Mon, 14 Sep 2020

Package threshr updated to version 1.0.3 with previous version 1.0.2 dated 2020-09-05

Title: Threshold Selection and Uncertainty for Extreme Value Analysis
Description: Provides functions for the selection of thresholds for use in extreme value models, based mainly on the methodology in Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>. It also performs predictive inferences about future extreme values, based either on a single threshold or on a weighted average of inferences from multiple thresholds, using the 'revdbayes' package <https://cran.r-project.org/package=revdbayes>. At the moment only the case where the data can be treated as independent identically distributed observations is considered.
Author: Paul J. Northrop [aut, cre, cph], Nicolas Attalides [aut]
Maintainer: Paul J. Northrop <p.northrop@ucl.ac.uk>

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Package rxylib updated to version 0.2.6 with previous version 0.2.4 dated 2019-06-05

Title: Import XY-Data into R
Description: Provides access to the 'xylib' C library for to import xy data from powder diffraction, spectroscopy and other experimental methods.
Author: Sebastian Kreutzer [aut, trl, cre] (<https://orcid.org/0000-0002-0734-2199>), Johannes Friedrich [aut] (<https://orcid.org/0000-0002-0805-9547>), RLum Team [ctb], Marcin Wojdyr [cph] (C++ library 'xylib'), Peng Zhang [cph] (C++ library 'xylib')
Maintainer: Sebastian Kreutzer <sebastian.kreutzer@aber.ac.uk>

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Package plumber updated to version 1.0.0 with previous version 0.4.6 dated 2018-06-05

Title: An API Generator for R
Description: Gives the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions.
Author: Barret Schloerke [cre, aut], Jeff Allen [aut, ccp], Bruno Tremblay [ctb], Frans van Dunné [ctb], Sebastiaan Vandewoude [ctb], RStudio [cph, fnd]
Maintainer: Barret Schloerke <barret@rstudio.com>

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Package PhysActBedRest updated to version 1.1 with previous version 1.0 dated 2016-04-06

Title: Marks Periods of 'Bedrest' in Actigraph Accelerometer Data
Description: Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active. The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively. The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package. Version 1.1 adds preschool thresholds and corrects for possible errors in algorithm implementation.
Author: J. Dustin Tracy, Zhiyi Xu, Sari Acra, Kong Y. Chen, Maciej S. Buchowski, Thomas Donnelly
Maintainer: J. Dustin Tracy <tracy@chapman.edu>

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Package pense updated to version 2.0.1 with previous version 1.2.9 dated 2020-02-09

Title: Penalized Elastic Net S/MM-Estimator of Regression
Description: Robust penalized (adaptive) elastic net S and M estimators for linear regression. The methods are proposed in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) <https://projecteuclid.org/euclid.aoas/1574910036>. The package implements the extensions and algorithms described in Kepplinger, D. (2020) <doi:10.14288/1.0392915>.
Author: David Kepplinger [aut, cre], Matías Salibián-Barrera [aut], Gabriela Cohen Freue [aut]
Maintainer: David Kepplinger <david.kepplinger@gmail.com>

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Package LSX updated to version 0.9.1 with previous version 0.9.0 dated 2020-09-09

Title: Latent Semantic Scaling
Description: A word embeddings-based semisupervised model for document scaling Watanabe (2017) <doi:10.1177/0267323117695735>. LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove).
Author: Kohei Watanabe [aut, cre, cph]
Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com>

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Package ggrastr updated to version 0.2.1 with previous version 0.2.0 dated 2020-09-12

Title: Raster Layers for 'ggplot2'
Description: Provides a set of geoms to rasterize only specific layers of the plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without loosing vector properties of the scale-sensitive information.
Author: Viktor Petukhov [aut, cph], Teun van den Brand [aut], Evan Biederstedt [cre, aut]
Maintainer: Evan Biederstedt <evan.biederstedt@gmail.com>

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Package dmdScheme updated to version 1.2.4 with previous version 1.2 dated 2020-06-12

Title: Domain Specific MetaData Scheme
Description: Forms the core for developing own domain specific metadata schemes. It contains the basic functionality needed for all metadata schemes based on the 'dmdScheme'. See R.M. Krug and O.L. Petchey (2019) <DOI:10.5281/zenodo.3581970>.
Author: Rainer M. Krug [aut, cre] (<https://orcid.org/0000-0002-7490-0066>), Owen L. Petchey [ctb] (<https://orcid.org/0000-0002-7724-1633>)
Maintainer: Rainer M. Krug <Rainer.Krug@uzh.ch>

