Thu, 29 Feb 2024

Package port4me updated to version 0.7.1 with previous version 0.7.0 dated 2024-01-15

Title: Get the Same, Personal, Free 'TCP' Port over and over
Description: An R implementation of the cross-platform, language-independent "port4me" algorithm (<https://github.com/HenrikBengtsson/port4me>), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration.
Author: Henrik Bengtsson [aut, cre, cph] , Jeroen Ooms [ctb]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between port4me versions 0.7.0 dated 2024-01-15 and 0.7.1 dated 2024-02-29

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Package FastJM updated to version 1.4.2 with previous version 1.4.1 dated 2024-01-09

Title: Semi-Parametric Joint Modeling of Longitudinal and Survival Data
Description: Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data applying customized linear scan algorithms, proposed by Li and colleagues (2022) <doi:10.1155/2022/1362913>. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.
Author: Shanpeng Li [aut, cre], Ning Li [ctb], Hong Wang [ctb], Jin Zhou [ctb], Hua Zhou [ctb], Gang Li [ctb]
Maintainer: Shanpeng Li <lishanpeng0913@ucla.edu>

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Package epidatr updated to version 1.1.0 with previous version 1.0.0 dated 2023-09-19

Title: Client for Delphi's 'Epidata' API
Description: The Delphi 'Epidata' API provides real-time access to epidemiological surveillance data for influenza, 'COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change 'Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron 'Rumack', Ryan J. 'Tibshirani', 'Roni' 'Rosenfeld' (2015). Delphi 'Epidata' API. <https://github.com/cmu-delphi/delphi-epidata>.
Author: Logan Brooks [aut], Dmitry Shemetov [aut], Samuel Gratzl [aut], David Weber [ctb, cre], Alex Reinhart [ctb], Daniel McDonald [ctb], Kean Ming Tan [ctb], Will Townes [ctb], George Haff [ctb], Kathryn Mazaitis [ctb]
Maintainer: David Weber <davidweb@andrew.cmu.edu>

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Package One4All updated to version 0.4 with previous version 0.3 dated 2024-02-04

Title: Validate, Share, and Download Data
Description: Designed to enhance data validation and management processes by employing a set of functions that read a set of rules from a 'CSV' or 'Excel' file and apply them to a dataset. Funded by the National Renewable Energy Laboratory and Possibility Lab, maintained by the Moore Institute for Plastic Pollution Research.
Author: Hannah Sherrod [cre, aut] , Nick Leong [aut] , Hannah Hapich [aut] , Fabian Gomez [aut], Shelly Moore [aut], Win Cowger [aut] , Scott Coffin [ctb], Tony Hale [ctb], Diana Lin [ctb], Gemma Shusterman [ctb], Rebecca Sutton [ctb], Adam Wong [ctb], Haig [...truncated...]
Maintainer: Hannah Sherrod <hannah@mooreplasticresearch.org>

Diff between One4All versions 0.3 dated 2024-02-04 and 0.4 dated 2024-02-29

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Package secr updated to version 4.6.6 with previous version 4.6.5 dated 2024-02-17

Title: Spatially Explicit Capture-Recapture
Description: Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Author: Murray Efford [aut, cre] , Philipp Jund [ctb] ), David Fletcher [ctb] )
Maintainer: Murray Efford <murray.efford@otago.ac.nz>

Diff between secr versions 4.6.5 dated 2024-02-17 and 4.6.6 dated 2024-02-29

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Package qrnn updated to version 2.1.1 with previous version 2.1 dated 2023-04-27

Title: Quantile Regression Neural Network
Description: Fit quantile regression neural network models with optional left censoring, partial monotonicity constraints, generalized additive model constraints, and the ability to fit multiple non-crossing quantile functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005> and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.
Author: Alex J. Cannon [aut, cre]
Maintainer: Alex J. Cannon <alex.cannon@ec.gc.ca>

Diff between qrnn versions 2.1 dated 2023-04-27 and 2.1.1 dated 2024-02-29

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Package yuima updated to version 1.15.27 with previous version 1.15.22 dated 2022-12-20

Title: The YUIMA Project Package for SDEs
Description: Simulation and Inference for SDEs and Other Stochastic Processes.
Author: YUIMA Project Team
Maintainer: Stefano M. Iacus <siacus@iq.harvard.edu>

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Package Surrogate updated to version 3.2.4 with previous version 3.2.2 dated 2024-02-20

Title: Evaluation of Surrogate Endpoints in Clinical Trials
Description: In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine.
Author: Wim Van Der Elst [cre, aut], Florian Stijven [aut], Fenny Ong [aut], Paul Meyvisch [aut], Alvaro Poveda [aut], Ariel Alonso [aut], Hannah Ensor [aut], Christoper Weir [aut], Geert Molenberghs [aut]
Maintainer: Wim Van Der Elst <wim.vanderelst@gmail.com>

Diff between Surrogate versions 3.2.2 dated 2024-02-20 and 3.2.4 dated 2024-02-29

 DESCRIPTION                                                                         |    6 
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 R/sensitivity_analysis_SurvSurv.R                                                   |   17 
 R/sensitivity_intervals.R                                                           |   21 
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New package specs with initial version 1.0.1
Package: specs
Title: Single-Equation Penalized Error-Correction Selector (SPECS)
Version: 1.0.1
Description: Implementation of SPECS, your favourite Single-Equation Penalized Error-Correction Selector developed in Smeekes and Wijler (2021) <doi:10.1016/j.jeconom.2020.07.021>. SPECS provides a fully automated estimation procedure for large and potentially (co)integrated datasets. The dataset in levels is converted to a conditional error-correction model, either by the user or by means of the functions included in this package, and various specialised forms of penalized regression can be applied to the model. Automated options for initializing and selecting a sequence of penalties, as well as the construction of penalty weights via an initial estimator, are available. Moreover, the user may choose from a number of pre-specified deterministic configurations to further simplify the model building process.
Depends: R (>= 3.5.0)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp
NeedsCompilation: yes
Packaged: 2024-02-29 19:48:32 UTC; Etienne
Author: Etienne Wijler [aut, cre], Stephan Smeekes [aut]
Maintainer: Etienne Wijler <e.j.j.wijler@vu.nl>
Repository: CRAN
Date/Publication: 2024-02-29 21:10:02 UTC

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Package simhelpers updated to version 0.2.1 with previous version 0.2.0 dated 2024-02-23

Title: Helper Functions for Simulation Studies
Description: Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies. Our derivation and explanation of formulas and our general simulation workflow is closely aligned with the approach described by Morris, White, and Crowther (2019) <DOI:10.1002/sim.8086>.
Author: Megha Joshi [aut, cre] , James Pustejovsky [aut]
Maintainer: Megha Joshi <megha.j456@gmail.com>

Diff between simhelpers versions 0.2.0 dated 2024-02-23 and 0.2.1 dated 2024-02-29

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Package RcppCWB updated to version 0.6.4 with previous version 0.6.3 dated 2023-11-30

