSON' data is read in as 'simple features' objects. This implementation wraps the 'yyjson' 'C' library which is available from <https://github.com/ibireme/yyjson>.
Author: Mike Cheng [aut, cre, cph], Yao Yuan [aut, cph]
Maintainer: Mike Cheng <mikefc@coolbutuseless.com>

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Package tidytext updated to version 0.4.2 with previous version 0.4.1 dated 2023-01-07

Title: Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
Description: Using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use. Much of the infrastructure needed for text mining with tidy data frames already exists in packages like 'dplyr', 'broom', 'tidyr', and 'ggplot2'. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages.
Author: Gabriela De Queiroz [ctb], Colin Fay [ctb] , Emil Hvitfeldt [ctb], Os Keyes [ctb] , Kanishka Misra [ctb], Tim Mastny [ctb], Jeff Erickson [ctb], David Robinson [aut], Julia Silge [aut, cre]
Maintainer: Julia Silge <julia.silge@gmail.com>

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Package LDlinkR updated to version 1.4.0 with previous version 1.3.0 dated 2023-05-31

Title: Calculating Linkage Disequilibrium (LD) in Human Population Groups of Interest
Description: Provides access to the 'LDlink' API (<https://ldlink.nih.gov/?tab=apiaccess>) using the R console. This programmatic access facilitates researchers who are interested in performing batch queries in 1000 Genomes Project (2015) <doi:10.1038/nature15393> data using 'LDlink'. 'LDlink' is an interactive and powerful suite of web-based tools for querying germline variants in human population groups of interest. For more details, please see Machiela et al. (2015) <doi:10.1093/bioinformatics/btv402>.
Author: Timothy A. Myers [aut, cre] , Stephen J. Chanock [aut], Mitchell J. Machiela [aut]
Maintainer: Timothy A. Myers <myersta@mail.nih.gov>

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Package DTComPair updated to version 1.2.4 with previous version 1.2.2 dated 2023-08-20

Title: Comparison of Binary Diagnostic Tests in a Paired Study Design
Description: Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.
Author: Christian Stock [aut, cre] , Thomas Hielscher [aut], Andrea Discacciati [aut]
Maintainer: Christian Stock <christian.stock@boehringer-ingelheim.com>

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Package pkgdown updated to version 2.0.8 with previous version 2.0.7 dated 2022-12-14

Title: Make Static HTML Documentation for a Package
Description: Generate an attractive and useful website from a source package. 'pkgdown' converts your documentation, vignettes, 'README', and more to 'HTML' making it easy to share information about your package online.
Author: Hadley Wickham [aut, cre] , Jay Hesselberth [aut] , Maelle Salmon [aut] , Posit Software, PBC [cph, fnd]
Maintainer: Hadley Wickham <hadley@posit.co>

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Package dupNodes updated to version 0.3.0 with previous version 0.2.0 dated 2024-03-17

Title: Computes DNSLbetweenness, a Betweenness Measure that Includes Self-Loops
Description: Computes a new measure, DNSL betweenness, via the creation of a new graph from an existing one, duplicating nodes with self-loops. This betweenness centrality does not drop this essential information. Implements Merelo & Molinari (2024) <doi:10.1007/s42001-023-00245-4>.
Author: Juan Julian Merelo-Guervos
Maintainer: Juan Julian Merelo-Guervos <jjmerelo@gmail.com>

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New package casebase with initial version 0.10.5
Package: casebase
Title: Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression
Version: 0.10.5
Date: 2024-04-09
Description: Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.
Depends: R (>= 3.5.0)
Imports: data.table, ggplot2 (>= 3.4.0), methods, mgcv, stats, survival, VGAM
License: MIT + file LICENSE
LazyData: TRUE
Suggests: colorspace, covr, dplyr, eha, glmnet, knitr, lubridate, progress, rmarkdown, splines, testthat (>= 3.0.0), visreg
VignetteBuilder: knitr
URL: https://sahirbhatnagar.com/casebase/
BugReports: https://github.com/sahirbhatnagar/casebase/issues
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-04-09 21:23:07 UTC; maxturgeon
Author: Sahir Bhatnagar [aut, cre] , Maxime Turgeon [aut] , Jesse Islam [aut] , Olli Saarela [aut] , James Hanley [aut]
Maintainer: Sahir Bhatnagar <sahir.bhatnagar@gmail.com>
Repository: CRAN
Date/Publication: 2024-04-10 12:00:06 UTC

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Package pedgene updated to version 3.9 with previous version 3.8 dated 2024-03-05

Title: Gene-Level Variant Association Tests for Pedigree Data
Description: Gene-level variant association tests with disease status for pedigree data: kernel and burden association statistics.
Author: Schaid Daniel [aut], Visconti Alessia [ctb], Jason Sinnwell [aut, cre]
Maintainer: Jason Sinnwell <sinnwell.jason@mayo.edu>

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Package dateback updated to version 1.0.4 with previous version 1.0.3 dated 2023-09-03

Title: Collect and Install R Packages on a Specified Date with Dependencies
Description: Works as a virtual CRAN snapshot for source packages. It automatically downloads and installs 'tar.gz' files with dependencies, all of which were available on a specific day.
Author: Ryota Suzuki <suzuki@ef-prime.com>
Maintainer: Ryota Suzuki <suzuki@ef-prime.com>

Diff between dateback versions 1.0.3 dated 2023-09-03 and 1.0.4 dated 2024-04-10

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Package bspm updated to version 0.5.7 with previous version 0.5.6 dated 2024-04-09

Title: Bridge to System Package Manager
Description: Enables binary package installations on Linux distributions. Provides functions to manage packages via the distribution's package manager. Also provides transparent integration with R's install.packages() and a fallback mechanism. When installed as a system package, interacts with the system's package manager without requiring administrative privileges via an integrated D-Bus service; otherwise, uses sudo. Currently, the following backends are supported: DNF, APT, ALPM.
Author: Inaki Ucar [aut, cph, cre]
Maintainer: Inaki Ucar <iucar@fedoraproject.org>

Diff between bspm versions 0.5.6 dated 2024-04-09 and 0.5.7 dated 2024-04-10

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