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Package colourpicker updated to version 1.1.0 with previous version 1.0 dated 2017-09-27

Title: A Colour Picker Tool for Shiny and for Selecting Colours in Plots
Description: A colour picker that can be used as an input in 'Shiny' apps or Rmarkdown documents. The colour picker supports alpha opacity, custom colour palettes, and many more options. A Plot Colour Helper tool is available as an 'RStudio' Addin, which helps you pick colours to use in your plots. A more generic Colour Picker 'RStudio' Addin is also provided to let you select colours to use in your R code.
Author: Dean Attali [aut, cre], David Griswold [ctb]
Maintainer: Dean Attali <daattali@gmail.com>

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Package broomExtra updated to version 4.0.6 with previous version 4.0.5 dated 2020-09-02

Title: Enhancements for 'broom' and 'easystats' Package Families
Description: Provides helper functions that assist in data analysis workflows involving regression analyses. The goal is to combine the functionality offered by different set of packages ('broom', 'broom.mixed', 'parameters', and 'performance') through a common syntax to return tidy dataframes containing model parameters and performance measure summaries. The 'grouped_' variants of the generics provides a convenient way to execute functions across a combination of grouping variable(s) in a dataframe.
Author: Indrajeet Patil [aut, cre, cph] (<https://orcid.org/0000-0003-1995-6531>)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package BGVAR updated to version 2.1.2 with previous version 2.1.0 dated 2020-09-07

Title: Bayesian Global Vector Autoregressions
Description: Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available.
Author: Maximilian Boeck [aut, cre] (<https://orcid.org/0000-0001-6024-8305>), Martin Feldkircher [aut] (<https://orcid.org/0000-0002-5511-9215>), Florian Huber [aut] (<https://orcid.org/0000-0002-2896-7921>), Christopher Sims [ctb]
Maintainer: Maximilian Boeck <maximilian.boeck@wu.ac.at>

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Package BayesianFROC updated to version 0.3.1 with previous version 0.3.0 dated 2020-07-02

Title: FROC Analysis by Bayesian Approaches
Description: Execute BayesianFROC::fit_GUI_Shiny() (or fit_GUI_Shiny_MRMC()) for a graphical user interface via Shiny. Provides new methods for the so-called Free-response Receiver Operating Characteristic (FROC) analysis. The ultimate aim of FROC analysis is to compare observer performances, which means comparing characteristics, such as area under the curve (AUC) or figure of merit (FOM). In this package, we only use the notion of AUC for modality comparison, where by "modality", we mean imaging methods such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), ..., etc. So there is a problem that which imaging method is better to detect lesions from shadows in radiographs. To solve modality comparison issues, this package provides new methods using hierarchical Bayesian models proposed by the author of this package. Using this package, one can obtain at least one conclusion that which imaging methods are better for finding lesions in radiographs with the case of your data. Fitting FROC statistical models is sometimes not so good, it can easily confirm by drawing FROC curves and comparing these curves and the points constructed by False Positive fractions (FPFs) and True Positive Fractions (TPFs), we can validate the goodness of fit intuitively. Such validation is also implemented by the Chi square goodness of fit statistics in the Bayesian context which means that the parameter is not deterministic, thus by integrating it with the posterior predictive measure, we get a desired value. To compare modalities (imaging methods: MRI, CT, PET, ... , etc), we evaluate AUCs for each modality. FROC is developed by Dev Chakraborty, his FROC model in his 1989 paper relies on the maximal likelihood methodology. The author modified and provided the alternative Bayesian FROC model. Strictly speaking, his model does not coincide with models in this package. In FROC context, we means by multiple reader and multiple case (MRMC) the case of the number of reader or modality is two or more. The MRMC data is available for functions of this package. I hope that medical researchers use not only the frequentist method but also alternative Bayesian methods. In medical research, many problems are considered under only frequentist methods, such as the notion of p-values. But p-value is sometimes misunderstood. Bayesian methods provide very simple, direct, intuitive answer for research questions. Combining frequentist methods with Bayesian methods, we can obtain more reliable answer for research questions. Please execute the following R scripts from the R (R studio) console, demo(demo_MRMC, package = "BayesianFROC"); demo(demo_srsc, package = "BayesianFROC"); demo(demo_stan, package = "BayesianFROC"); demo(demo_drawcurves_srsc, package = "BayesianFROC"); demo_Bayesian_FROC(); demo_Bayesian_FROC_without_pause(). References: Dev Chakraborty (1989) <doi:10.1118/1.596358> Maximum likelihood analysis of free - response receiver operating characteristic (FROC) data. Pre-print: Issei Tsunoda; Bayesian Models for free-response receiver operating characteristic analysis.
Author: Issei Tsunoda [aut, cre]
Maintainer: Issei Tsunoda <tsunoda.issei1111@gmail.com>