Title: 'Rcpp' Bindings for the 'Corpus Workbench' ('CWB')
Description: 'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query engine to efficiently analyze large corpora (<https://cwb.sourceforge.io>). 'RcppCWB' is licensed under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (<https://www.r-project.org/Licenses/GPL-3>). The 'CWB' relies on 'pcre2' (BSD license, see <http://www.pcre.org/licence.txt>) and 'GLib' (LGPL license, see <https://www.gnu.org/licenses/lgpl-3.0.en.html>). See the file LICENSE.note for further information. The package includes modified code of the 'rcqp' package (GPL-2, see <https://cran.r-project.org/package=rcqp>). The original work of the authors of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code is the approach used by 'RcppCWB'.
Author: Andreas Blaette [aut, cre], Bernard Desgraupes [aut], Sylvain Loiseau [aut], Oliver Christ [ctb], Bruno Maximilian Schulze [ctb], Stephanie Evert [ctb], Arne Fitschen [ctb], Jeroen Ooms [ctb], Marius Bertram [ctb], Tomas Kalibera [ctb]
Maintainer: Andreas Blaette <andreas.blaette@uni-due.de>

Diff between RcppCWB versions 0.6.3 dated 2023-11-30 and 0.6.4 dated 2024-02-29

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 NEWS.md                          |   15 +
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 configure                        |   14 +
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 man/cwb_charsets.Rd              |    6 
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 man/get_region_matrix.Rd         |   25 +-
 tests/testthat/test_cwb_encode.R |   70 ++----
 11 files changed, 420 insertions(+), 287 deletions(-)

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Package namer updated to version 0.1.8 with previous version 0.1.6 dated 2022-10-06

Title: Names Your 'R Markdown' Chunks
Description: It names the 'R Markdown' chunks of files based on the filename.
Author: Colin Gillespie [aut, cre], Steph Locke [aut], Maelle Salmon [aut] , Ellis Valentiner [ctb], Charlie Hadley [ctb] , Jumping Rivers [fnd] , Han Oostdijk [ctb] , Patrick Schratz [ctb]
Maintainer: Colin Gillespie <colin@jumpingrivers.com>

Diff between namer versions 0.1.6 dated 2022-10-06 and 0.1.8 dated 2024-02-29

 DESCRIPTION                |   22 +++---
 LICENSE                    |only
 MD5                        |   15 ++--
 NEWS.md                    |   17 ++++-
 R/name_chunks.R            |    2 
 build/vignette.rds         |binary
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 man/rmdfragments/intro.Rmd |    4 -
 man/rmdfragments/use.Rmd   |    4 -
 9 files changed, 111 insertions(+), 100 deletions(-)

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Package minimaxApprox updated to version 0.4.1 with previous version 0.4.0 dated 2024-02-26

Title: Implementation of Remez Algorithm for Polynomial and Rational Function Approximation
Description: Implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for rational minimax approximation.
Author: Avraham Adler [aut, cre, cph]
Maintainer: Avraham Adler <Avraham.Adler@gmail.com>

Diff between minimaxApprox versions 0.4.0 dated 2024-02-26 and 0.4.1 dated 2024-02-29

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 MD5                                |   10 +++++-----
 build/partial.rdb                  |binary
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 6 files changed, 23 insertions(+), 12 deletions(-)

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Package kit updated to version 0.0.16 with previous version 0.0.15 dated 2023-10-01

Title: Data Manipulation Functions Implemented in C
Description: Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.
Author: Morgan Jacob [aut, cre, cph], Sebastian Krantz [ctb]
Maintainer: Morgan Jacob <morgan.emailbox@gmail.com>

Diff between kit versions 0.0.15 dated 2023-10-01 and 0.0.16 dated 2024-02-29

 DESCRIPTION              |    8 ++++----
 MD5                      |   10 +++++-----
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 6 files changed, 30 insertions(+), 14 deletions(-)

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Package DIDmultiplegt updated to version 0.1.3 with previous version 0.1.2 dated 2024-02-03

Title: Estimation in DID with Multiple Groups and Periods
Description: Estimate the effect of a treatment on an outcome in sharp Difference-in-Difference designs with multiple groups and periods. It computes the DIDM estimator introduced in Section 4 of "Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects" (Chaisemartin, D'Haultfoeuille (2020) <doi:10.1257/aer.20181169>), which generalizes the standard DID estimator with two groups, two periods and a binary treatment to situations with many groups,many periods and a potentially non-binary treatment. For each pair of consecutive time periods t-1 and t and for each value of the treatment d, the package computes a DID estimator comparing the outcome evolution among the switchers, the groups whose treatment changes from d to some other value between t-1 and t, to the same evolution among control groups whose treatment is equal to d both in t-1 and t. Then the DIDM estimator is equal to the average of those DIDs across all pairs of consecutive time periods and across all values of the [...truncated...]
Author: Shuo Zhang [aut], Clement de Chaisemartin [aut], Diego Ciccia [cre]
Maintainer: Diego Ciccia <diego.ciccia@sciencespo.fr>

Diff between DIDmultiplegt versions 0.1.2 dated 2024-02-03 and 0.1.3 dated 2024-02-29

 DESCRIPTION           |    6 +++---
 MD5                   |    6 +++---
 R/did_multiplegt.R    |    4 ++++
 man/did_multiplegt.Rd |    4 +++-
 4 files changed, 13 insertions(+), 7 deletions(-)

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New package ao with initial version 0.3.3
Package: ao
Title: Alternating Optimization
Version: 0.3.3
Description: Alternating optimization of (high-dimensional) functions is an iterative procedure for optimizing jointly over all parameters by alternately optimizing parameter subsets.
URL: https://loelschlaeger.de/ao/, https://github.com/loelschlaeger/ao/
BugReports: https://github.com/loelschlaeger/ao/issues
License: GPL-3
Encoding: UTF-8
Imports: checkmate, cli, oeli, utils
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
Depends: R (>= 4.0.0), optimizeR (>= 1.0.4)
NeedsCompilation: no
Packaged: 2024-02-29 20:05:27 UTC; Oelschlaeger
Author: Lennart Oelschlaeger [aut, cre]
Maintainer: Lennart Oelschlaeger <oelschlaeger.lennart@gmail.com>
Repository: CRAN
Date/Publication: 2024-02-29 21:12:46 UTC

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New package SemiPar.depCens with initial version 0.1.1
Package: SemiPar.depCens
Title: Copula Based Cox Proportional Hazards Models for Dependent Censoring
Version: 0.1.1
Date: 2024-02-27
Maintainer: Negera Wakgari Deresa <negera.deresa@gmail.com>
Description: Copula based Cox proportional hazards models for survival data subject to dependent censoring. This approach does not assume that the parameter defining the copula is known. The dependency parameter is estimated with other finite model parameters by maximizing a Pseudo likelihood function. The cumulative hazard function is estimated via estimating equations derived based on martingale ideas. Available copula functions include Frank, Gumbel and Normal copulas. Only Weibull and lognormal models are allowed for the censoring model, even though any parametric model that satisfies certain identifiability conditions could be used. Implemented methods are described in the article "Copula based Cox proportional hazards models for dependent censoring" by Deresa and Van Keilegom (2023) <doi:10.1080/01621459.2022.2161387>.
Imports: survival, copula, stats, foreach, parallel, doParallel, pbivnorm
License: GPL-3
Depends: R (>= 3.5.0)
Encoding: UTF-8
LazyData: true
Suggests: testthat (>= 3.0.0)
URL: https://github.com/Nago2020/SemiPar.depCens
BugReports: https://github.com/Nago2020/SemiPar.depCens/issues
NeedsCompilation: no
Packaged: 2024-02-28 23:13:41 UTC; lucp11636
Author: Negera Wakgari Deresa [aut, cre] , Ingrid Van Keilegom [aut]
Repository: CRAN
Date/Publication: 2024-02-29 20:42:46 UTC

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New package roxytypes with initial version 0.1.0
Package: roxytypes
Title: Typed Parameter Tags for Integration with 'roxygen2'
Version: 0.1.0
Description: Provides typed parameter documentation tags for integration with 'roxygen2'. Typed parameter tags provide a consistent interface for annotating expected types for parameters and returned values. Tools for converting from existing styles are also provided to easily adapt projects which implement typed documentation by convention rather than tag. Use the default format or provide your own.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/openpharma/roxytypes, https://openpharma.github.io/roxytypes/
BugReports: https://github.com/openpharma/roxytypes/issues
Imports: utils, cli, glue, roxygen2
Suggests: mockery, testthat
Enhances: roxylint
NeedsCompilation: no
Packaged: 2024-02-28 18:25:38 UTC; root
Author: Doug Kelkhoff [aut, cre]
Maintainer: Doug Kelkhoff <doug.kelkhoff@gmail.com>
Repository: CRAN
Date/Publication: 2024-02-29 20:30:02 UTC

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New package metaConvert with initial version 1.0.0
Package: metaConvert
Title: An Automatic Suite for Estimation of Various Effect Size Measures
Version: 1.0.0
Maintainer: Corentin J. Gosling <corentin.gosling@parisnanterre.fr>
Description: Automatically estimate 11 effect size measures from a well-formatted dataset. Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets. This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size.
Imports: compareDF, metafor, estimraw, mvtnorm, numDeriv, rio, utils
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Suggests: testthat (>= 3.0.0), metaumbrella, TOSTER, esc, epiR, compute.es, meta, effectsize, MetaUtility, knitr, DT, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-02-28 21:57:11 UTC; Corentin Gosling
Author: Corentin J. Gosling [aut, cre], Samuele Cortese [aut], Marco Solmi [aut], Belen Haza [aut], Eduard Vieta [aut], Richard Delorme [aut], Paolo Fusar-Poli [aut], Joaquim Radua [aut]
Repository: CRAN
Date/Publication: 2024-02-29 20:40:02 UTC

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New package JSDNE with initial version 4.2.2
Package: JSDNE
Title: Estimating the Age using Auricular Surface by DNE
Version: 4.2.2
Maintainer: Jisun Jang <jisun.jang.19@ucl.ac.uk>
Description: The age is estimated by calculating the Dirichlet Normal Energy (DNE) on the whole auricular surface and the apex of the auricular surface. It involves three estimation methods: principal component discriminant analysis (PCQDA), principal component regression analysis (PCR), and principal component logistic regression analysis (PCLR) methods. The package is created with the data from the Louis Lopes Collection in Lisbon, the 21st Century Identified Human Remains Collection in Coimbra, and the CAL Milano Cemetery Skeletal Collection in Milan.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 2.10)
LazyData: true
Imports: dplyr, MASS, molaR, nnet, Rvcg
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-02-28 20:38:38 UTC; jangj
Author: Jisun Jang [aut, cre]
Repository: CRAN
Date/Publication: 2024-02-29 20:32:39 UTC

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New package dlim with initial version 0.1.0
Package: dlim
Date: 2024-02-25
Title: Distributed Lag Interaction Model
Version: 0.1.0
Description: Collection of functions for fitting and interpreting distributed lag interaction models (DLIM). A DLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a continuous variable. Includes a dlim() function for fitting, predict() function for inference, and plotting functions for visualization. Details on methodology are described in Demateis et al. (2024) <doi:10.1002/env.2843>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: dlnm, ggplot2, mgcv, reshape2, rlang, splines, tsModel, viridis
Depends: R (>= 2.10)
Suggests: rmarkdown, knitr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-02-29 00:31:34 UTC; ddema
Author: Danielle Demateis [aut, cre] , Kayleigh Keller [aut] , Ander Wilson [aut]
Maintainer: Danielle Demateis <Danielle.Demateis@colostate.edu>
Repository: CRAN
Date/Publication: 2024-02-29 20:42:42 UTC

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New package agcounts with initial version 0.6.6
Package: agcounts
Title: Calculate 'ActiGraph' Counts from Accelerometer Data
Version: 0.6.6
Description: Calculate 'ActiGraph' counts from the X, Y, and Z axes of a triaxial accelerometer. This work was inspired by Neishabouri et al. who published the article "Quantification of Acceleration as Activity Counts in 'ActiGraph' Wearables" on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) and 'python' implementation of this code (<https://github.com/actigraph/agcounts>).
Depends: R (>= 3.5.0)
License: MIT + file LICENSE
Encoding: UTF-8
Imports: data.table, gsignal, lubridate, magrittr, Rcpp, GGIR, stats, utils, zoo, reticulate, dplyr, stringr, ggplot2, reactable, shiny, bslib, read.gt3x, DBI, RSQLite
Suggests: parallel, devtools, foreach, testthat (>= 3.0.0), shinytest2, covr
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2024-02-28 18:57:14 UTC; bhelsel
Author: Brian C. Helsel [aut, cre], Paul R. Hibbing [ctb], Robert N. Montgomery [ctb], Eric D. Vidoni [ctb], Jonathan Clutton [ctb], University of Kansas [cph]
Maintainer: Brian C. Helsel <bhelsel@kumc.edu>
Repository: CRAN
Date/Publication: 2024-02-29 20:12:36 UTC

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New package SINRELEF.LD with initial version 1.0.3
Package: SINRELEF.LD
Title: Reliability and Relative Efficiency in Locally-Dependent Measures
Version: 1.0.3
Date: 2024-02-28
Description: Implements an approach aimed at assessing the accuracy and effectiveness of raw scores obtained in scales that contain locally dependent items. The program uses as input the calibration (structural) item estimates obtained from fitting extended unidimensional factor-analytic solutions in which the existing local dependencies are included. Measures of reliability (Omega) and information are proposed at three levels: (a) total score, (b) bivariate-doublet, and (c) item-by-item deletion, and are compared to those that would be obtained if all the items had been locally independent. All the implemented procedures can be obtained from: (a) linear factor-analytic solutions in which the item scores are treated as approximately continuous, and (b) non-linear solutions in which the item scores are treated as ordered-categorical.
Depends: R (>= 3.5)
Imports: stats
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
LazyData: true
Packaged: 2024-02-28 13:48:43 UTC; dnava
Author: David Navarro-Gonzalez [aut, cre], Fabia Morales-Vives [aut], Pere J. Ferrando [aut]
Maintainer: David Navarro-Gonzalez <david.navarro@urv.cat>
Repository: CRAN
Date/Publication: 2024-02-29 19:22:51 UTC