Diff between BayesianFROC versions 0.3.0 dated 2020-07-02 and 0.3.1 dated 2020-09-14

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 BayesianFROC-0.3.1/BayesianFROC/MD5                                                                             |  301 
 BayesianFROC-0.3.1/BayesianFROC/NAMESPACE                                                                       |   15 
 BayesianFROC-0.3.1/BayesianFROC/NEWS.md                                                                         |  150 
 BayesianFROC-0.3.1/BayesianFROC/R/AFROC.R                                                                       |    8 
 BayesianFROC-0.3.1/BayesianFROC/R/BayesianFROC.R                                                                |    9 
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 BayesianFROC-0.3.1/BayesianFROC/R/check_rhat.R                                                                  |   15 
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 BayesianFROC-0.3.1/BayesianFROC/R/document_dataset_srsc.R                                                       |   16 
 BayesianFROC-0.3.1/BayesianFROC/R/download.R                                                                    |   22 
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 BayesianFROC-0.3.1/BayesianFROC/R/fit_GUI_dashboard.R                                                           |    2 
 BayesianFROC-0.3.1/BayesianFROC/R/fit_MRMC_versionTWO.R                                                         |   16 
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 BayesianFROC-0.3.1/BayesianFROC/R/install_imports.R                                                             |   23 
 BayesianFROC-0.3.1/BayesianFROC/R/metadata.R                                                                    |   21 
 BayesianFROC-0.3.1/BayesianFROC/R/method.R                                                                      |    2 
 BayesianFROC-0.3.1/BayesianFROC/R/methods_print.R                                                               |    1 
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 BayesianFROC-0.3.1/BayesianFROC/R/ppp.R                                                                         |  169 
 BayesianFROC-0.3.1/BayesianFROC/R/prior_print.R                                                                 |   22 
 BayesianFROC-0.3.1/BayesianFROC/R/sbcVer2.R                                                                     |   74 
 BayesianFROC-0.3.1/BayesianFROC/R/size_of_return_value.R                                                        |    4 
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 BayesianFROC-0.3.1/BayesianFROC/R/viewdata.R                                                                    |   39 
 BayesianFROC-0.3.1/BayesianFROC/R/waic.R                                                                        |   14 
 BayesianFROC-0.3.1/BayesianFROC/build/vignette.rds                                                              |binary
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 BayesianFROC-0.3.1/BayesianFROC/man/Author_vs_classic_for_AUC.Rd                                                |    2 
 BayesianFROC-0.3.1/BayesianFROC/man/BayesianFROC.Rd                                                             |    9 
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 BayesianFROC-0.3.1/BayesianFROC/man/DrawCurves.Rd                                                               |    7 
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 BayesianFROC-0.3.1/BayesianFROC/man/DrawCurves_MRMC_pairwise.Rd                                                 |    8 
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 BayesianFROC-0.3.1/BayesianFROC/man/DrawCurves_srsc.Rd                                                          |    5 
 BayesianFROC-0.3.1/BayesianFROC/man/Draw_AUC.Rd                                                                 |    4 
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 BayesianFROC-0.3.1/BayesianFROC/man/data.nonconverge.srsc.Rd                                                    |    2 
 BayesianFROC-0.3.1/BayesianFROC/man/dataList.Chakra.1.Rd                                                        |    2 
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 BayesianFROC-0.3.1/BayesianFROC/man/dataList.Chakra.4.Rd                                                        |    2 
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 BayesianFROC-0.3.1/BayesianFROC/man/demo_Bayesian_FROC.Rd                                                       |    2 
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 BayesianFROC-0.3.1/BayesianFROC/man/error_MRMC.Rd                                                               |    2 
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 BayesianFROC-0.3.1/BayesianFROC/man/extractAUC.Rd                                                               |    2 
 BayesianFROC-0.3.1/BayesianFROC/man/extract_EAP_by_array.Rd                                                     |    2 
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 BayesianFROC-0.3.1/BayesianFROC/man/fit_GUI_Shiny_MRMC.Rd                                                       |    3 
 BayesianFROC-0.3.1/BayesianFROC/man/fit_GUI_dashboard.Rd                                                        |    6 
 BayesianFROC-0.3.1/BayesianFROC/man/fit_MRMC.Rd                                                                 |   30 
 BayesianFROC-0.3.1/BayesianFROC/man/fit_MRMC_versionTWO.Rd                                                      |   13 
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 BayesianFROC-0.3.1/BayesianFROC/man/fit_a_model_to.Rd                                                           |    2 
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 BayesianFROC-0.3.1/BayesianFROC/man/give_name_srsc_data.Rd                                                      |    4 
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 BayesianFROC-0.3.1/BayesianFROC/man/make_true_parameter_MRMC.Rd                                                 |    2 
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 BayesianFROC-0.3.1/BayesianFROC/man/sortAUC.Rd                                                                  |    2 
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Package patrick updated to version 0.0.2 with previous version 0.0.1 dated 2018-08-13

Title: Parameterized Unit Testing
Description: This is an extension of the 'testthat' package that lets you add parameters to your unit tests. Parameterized unit tests are often easier to read and more reliable, since they follow the DNRY (do not repeat yourself) rule.
Author: Michael Quinn <msquinn@google.com>
Maintainer: Michael Quinn <msquinn@google.com>

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Package packagefinder updated to version 0.3.0 with previous version 0.2.1 dated 2020-03-31

Title: Comfortable Search for R Packages on CRAN, Either Directly from the R Console or with an R Studio Add-in
Description: Search for R packages on CRAN directly from the R console, based on the packages' titles, short and long descriptions, or other fields. Combine multiple keywords with logical operators ('and', 'or'), view detailed information on any package and keep track of the latest package contributions to CRAN. If you don't want to search from the R console, use the comfortable R Studio add-in.
Author: Joachim Zuckarelli [aut, cre] (<https://orcid.org/0000-0002-9280-3016>)
Maintainer: Joachim Zuckarelli <joachim@zuckarelli.de>

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Package pcnetmeta updated to version 2.7 with previous version 2.6 dated 2017-11-07

Title: Patient-Centered Network Meta-Analysis
Description: Performs Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes (Zhang et al., 2014 <doi:10.1177/1740774513498322>; Lin et al., 2017 <doi:10.18637/jss.v080.i05>).
Author: Lifeng Lin [aut, cre] (<https://orcid.org/0000-0002-3562-9816>), Jing Zhang [aut], Haitao Chu [aut] (<https://orcid.org/0000-0003-0932-598X>)
Maintainer: Lifeng Lin <linl@stat.fsu.edu>

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Package cbsodataR updated to version 0.5.1 with previous version 0.4.1 dated 2020-02-20

Title: Statistics Netherlands (CBS) Open Data API Client
Description: The data and meta data from Statistics Netherlands (<https://www.cbs.nl>) can be browsed and downloaded. The client uses the open data API of Statistics Netherlands.
Author: Edwin de Jonge [aut, cre], Sara Houweling [ctb]
Maintainer: Edwin de Jonge <edwindjonge@gmail.com>

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Package gausscov updated to version 0.0.7 with previous version 0.0.6 dated 2020-09-05