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New package rnaCrosslinkOO with initial version 0.1.2
Package: rnaCrosslinkOO
Title: Analysis of RNA Crosslinking Data
Version: 0.1.2
Maintainer: Jonathan Price <jlp76@cam.ac.uk>
Description: Analysis of RNA crosslinking data for RNA structure prediction. The package is suitable for the analysis of RNA structure cross-linking data and chemical probing data.
License: GPL-3
Encoding: UTF-8
BugReports: https://github.com/JLP-BioInf/rnaCrosslinkOO/issues
Depends: seqinr, GenomicRanges, stats
Imports: ggplot2, reshape2, MASS, mixtools, utils, S4Vectors, patchwork, doParallel, igraph, R4RNA, RColorBrewer, IRanges, foreach,grDevices, heatmap3, TopDom, tidyverse, RRNA, ggrepel, methods, parallel, ClassDiscovery
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-02-28 14:05:35 UTC; jp
Author: Jonathan Price [aut, cre] , Andrew Lim [ctb]
Repository: CRAN
Date/Publication: 2024-02-29 19:20:02 UTC

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New package paleobioDB with initial version 1.0.0
Package: paleobioDB
Title: Download and Process Data from the Paleobiology Database
Version: 1.0.0
Maintainer: Adrian Castro Insua <adrian.castro.insua@uvigo.gal>
Description: Includes functions to wrap most endpoints of the 'PaleobioDB' API and functions to visualize and process the fossil data. The API documentation for the Paleobiology Database can be found at <https://paleobiodb.org/data1.2/>.
License: GPL-2
URL: https://docs.ropensci.org/paleobioDB/, https://github.com/ropensci/paleobioDB
BugReports: https://github.com/ropensci/paleobioDB/issues
Imports: curl, graphics, grDevices, gtools, maps, rjson, terra
Suggests: roxygen2, testthat (>= 3.0.0)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-02-28 10:18:18 UTC; aci
Author: Sara Varela [aut] , Javier Gonzalez Hernandez [aut], Luciano Fabris Sgarbi [aut] , Adrian Castro Insua [cre, ctb]
Repository: CRAN
Date/Publication: 2024-02-29 19:20:07 UTC

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Package FactoMineR updated to version 2.10 with previous version 2.9 dated 2023-10-12

Title: Multivariate Exploratory Data Analysis and Data Mining
Description: Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).
Author: Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet
Maintainer: Francois Husson <francois.husson@institut-agro.fr>

Diff between FactoMineR versions 2.9 dated 2023-10-12 and 2.10 dated 2024-02-29

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 MD5                       |   42 +++---
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 vignettes/FactoMineR.html |  250 +++++++++++--------------------------
 22 files changed, 415 insertions(+), 540 deletions(-)

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New package chouca with initial version 0.0.999
Package: chouca
Title: A Stochastic Cellular Automaton Engine
Version: 0.0.999
Description: An engine for stochastic cellular automata. It provides a high-level interface to declare a model, which can then be simulated by various backends (Genin et al. (2023) <doi:10.1101/2023.11.08.566206>).
License: GPL (>= 3)
Encoding: UTF-8
Depends: R (>= 4.0.0)
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, plyr, digest, stats, graphics, grDevices
Suggests: covr, testthat (>= 3.0.0), deSolve, igraph, knitr, rmarkdown, ggplot2, spatialwarnings
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2024-02-28 13:38:10 UTC; alex
Author: Alexandre Genin [aut, cre]
Maintainer: Alexandre Genin <a.a.h.genin@uu.nl>
Repository: CRAN
Date/Publication: 2024-02-29 19:22:47 UTC

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Package zdeskR updated to version 0.4.0 with previous version 0.3.0 dated 2023-05-02

Title: Connect to Your 'Zendesk' Data
Description: Facilitates making a connection to the 'Zendesk' API and executing various queries. You can use it to get ticket, ticket metrics, and user data. The 'Zendesk' documentation is available at <https://developer.zendesk.com/rest_api /docs/support/introduction>. This package is not supported by 'Zendesk' (owner of the software).
Author: Chris Umphlett [aut, cre], Avinash Panigrahi [aut]
Maintainer: Chris Umphlett <christopher.umphlett@gmail.com>

Diff between zdeskR versions 0.3.0 dated 2023-05-02 and 0.4.0 dated 2024-02-29

 DESCRIPTION                 |   10 +++++-----
 MD5                         |    6 ++++--
 NAMESPACE                   |    4 ++++
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 5 files changed, 13 insertions(+), 7 deletions(-)

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Package lpcde updated to version 0.1.2 with previous version 0.1.1 dated 2023-09-01

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).
Author: Rajita Chandak [aut, cre]
Maintainer: Rajita Chandak <rchandak@princeton.edu>

Diff between lpcde versions 0.1.1 dated 2023-09-01 and 0.1.2 dated 2024-02-29

 DESCRIPTION    |    8 ++++----
 MD5            |   14 +++++++-------
 NEWS.md        |    9 +++++++++
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 man/lpbwcde.Rd |    6 ++++++
 man/lpcde.Rd   |    9 +++++++++
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 8 files changed, 103 insertions(+), 26 deletions(-)

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Package aRxiv updated to version 0.10 with previous version 0.8 dated 2024-01-22

Title: Interface to the arXiv API
Description: An interface to the API for 'arXiv', a repository of electronic preprints for computer science, mathematics, physics, quantitative biology, quantitative finance, and statistics.
Author: Karthik Ram [aut] , Karl Broman [aut, cre]
Maintainer: Karl Broman <broman@wisc.edu>

Diff between aRxiv versions 0.8 dated 2024-01-22 and 0.10 dated 2024-02-29

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 LICENSE             |    2 -
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 vignettes/aRxiv.Rmd |   19 +++++++++----
 9 files changed, 98 insertions(+), 63 deletions(-)

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Package r2dii.plot updated to version 0.4.0 with previous version 0.3.1 dated 2023-05-23

Title: Visualize the Climate Scenario Alignment of a Financial Portfolio
Description: Create plots to visualize the alignment of a corporate lending financial portfolio to climate change scenarios based on climate indicators (production and emission intensities) across key climate relevant sectors of the 'PACTA' methodology (Paris Agreement Capital Transition Assessment; <https://www.transitionmonitor.com/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals.
Author: Monika Furdyna [aut, ctr, cre] , Mauro Lepore [aut, ctr] , Alex Axthelm [aut, ctr] , Rocky Mountain Institute [cph, fnd]
Maintainer: Monika Furdyna <monika.furdyna@gmail.com>

Diff between r2dii.plot versions 0.3.1 dated 2023-05-23 and 0.4.0 dated 2024-02-29

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Package gmvarkit updated to version 2.1.2 with previous version 2.1.1 dated 2024-01-22

Title: Estimate Gaussian and Student's t Mixture Vector Autoregressive Models
Description: Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (forthcoming) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen (2022) <arXiv:2109.13648>.
Author: Savi Virolainen [aut, cre]
Maintainer: Savi Virolainen <savi.virolainen@helsinki.fi>

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Package intendo updated to version 0.1.1 with previous version 0.1 dated 2023-02-22

Title: A Group of Fun Datasets of Various Sizes and Differing Levels of Quality
Description: Four datasets are provided here from the 'Intendo' game 'Super Jetroid'. It is data from the 2015 year of operation and it comprises a revenue table ('all_revenue'), a daily users table ('users_daily'), a user summary table ('user_summary'), and a table with data on all user sessions ('all_sessions'). These core datasets come in different sizes, and, each of them has a variant that was intentionally made faulty (totally riddled with errors and inconsistencies). This suite of tables is useful for testing with packages that focus on data validation and data documentation.
Author: Richard Iannone [aut, cre]
Maintainer: Richard Iannone <riannone@me.com>

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Package hdflex updated to version 0.2.1 with previous version 0.2.0 dated 2023-10-31

Title: High-Dimensional Aggregate Density Forecasts
Description: Provides a forecasting method that maps vast numbers of (scalar-valued) signals of any type into an aggregate density forecast in a time-varying and computationally fast manner. The method proceeds in two steps: First, it transforms a predictive signal into a density forecast. Second, it combines the generated candidate density forecasts into an ultimate density forecast. The methods are explained in detail in Adaemmer et al. (2023) <doi:10.2139/ssrn.4342487>.
Author: Sven Lehmann [aut, cre, cph], Philipp Adaemmer [aut], Rainer Schuessler [aut]
Maintainer: Sven Lehmann <sven.lehmann@uni-rostock.de>

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Package svSocket updated to version 1.1.5 with previous version 1.1.0 dated 2022-05-10

Title: 'SciViews' - Socket Server
Description: A socket server allows to connect clients to R.
Author: Philippe Grosjean [aut, cre] , Matthew Dowle [ctb]
Maintainer: Philippe Grosjean <phgrosjean@sciviews.org>

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Package qrng updated to version 0.0-10 with previous version 0.0-9 dated 2023-04-20

Title: (Randomized) Quasi-Random Number Generators
Description: Functionality for generating (randomized) quasi-random numbers in high dimensions.
Author: Marius Hofert [aut, cre], Christiane Lemieux [aut]
Maintainer: Marius Hofert <mhofert@hku.hk>

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Package noise updated to version 1.0.2 with previous version 1.0.1 dated 2021-01-07

Title: Estimation of Intrinsic and Extrinsic Noise from Single-Cell Data
Description: Functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186. Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471.
Author: Audrey Qiuyan Fu and Lior Pachter
Maintainer: Audrey Q. Fu <audreyqyfu@gmail.com>

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New package tidyheatmaps with initial version 0.2.1
Package: tidyheatmaps
Title: Heatmaps from Tidy Data
Version: 0.2.1
Description: The goal of 'tidyheatmaps' is to simplify the generation of publication-ready heatmaps from tidy data. By offering an interface to the powerful 'pheatmap' package, it allows for the effortless creation of intricate heatmaps with minimal code.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: dplyr, pheatmap, rlang, grDevices, tidyr, tibble, RColorBrewer
Suggests: testthat (>= 2.1.0), knitr, rmarkdown
URL: https://github.com/jbengler/tidyheatmaps, https://jbengler.github.io/tidyheatmaps/
BugReports: https://github.com/jbengler/tidyheatmaps/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-02-28 12:54:41 UTC; janbroderengler
Author: Jan Broder Engler [aut, cre, cph]
Maintainer: Jan Broder Engler <broder.engler@gmail.com>
Repository: CRAN
Date/Publication: 2024-02-29 13:00:02 UTC

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Package PUMP updated to version 1.0.3 with previous version 1.0.2 dated 2023-05-22

Title: Power Under Multiplicity Project
Description: Estimates power, minimum detectable effect size (MDES) and sample size requirements. The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see <doi:10.18637/jss.v108.i06>.
Author: Kristin Porter [aut], Luke Miratrix [aut, cre] , Kristen Hunter [aut] , Zarni Htet [aut], MDRC [cph], Institute of Education Sciences [fnd]
Maintainer: Luke Miratrix <luke_miratrix@gse.harvard.edu>

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New package OptGS with initial version 1.2
Package: OptGS
Title: Near-Optimal Group-Sequential Designs for Continuous Outcomes
Version: 1.2
Date: 2024-02-27
Maintainer: James Wason <james.wason@newcastle.ac.uk>
Description: Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. 'OptGS' provides functions to quickly search for near-optimal group-sequential designs for normally distributed outcomes. The methods used are described in Wason, JMS (2015) <doi:10.18637/jss.v066.i02>.
License: GPL-2
NeedsCompilation: yes
Packaged: 2024-02-28 13:18:08 UTC; njw228
Author: James Wason [aut, cre], John Burkardt [ctb], R O'Neill [ctb]
Repository: CRAN
Date/Publication: 2024-02-29 13:02:48 UTC

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New package moderate.mediation with initial version 0.0.8
Package: moderate.mediation
Version: 0.0.8
Date: 2024-02-12
Title: Causal Moderated Mediation Analysis
Maintainer: Xu Qin <xuqin@pitt.edu>
Depends: R (>= 3.5.0)
Imports: stats, mvtnorm, cowplot, distr, graphics, ggplot2, reshape2, doSNOW, scales, foreach, earth
Description: Causal moderated mediation analysis using the methods proposed by Qin and Wang (2023) <doi:10.3758/s13428-023-02095-4>. Causal moderated mediation analysis is crucial for investigating how, for whom, and where a treatment is effective by assessing the heterogeneity of mediation mechanism across individuals and contexts. This package enables researchers to estimate and test the conditional and moderated mediation effects, assess their sensitivity to unmeasured pre-treatment confounding, and visualize the results. The package is built based on the quasi-Bayesian Monte Carlo method, because it has relatively better performance at small sample sizes, and its running speed is the fastest. The package is applicable to a treatment of any scale, a binary or continuous mediator, a binary or continuous outcome, and one or more moderators of any scale.
License: GPL-2
NeedsCompilation: no
Packaged: 2024-02-12 23:32:03 UTC; XUQIN
Author: Xu Qin [aut, cre, cph], Lijuan Wang [ctb]
Repository: CRAN
Date/Publication: 2024-02-29 13:00:05 UTC

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Package Directional updated to version 6.5 with previous version 6.4 dated 2023-12-09

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>

Diff between Directional versions 6.4 dated 2023-12-09 and 6.5 dated 2024-02-29

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Package Compositional updated to version 6.7 with previous version 6.6 dated 2023-12-09

Title: Compositional Data Analysis
Description: Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. b) Tsagris M. (2014). "The k-NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3): 519--534. c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. d) Tsagris M. (2015). "Regression analysis with compositional data containing zero values". Chilean Journa [...truncated...]
Author: Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb], Christos Adam [ctb]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

Diff between Compositional versions 6.6 dated 2023-12-09 and 6.7 dated 2024-02-29

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New package SlicedLHD with initial version 1.0
Package: SlicedLHD
Version: 1.0
Date: 2024-02-28
Title: Sliced Latin Hypercube Designs
Author: A Anil Kumar [aut], Baidya Nath Mandal [aut, cre], Rajender Parsad [aut], Sukanta Dash [aut], Mukesh Kumar [aut]
Maintainer: Baidya Nath Mandal <mandal.stat@gmail.com>
Depends: R (>= 4.3.0)
Description: A facility to generate sliced (orthogonal) Latin hypercube designs with four and five slices. For details about sliced and orthogonal Latin hypercube designs, see Yang, J. F., Lin, C. D., Qian, P. Z., and Lin, D. K. (2013). "Construction of sliced orthogonal Latin hypercube designs". Statistica Sinica, 1117-1130, <doi:10.5705/ss.2012.037>.
Note: This package is developed as part of ongoing Ph.D. (Agricultural Statistics) thesis research work of first author at ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2024-02-28 12:10:51 UTC; PC
Repository: CRAN
Date/Publication: 2024-02-29 12:42:40 UTC