Title: The Gaussian Covariate Method for Variable Selection
Description: Given the standard linear model the traditional way of deciding whether to include the jth covariate is to apply the F-test to decide whether the corresponding beta coefficient is zero. The Gaussian covariate method is completely different. The question as to whether the beta coefficient is or is not zero is replaced by the question as to whether the covariate is better or worse than i.i.d. Gaussian noise. The P-value for the covariate is the probability that Gaussian noise is better. Surprisingly this can be given exactly and it is the same a the P-value for the classical model based on the F-distribution. The Gaussian covariate P-value is model free, it is the same for any data set. Using the idea it is possible to do covariate selection for a small number of covariates 25 by considering all subsets. Post selection inference causes no problems as the P-values hold whatever the data. The idea extends to stepwise regression again with exact probabilities. In the simplest version the only parameter is a specified cut-off P-value which can be interpreted as the probability of a false positive being included in the final selection. For more information see the website below and the accompanying papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", 2020, <arXiv:1906.01990>. L. Davies, "Lasso, Knockoff and Gaussian covariates: A comparison", 2018, <arXiv:1807.09633>.
Author: Laurie Davies [aut, cre]
Maintainer: Laurie Davies <laurie.davies@uni-due.de>

Diff between gausscov versions 0.0.6 dated 2020-09-05 and 0.0.7 dated 2020-09-14

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Package calendR updated to version 1.1 with previous version 1.0 dated 2020-08-31

Title: Ready to Print Monthly and Yearly Calendars Made with 'ggplot2'
Description: Contains the function calendR() for creating fully customizable monthly and yearly calendars (colors, fonts, formats, ...) and even heatmap calendars. In addition, it allows saving the calendars in ready to print A4 format PDF files.
Author: José Carlos Soage González [aut, cre], Natalia Pérez Veiga [aut]
Maintainer: José Carlos Soage González <jsoage@uvigo.es>

Diff between calendR versions 1.0 dated 2020-08-31 and 1.1 dated 2020-09-14

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New package ROI.plugin.osqp with initial version 1.0-0
Package: ROI.plugin.osqp
Version: 1.0-0
Title: 'osqp' Plugin for the 'R' Optimization Infrastructure
Authors@R: person("Florian", "Schwendinger", role = c("aut", "cre"), email = "FlorianSchwendinger@gmx.at")
Description: Enhances the 'R' Optimization Infrastructure ('ROI') package with the quadratic solver 'OSQP'. More information about 'OSQP' can be found at <https://osqp.org>.
Imports: methods, slam, ROI (>= 0.2-5), osqp, Matrix
License: GPL-3
URL: http://roi.r-forge.r-project.org/, https://r-forge.r-project.org/projects/roi/
NeedsCompilation: no
Packaged: 2020-09-05 12:12:08 UTC; f
Author: Florian Schwendinger [aut, cre]
Maintainer: Florian Schwendinger <FlorianSchwendinger@gmx.at>
Repository: CRAN
Date/Publication: 2020-09-14 10:20:03 UTC

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Package PSPManalysis updated to version 0.3.2 with previous version 0.2.2 dated 2019-10-22

Title: Analysis of Physiologically Structured Population Models
Description: Performs demographic, bifurcation and evolutionary analysis of physiologically structured population models, which is a class of models that consistently translates continuous-time models of individual life history to the population level. A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) <doi:10.1142/S0218202501001264>. O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) <doi:10.1016/S0040-5809(02)00058-8>. A.M. de Roos (2008) <doi:10.1111/j.1461-0248.2007.01121.x>.
Author: Andre M. de Roos [aut, cre], Ernst Hairer [ctb], Gerhard Wanner [ctb]
Maintainer: Andre M. de Roos <A.M.deRoos@uva.nl>

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Package tablaxlsx (with last version 1.2.2) was removed from CRAN

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

2017-05-27 1.2.2
2016-06-13 1.2.0
2016-05-02 1.1.0
2016-04-06 1.0.9
2016-03-10 1.0.8
2016-02-23 1.0.6
2016-02-15 1.0.5
2016-01-21 1.0.4
2015-12-05 1.0.3
2015-11-30 1.0.1

Permanent link
New package slanter with initial version 0.1-0
Package: slanter
Version: 0.1-0
Date: 2020-08-13
Title: Slanted Matrices and Ordered Clustering
Description: Slanted matrices and ordered clustering for better visualization of similarity data.
Authors@R: c( person("Oren", "Ben-Kiki", email="oren@ben-kiki.org", role=c("aut", "cre")), person("Weizmann Institute of Science", role="cph") )
RoxygenNote: 6.1.1
Encoding: UTF-8
License: MIT + file LICENSE
Imports: Matrix, pheatmap, pracma, stats
LazyData: true
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-09-05 07:49:52 UTC; obk
Author: Oren Ben-Kiki [aut, cre], Weizmann Institute of Science [cph]
Maintainer: Oren Ben-Kiki <oren@ben-kiki.org>
Depends: R (>= 2.10)
Repository: CRAN
Date/Publication: 2020-09-14 09:10:03 UTC