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New package Lifertable with initial version 0.0.1
Package: Lifertable
Title: Life and Fertility Tables Specially for Insects
Version: 0.0.1
Description: Life and Fertility Tables are appropriate to study the dynamics of arthropods populations. This package provides utilities for constructing Life Tables and Fertility Tables, related demographic parameters, and some simple graphs of interest. It also offers functions to transform the obtained data into a known format for better manipulation. This document is based on the article by Maia, Luiz, and Campanhola "Statistical Inference on Associated Fertility Life Table Parameters Using Jackknife Technique Computational Aspects" (April 2000, Journal of Economic Entomology, Volume 93, Issue 2) <doi:10.1603/0022-0493-93.2.511>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: TRUE
Imports: ggplot2, methods, stats, tidyr
Suggests: openxlsx
Depends: R (>= 2.10)
Author: Carlos Abimael Sarmiento Sanchez [aut, cre]
Maintainer: Carlos Abimael Sarmiento Sanchez <cass9918@hotmail.com>
NeedsCompilation: no
Packaged: 2024-02-28 02:07:19 UTC; CASS
Repository: CRAN
Date/Publication: 2024-02-29 12:02:37 UTC

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New package GeneNMF with initial version 0.4.0
Package: GeneNMF
Title: Non-Negative Matrix Factorization for Single-Cell Omics
Version: 0.4.0
Description: A collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). 'GeneNMF' contains functions to directly interact with the 'Seurat' toolkit and derive interpretable gene program signatures.
Depends: R (>= 4.3.0)
Imports: RcppML, NMF, stats, Seurat (>= 4.3.0), cluster, pheatmap, viridis
Suggests: knitr, rmarkdown, fgsea, dplyr, msigdbr
VignetteBuilder: knitr
URL: https://github.com/carmonalab/GeneNMF
BugReports: https://github.com/carmonalab/GeneNMF/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2024-02-28 08:27:05 UTC; mass
Author: Massimo Andreatta [aut, cre] , Santiago Carmona [aut]
Maintainer: Massimo Andreatta <massimo.andreatta@unil.ch>
Repository: CRAN
Date/Publication: 2024-02-29 12:22:40 UTC

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Package gausscov updated to version 1.1.0 with previous version 1.0.3 dated 2023-10-11

Title: The Gaussian Covariate Method for Variable Selection
Description: The standard linear regression theory whether frequentist or Bayesian is based on an 'assumed (revealed?) truth' (John Tukey) attitude to models. This is reflected in the language of statistical inference which involves a concept of truth, for example confidence intervals, hypothesis testing and consistency. The motivation behind this package was to remove the word true from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper, it is intuitive and requires only four simple equations. Its overwhelming advantage compared with a standard F P-value is that is is exact and valid whatever the data. In contrast F P [...truncated...]
Author: Laurie Davies [aut, cre]
Maintainer: Laurie Davies <pldavies44@cantab.net>

Diff between gausscov versions 1.0.3 dated 2023-10-11 and 1.1.0 dated 2024-02-29

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New package COAP with initial version 1.1
Package: COAP
Title: High-Dimensional Covariate-Augmented Overdispersed Poisson Factor Model
Version: 1.1
Date: 2024-02-28
Author: Wei Liu [aut, cre], Qingzhi Zhong [aut]
Maintainer: Wei Liu <liuweideng@gmail.com>
Description: A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2402.15071>.
License: GPL-3
Depends: irlba, R (>= 3.5.0)
Imports: MASS, stats, Rcpp (>= 1.0.10)
URL: https://github.com/feiyoung/COAP
BugReports: https://github.com/feiyoung/COAP/issues
Suggests: knitr, rmarkdown
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2024-02-28 06:04:26 UTC; Liuxianju
Repository: CRAN
Date/Publication: 2024-02-29 12:22:43 UTC

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Package bbotk updated to version 0.8.0 with previous version 0.7.3 dated 2023-11-13

Title: Black-Box Optimization Toolkit
Description: Features highly configurable search spaces via the 'paradox' package and optimizes every user-defined objective function. The package includes several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). bbotk is the base package of 'mlr3tuning', 'mlr3fselect' and 'miesmuschel'.
Author: Marc Becker [cre, aut] , Jakob Richter [aut] , Michel Lang [aut] , Bernd Bischl [aut] , Martin Binder [aut], Olaf Mersmann [ctb]
Maintainer: Marc Becker <marcbecker@posteo.de>

Diff between bbotk versions 0.7.3 dated 2023-11-13 and 0.8.0 dated 2024-02-29

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Package spatstat.random updated to version 3.2-3 with previous version 3.2-2 dated 2023-11-29

Title: Random Generation Functionality for the 'spatstat' Family
Description: Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'.
Author: Adrian Baddeley [aut, cre, cph] , Rolf Turner [aut, cph] , Ege Rubak [aut, cph] , Tilman Davies [aut, cph] , Kasper Klitgaard Berthelsen [ctb, cph], David Bryant [ctb, cph], Ya-Mei Chang [ctb, cph], Ute Hahn [ctb], Abdollah Jalilian [ctb], Dominic Sc [...truncated...]
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between spatstat.random versions 3.2-2 dated 2023-11-29 and 3.2-3 dated 2024-02-29

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New package normaliseR with initial version 0.1.2
Package: normaliseR
Title: Re-Scale Vectors and Time-Series Features
Version: 0.1.2
Date: 2024-02-28
Maintainer: Trent Henderson <then6675@uni.sydney.edu.au>
Description: Provides standardized access to a range of re-scaling methods for numerical vectors and time-series features calculated within the 'theft' ecosystem.
BugReports: https://github.com/hendersontrent/normaliseR/issues
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: rlang, stats, dplyr, scales
Suggests: knitr, markdown, rmarkdown, pkgdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://hendersontrent.github.io/normaliseR/
NeedsCompilation: no
Packaged: 2024-02-28 00:25:29 UTC; trenthenderson
Author: Trent Henderson [cre, aut]
Repository: CRAN
Date/Publication: 2024-02-29 11:50:02 UTC

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New package multilevelmediation with initial version 0.3.1
Package: multilevelmediation
Title: Utility Functions for Multilevel Mediation Analysis
Version: 0.3.1
Date: 2024-02-27
Maintainer: Carl F. Falk <carl.falk@mcgill.ca>
BugReports: https://github.com/falkcarl/multilevelmediation
Description: The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for multilevel mediation, the option of a moderating variable for either the a, b, or both paths, and covariates. Currently the 1-1-1 model is supported and several options of random effects; the initial code for bootstrapping was evaluated in simulations by Falk, Vogel, Hammami, and Miočević (2024) <doi:10.3758/s13428-023-02079-4>. Support for Bayesian estimation using 'brms' comprises ongoing work. Currently only continuous mediators and outcomes are supported. Factors for any predictors must be numerically represented.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: furrr, future, matrixcalc, MCMCpack, nlme, parallel, parallelly, stats, tidyr, brms, posterior
Suggests: testthat (>= 3.0.0), boot
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2024-02-28 00:21:41 UTC; CarlFalkMcGill
Author: Carl F. Falk [cre, aut], Todd Vogel [aut], Sarah Hammami [aut], Milica Miocevic [aut]
Repository: CRAN
Date/Publication: 2024-02-29 11:50:05 UTC