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New package rsBayes with initial version 0.1.0
Package: rsBayes
Title: Bayesian Models for Remotely Sensed Data
Version: 0.1.0
Date: 2020-09-04
Authors@R: c(person("Andrew", "Finley", role=c("aut", "cre"), email="finleya@msu.edu"), person("Chad", "Babcock", role="aut", email="cbabcock@umn.edu"))
Author: Andrew Finley [aut, cre], Chad Babcock [aut]
Maintainer: Andrew Finley <finleya@msu.edu>
Description: Models and associated tools for Bayesian inference of remotely sensed variables.
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 3.5.0)
LazyData: true
Imports: coda
NeedsCompilation: yes
RoxygenNote: 7.0.2
Packaged: 2020-09-05 17:02:26 UTC; andy
Repository: CRAN
Date/Publication: 2020-09-14 09:40:02 UTC

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Package LPCM updated to version 0.46-7 with previous version 0.46-3 dated 2019-08-30

Title: Local Principal Curve Methods
Description: Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering.
Author: Jochen Einbeck and Ludger Evers
Maintainer: Jochen Einbeck <jochen.einbeck@durham.ac.uk>

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New package GARCHIto with initial version 0.1.0
Package: GARCHIto
Type: Package
Title: Class of GARCH-Ito Models
Version: 0.1.0
Author: Xinyu Song
Maintainer: Xinyu Song <song.xinyu@mail.shufe.edu.cn>
Description: Provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: Rsolnp, stats
Depends: R (>= 2.10)
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-09-05 07:43:14 UTC; cindysong
Repository: CRAN
Date/Publication: 2020-09-14 09:10:14 UTC

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Package cnaOpt updated to version 0.2.0 with previous version 0.1.1 dated 2019-12-09

Title: Optimizing Consistency and Coverage in Configurational Causal Modeling
Description: This is an add-on to the 'cna' package <https://CRAN.R-project.org/package=cna> comprising various functions for optimizing consistency and coverage scores of models of configurational comparative methods as Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA). The function conCovOpt() calculates con-cov optima, selectMax() selects con-cov maxima among the con-cov optima, DNFbuild() can be used to build models actually reaching those optima, and findOutcomes() identifies those factor values in analyzed data that can be modeled as outcomes. For a theoretical introduction to these functions see <https://people.uib.no/mba110/docs/ConCovOpt.pdf>.
Author: Mathias Ambuehl [aut, cre, cph], Michael Baumgartner [aut, cph]
Maintainer: Mathias Ambuehl <mathias.ambuehl@consultag.ch>

Diff between cnaOpt versions 0.1.1 dated 2019-12-09 and 0.2.0 dated 2020-09-14

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Package phybreak (with last version 0.2.0) was removed from CRAN

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

2017-07-30 0.2.0
2016-08-31 0.1.1
2016-08-17 0.1.0

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Package MSIseq (with last version 1.0.0) was removed from CRAN

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

2015-06-15 1.0.0
2014-09-03 0.99.1

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New package shinyMergely with initial version 0.1.0
Package: shinyMergely
Type: Package
Title: Compare and Merge Two Files with a 'Shiny' App
Version: 0.1.0
Authors@R: c( person(given = "Stéphane", family = "Laurent", role = c("aut", "cre"), email = "laurent_step@outlook.fr"), person(given = "Jamie", family = "Peabody", role = c("ctb", "cph"), comment = "Mergely"), person(given = "Marijn", family = "Haverbeke", role = c("ctb", "cph"), comment = "CodeMirror"), person(given = "Leandro", family = "Vital", role = c("ctb", "cph"), comment = "Bootstrap Flash Alert"), person(given = "Daniel", family = "Eden", role = c("ctb", "cph"), comment = "animate.css") )
Description: A 'Shiny' app allowing to compare and merge two files, with syntax highlighting for several coding languages.
URL: https://github.com/stla/shinyMergely
BugReports: https://github.com/stla/shinyMergely/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: shiny
Suggests: shinyjqui, shinythemes, tools, uchardet
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-09-04 15:40:51 UTC; stla
Author: Stéphane Laurent [aut, cre], Jamie Peabody [ctb, cph] (Mergely), Marijn Haverbeke [ctb, cph] (CodeMirror), Leandro Vital [ctb, cph] (Bootstrap Flash Alert), Daniel Eden [ctb, cph] (animate.css)
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>
Repository: CRAN
Date/Publication: 2020-09-14 08:30:03 UTC

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New package ripserr with initial version 0.1.0
Package: ripserr
Title: Calculate Persistent Homology with Ripser-Based Engines
Version: 0.1.0
Authors@R: c(person(given = "Raoul", family = "Wadhwa", role = c("aut", "cre"), email = "raoulwadhwa@gmail.com", comment = c(ORCID = "0000-0003-0503-9580")), person(given = "Matt", family = "Piekenbrock", role = "aut", email = "matt.piekenbrock@gmail.com"), person(given = "Jacob", family = "Scott", role = "aut", comment = c(ORCID = "0000-0003-2971-7673")), person(given = "Takeki", family = "Sudo", role = c("cph", "ctb"), comment = paste("Takeki Sudo is a copyright holder for Cubical", "Ripser (GPL-3 license), which was refactored", "prior to inclusion in ripserr.")), person(given = "Kazushi", family = "Ahara", role = c("cph", "ctb"), comment = paste("Kazushi Ahara is a copyright holder for Cubical", "Ripser (GPL-3 license), which was refactored", "prior to inclusion in ripserr.")), person(given = "Ulrich", family = "Bauer", role = c("cph", "ctb"), comment = paste("Ulrich Bauer holds the copyright to Ripser", "(MIT license), which was refactored prior", "to inclusion in ripserr.")))
Description: Ports the Ripser <arXiv:1908.02518> and Cubical Ripser <arXiv:2005.12692> persistent homology calculation engines from C++. Can be used as a rapid calculation tool in topological data analysis pipelines.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
URL: https://github.com/rrrlw/ripserr
BugReports: https://github.com/rrrlw/ripserr/issues
LinkingTo: Rcpp
Depends: R (>= 3.5.0)
Imports: methods (>= 3.0), Rcpp (>= 1.0), stats (>= 3.0)
SystemRequirements: C++11
Suggests: testthat (>= 2.3), covr (>= 3.5), knitr (>= 1.29), rmarkdown (>= 2.3), TDAstats (>= 0.4)
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2020-09-04 21:14:56 UTC; wadhwar
Author: Raoul Wadhwa [aut, cre] (<https://orcid.org/0000-0003-0503-9580>), Matt Piekenbrock [aut], Jacob Scott [aut] (<https://orcid.org/0000-0003-2971-7673>), Takeki Sudo [cph, ctb] (Takeki Sudo is a copyright holder for Cubical Ripser (GPL-3 license), which was refactored prior to inclusion in ripserr.), Kazushi Ahara [cph, ctb] (Kazushi Ahara is a copyright holder for Cubical Ripser (GPL-3 license), which was refactored prior to inclusion in ripserr.), Ulrich Bauer [cph, ctb] (Ulrich Bauer holds the copyright to Ripser (MIT license), which was refactored prior to inclusion in ripserr.)
Maintainer: Raoul Wadhwa <raoulwadhwa@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-14 08:50:03 UTC

More information about ripserr at CRAN
Permanent link

New package binsegRcpp with initial version 2020.9.3
Package: binsegRcpp
Type: Package
Title: Efficient Implementation of Binary Segmentation
Version: 2020.9.3
Author: Toby Dylan Hocking
Maintainer: Toby Dylan Hocking <toby.hocking@r-project.org>
Description: Standard template library containers are used to implement an efficient binary segmentation algorithm, which is log-linear on average and quadratic in the worst case.
License: GPL-3
LinkingTo: Rcpp
URL: https://github.com/tdhock/binseg
BugReports: https://github.com/tdhock/binseg/issues
Imports: data.table, Rcpp
Suggests: ggplot2, testthat
NeedsCompilation: yes
Packaged: 2020-09-04 17:27:51 UTC; tdhock
Repository: CRAN
Date/Publication: 2020-09-14 08:30:07 UTC