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Package lfe updated to version 3.0-0 with previous version 2.9-0 dated 2023-02-13

Title: Linear Group Fixed Effects
Description: Transforms away factors with many levels prior to doing an OLS. Useful for estimating linear models with multiple group fixed effects, and for estimating linear models which uses factors with many levels as pure control variables. See Gaure (2013) <doi:10.1016/j.csda.2013.03.024> Includes support for instrumental variables, conditional F statistics for weak instruments, robust and multi-way clustered standard errors, as well as limited mobility bias correction (Gaure 2014 <doi:10.1002/sta4.68>). Since version 3.0, it provides dedicated functions to estimate Poisson models.
Author: Simen Gaure [aut] , Grant McDermott [ctb], Mauricio Vargas Sepulveda [ctb, cre] , Karl Dunkle Werner [ctb], Matthieu Stigler [ctb] , Daniel Luedecke [ctb]
Maintainer: Mauricio Vargas Sepulveda <m.sepulveda@mail.utoronto.ca>

Diff between lfe versions 2.9-0 dated 2023-02-13 and 3.0-0 dated 2024-02-29

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Package predictmeans updated to version 1.1.0 with previous version 1.0.9 dated 2023-10-20

Title: Predicted Means for Linear and Semiparametric Models
Description: Providing functions to diagnose and make inferences from various linear models, such as those obtained from 'aov', 'lm', 'glm', 'gls', 'lme', 'lmer', 'glmmTMB' and 'semireg'. Inferences include predicted means and standard errors, contrasts, multiple comparisons, permutation tests, adjusted R-square and graphs.
Author: Dongwen Luo, Siva Ganesh and John Koolaard
Maintainer: Dongwen Luo <dongwen.luo@agresearch.co.nz>

Diff between predictmeans versions 1.0.9 dated 2023-10-20 and 1.1.0 dated 2024-02-29

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Package BioM2 updated to version 1.0.4 with previous version 1.0.3 dated 2024-01-10

Title: Biologically Explainable Machine Learning Framework
Description: Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <arXiv:1712.0036v1>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.
Author: Shunjie Zhang and Junfang Chen
Maintainer: Shunjie Zhang <zhang.shunjie@qq.com>

Diff between BioM2 versions 1.0.3 dated 2024-01-10 and 1.0.4 dated 2024-02-29

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Package multilevelcoda (with last version 1.2.1) was removed from CRAN

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

2023-08-13 1.2.1
2023-08-12 1.2.0
2023-06-16 1.1.0

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Package MSoutcomes updated to version 0.2.0 with previous version 0.1.0 dated 2023-09-13

Title: CORe Multiple Sclerosis Outcomes Toolkit
Description: Enable operationalized evaluation of disease outcomes in multiple sclerosis. ‘MSoutcomes’ requires longitudinally recorded clinical data structured in long format. The package is based on the research developed at Clinical Outcomes Research unit (CORe), University of Melbourne and Neuroimmunology Centre, Royal Melbourne Hospital. Kalincik et al. (2015) <doi:10.1093/brain/awv258>. Lorscheider et al. (2016) <doi:10.1093/brain/aww173>. Sharmin et al. (2022) <doi:10.1111/ene.15406>. Dzau et al. (2023) <doi:10.1136/jnnp-2023-331748>.
Author: Sifat Sharmin [aut], Johannes Lorscheider [aut], Nathaniel Lizak [aut], Sam Harding-Forrester [aut], Winston Dzau [aut], Tomas Kalincik [aut, cre, cph]
Maintainer: Tomas Kalincik <tomas.kalincik@unimelb.edu.au>

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Package AmigaFFH updated to version 0.4.5 with previous version 0.4.3 dated 2023-08-27

Title: Commodore Amiga File Format Handler
Description: Modern software often poorly support older file formats. This package intends to handle many file formats that were native to the antiquated Commodore Amiga machine. This package focuses on file types from the older Amiga operating systems (<= 3.0). It will read and write specific file formats and coerces them into more contemporary data.
Author: Pepijn de Vries [aut, cre, dtc]
Maintainer: Pepijn de Vries <pepijn.devries@outlook.com>

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Package geostan (with last version 0.5.3) was removed from CRAN

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

2023-11-24 0.5.3
2023-10-04 0.5.2
2023-06-03 0.5.1
2023-05-29 0.5.0
2022-12-04 0.4.1
2022-09-19 0.4.0
2022-07-11 0.3.0
2022-02-09 0.2.1

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Package RivRetrieve (with last version 0.1.3) was removed from CRAN

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

2023-11-14 0.1.3
2023-11-03 0.1.2

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Package mlr3resampling (with last version 2024.1.23) was removed from CRAN

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

2024-02-01 2024.1.23
2024-01-08 2024.1.8
2023-12-21 2023.12.20

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Package atime (with last version 2024.1.31) was removed from CRAN

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

2024-02-01 2024.1.31
2024-01-24 2024.1.24
2023-04-03 2023.3.31
2023-03-31 2023.3.30
2022-09-19 2022.9.16
2022-08-27 2022.8.25

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Package jmastats (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:

2023-09-11 0.2.0

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Package jlmerclusterperm (with last version 1.1.2) was removed from CRAN

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

2024-02-17 1.1.2
2024-02-08 1.1.1
2023-10-07 1.1.0
2023-09-18 1.0.6
2023-09-10 1.0.5
2023-09-06 1.0.4
2023-07-19 1.0.3
2023-07-15 1.0.2
2023-07-02 1.0.1
2023-06-27 1.0.0

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Package QTLEMM updated to version 1.5.1 with previous version 1.5.0 dated 2024-01-22

Title: QTL Mapping and Hotspots Detection
Description: For QTL mapping, this package comprises several functions designed to execute diverse tasks, such as simulating or analyzing data, calculating significance thresholds, and visualizing QTL mapping results. The single-QTL or multiple-QTL method, which enables the fitting and comparison of various statistical models, is employed to analyze the data for estimating QTL parameters. The models encompass linear regression, permutation tests, normal mixture models, and truncated normal mixture models. The Gaussian stochastic process is utilized to compute significance thresholds for QTL detection on a genetic linkage map within experimental populations. Two types of data, complete genotyping, and selective genotyping data from various experimental populations, including backcross, F2, recombinant inbred (RI) populations, and advanced intercrossed (AI) populations, are considered in the QTL mapping analysis. For QTL hotspot detection, statistical methods can be developed based on either utilizin [...truncated...]
Author: Ping-Yuan Chung [cre], Chen-Hung Kao [aut], Y.-T. Guo [aut], H.-N. Ho [aut], H.-I. Lee [aut], P.-Y. Wu [aut], M.-H. Yang [aut], M.-H. Zeng [aut]
Maintainer: Ping-Yuan Chung <pychung@webmail.stat.sinica.edu.tw>

Diff between QTLEMM versions 1.5.0 dated 2024-01-22 and 1.5.1 dated 2024-02-29

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Package parallelly updated to version 1.37.1 with previous version 1.37.0 dated 2024-02-14