More information about binsegRcpp at CRAN
Permanent link

Package Numero updated to version 1.7.0 with previous version 1.6.0 dated 2020-06-23

Title: Statistical Framework to Define Subgroups in Complex Datasets
Description: High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
Author: Song Gao [aut], Stefan Mutter [aut], Aaron E. Casey [aut], Ville-Petteri Makinen [aut, cre]
Maintainer: Ville-Petteri Makinen <vpmakine@gmail.com>

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Package eixport updated to version 0.4.9 with previous version 0.4.7 dated 2020-06-25

Title: Export Emissions to Atmospheric Models
Description: Emissions are the mass of pollutants released into the atmosphere. Air quality models need emissions data, with spatial and temporal distribution, to represent air pollutant concentrations. This package, eixport, creates inputs for the air quality models 'WRF-Chem' Grell et al (2005) <doi:10.1016/j.atmosenv.2005.04.027>, 'BRAMS-SPM' Freitas et al (2005) <doi:10.1016/j.atmosenv.2005.07.017> and 'RLINE' Snyder et al (2013) <doi:10.1016/j.atmosenv.2013.05.074>. See the eixport website (<https://atmoschem.github.io/eixport/>) for more information, documentations and examples. More details in Ibarra-Espinosa et al (2018) <doi.org/10.21105/joss.00607>.
Author: Sergio Ibarra-Espinosa [aut, cre] (<https://orcid.org/0000-0002-3162-1905>), Daniel Schuch [ctb] (<https://orcid.org/0000-0001-5977-4519>), Edmilson Freitas [ctb] (<https://orcid.org/0000-0001-8783-2747>)
Maintainer: Sergio Ibarra-Espinosa <sergio.ibarra@usp.br>

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Package bnviewer updated to version 0.1.6 with previous version 0.1.5 dated 2020-07-22

Title: Bayesian Networks Interactive Visualization and Explainable Artificial Intelligence
Description: Bayesian networks provide an intuitive framework for probabilistic reasoning and its graphical nature can be interpreted quite clearly. Graph based methods of machine learning are becoming more popular because they offer a richer model of knowledge that can be understood by a human in a graphical format. The 'bnviewer' is an R Package that allows the interactive visualization of Bayesian Networks. The aim of this package is to improve the Bayesian Networks visualization over the basic and static views offered by existing packages.
Author: Robson Fernandes [aut, cre, cph]
Maintainer: Robson Fernandes <robson.fernandes@usp.br>

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Package Rfast updated to version 2.0.1 with previous version 2.0.0 dated 2020-08-31

Title: A Collection of Efficient and Extremely Fast R Functions
Description: A collection of fast (utility) functions for data analysis. Column- and row- wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>.
Author: Manos Papadakis, Michail Tsagris, Marios Dimitriadis, Stefanos Fafalios, Ioannis Tsamardinos, Matteo Fasiolo, Giorgos Borboudakis, John Burkardt, Changliang Zou, Kleanthi Lakiotaki and Christina Chatzipantsiou.
Maintainer: Manos Papadakis <rfastofficial@gmail.com>

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Package polAr updated to version 0.2.0 with previous version 0.1.3 dated 2020-05-16

Title: Argentina Political Analysis
Description: Toolbox for the Analysis of Political and Electoral Data from Argentina.
Author: Juan Pablo Ruiz Nicolini [aut, cre, cph] (<https://orcid.org/0000-0002-3138-6343>), Camila Higa [ctb], Iván Lewin [ctb], Lucas Enrich [ctb]
Maintainer: Juan Pablo Ruiz Nicolini <juanpabloruiznicolini@gmail.com>

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Package felp updated to version 0.2.3 with previous version 0.2.2 dated 2019-12-06

Title: Functional Help for Functions, Objects, and Packages
Description: Provides pseudo-postfix operators and more to enhance displaying documents. The `?.` pseudo-postfix operator and the `?` prefix operator displays documents and contents (source or structure) of objects simultaneously to help understanding the objects. The `?p` pseudo-postfix operator displays package documents, and is shorter than help(package = foo).
Author: Atsushi Yasumoto [aut, cph, cre] (<https://orcid.org/0000-0002-8335-495X>)
Maintainer: Atsushi Yasumoto <atusy.rpkg@gmail.com>

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