Title: Enhancing the 'parallel' Package
Description: Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package. For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between parallelly versions 1.37.0 dated 2024-02-14 and 1.37.1 dated 2024-02-29

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Package data.table updated to version 1.15.2 with previous version 1.15.0 dated 2024-01-30

Title: Extension of `data.frame`
Description: Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.
Author: Tyson Barrett [aut, cre], Matt Dowle [aut], Arun Srinivasan [aut], Jan Gorecki [aut], Michael Chirico [aut], Toby Hocking [aut] , Pasha Stetsenko [ctb], Tom Short [ctb], Steve Lianoglou [ctb], Eduard Antonyan [ctb], Markus Bonsch [ctb], Hugh Parsonag [...truncated...]
Maintainer: Tyson Barrett <t.barrett88@gmail.com>

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Package VGAM updated to version 1.1-10 with previous version 1.1-9 dated 2023-09-19

Title: Vector Generalized Linear and Additive Models
Description: An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecolog [...truncated...]
Author: Thomas Yee [aut, cre] , Cleve Moler [ctb]
Maintainer: Thomas Yee <t.yee@auckland.ac.nz>

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 VGAM-1.1-10/VGAM/R/family.gaitd.R                 |  107 
 VGAM-1.1-10/VGAM/R/family.genetic.R               |    2 
 VGAM-1.1-10/VGAM/R/family.glmgam.R                |    2 
 VGAM-1.1-10/VGAM/R/family.loglin.R                |    2 
 VGAM-1.1-10/VGAM/R/family.maths.R                 |    2 
 VGAM-1.1-10/VGAM/R/family.mixture.R               |    2 
 VGAM-1.1-10/VGAM/R/family.nbd.R                   |  148 -
 VGAM-1.1-10/VGAM/R/family.nonlinear.R             |    2 
 VGAM-1.1-10/VGAM/R/family.normal.R                |  213 +
 VGAM-1.1-10/VGAM/R/family.oneinf.R                |   56 
 VGAM-1.1-10/VGAM/R/family.others.R                |    2 
 VGAM-1.1-10/VGAM/R/family.positive.R              |    2 
 VGAM-1.1-10/VGAM/R/family.qreg.R                  |    2 
 VGAM-1.1-10/VGAM/R/family.rcim.R                  |    2 
 VGAM-1.1-10/VGAM/R/family.rcqo.R                  |    2 
 VGAM-1.1-10/VGAM/R/family.robust.R                |    2 
 VGAM-1.1-10/VGAM/R/family.rrr.R                   | 2079 +++++++++--------
 VGAM-1.1-10/VGAM/R/family.sur.R                   |    2 
 VGAM-1.1-10/VGAM/R/family.survival.R              |    2 
 VGAM-1.1-10/VGAM/R/family.ts.R                    |    3 
 VGAM-1.1-10/VGAM/R/family.univariate.R            |  493 ++--
 VGAM-1.1-10/VGAM/R/family.vglm.R                  |    2 
 VGAM-1.1-10/VGAM/R/family.zeroinf.R               |    2 
 VGAM-1.1-10/VGAM/R/fittedvlm.R                    |    2 
 VGAM-1.1-10/VGAM/R/formula.vlm.q                  |    2 
 VGAM-1.1-10/VGAM/R/generic.q                      |    2 
 VGAM-1.1-10/VGAM/R/getxvlmaug.R                   |    2 
 VGAM-1.1-10/VGAM/R/hdeff.R                        |    2 
 VGAM-1.1-10/VGAM/R/links.q                        | 1760 +++++++-------
 VGAM-1.1-10/VGAM/R/logLik.vlm.q                   |    2 
 VGAM-1.1-10/VGAM/R/lrp.R                          |    2 
 VGAM-1.1-10/VGAM/R/lrwaldtest.R                   |    2 
 VGAM-1.1-10/VGAM/R/model.matrix.vglm.q            |    2 
 VGAM-1.1-10/VGAM/R/mux.q                          |    2 
 VGAM-1.1-10/VGAM/R/nobs.R                         |    2 
 VGAM-1.1-10/VGAM/R/plot.vgam.R                    |  482 ++-
 VGAM-1.1-10/VGAM/R/plot.vglm.R                    |    2 
 VGAM-1.1-10/VGAM/R/predict.vgam.q                 |    2 
 VGAM-1.1-10/VGAM/R/predict.vglm.q                 |    2 
 VGAM-1.1-10/VGAM/R/predict.vlm.q                  |    2 
 VGAM-1.1-10/VGAM/R/print.vglm.q                   |    2 
 VGAM-1.1-10/VGAM/R/print.vlm.q                    |    7 
 VGAM-1.1-10/VGAM/R/psv2magic.R                    |    2 
 VGAM-1.1-10/VGAM/R/qrrvglm.control.q              |    2 
 VGAM-1.1-10/VGAM/R/qtplot.q                       |    2 
 VGAM-1.1-10/VGAM/R/residuals.vlm.q                |    2 
 VGAM-1.1-10/VGAM/R/rootogram4.vglm.R              |    2 
 VGAM-1.1-10/VGAM/R/rrvglm.R                       |   53 
 VGAM-1.1-10/VGAM/R/rrvglm.control.q               |  152 -
 VGAM-1.1-10/VGAM/R/rrvglm.fit.q                   |  302 +-
 VGAM-1.1-10/VGAM/R/s.q                            |    2 
 VGAM-1.1-10/VGAM/R/s.vam.q                        |    2 
 VGAM-1.1-10/VGAM/R/simulate.vglm.R                |    2 
 VGAM-1.1-10/VGAM/R/sm.os.R                        |    2 
 VGAM-1.1-10/VGAM/R/sm.ps.R                        |    2 
 VGAM-1.1-10/VGAM/R/smart.R                        |    2 
 VGAM-1.1-10/VGAM/R/step4.vglm.R                   |    2 
 VGAM-1.1-10/VGAM/R/summary.drrvglm.R              |only
 VGAM-1.1-10/VGAM/R/summary.vgam.q                 |    6 
 VGAM-1.1-10/VGAM/R/summary.vglm.q                 |  154 -
 VGAM-1.1-10/VGAM/R/summary.vlm.q                  |   24 
 VGAM-1.1-10/VGAM/R/vcov.pvgam.R                   |    3 
 VGAM-1.1-10/VGAM/R/vgam.R                         |    2 
 VGAM-1.1-10/VGAM/R/vgam.control.q                 |    2 
 VGAM-1.1-10/VGAM/R/vgam.fit.q                     |    2 
 VGAM-1.1-10/VGAM/R/vgam.match.q                   |    2 
 VGAM-1.1-10/VGAM/R/vglm.R                         |    2 
 VGAM-1.1-10/VGAM/R/vglm.control.q                 |    2 
 VGAM-1.1-10/VGAM/R/vglm.fit.q                     |    2 
 VGAM-1.1-10/VGAM/R/vlm.R                          |    2 
 VGAM-1.1-10/VGAM/R/vlm.wfit.q                     |   10 
 VGAM-1.1-10/VGAM/R/vsmooth.spline.q               |    2 
 VGAM-1.1-10/VGAM/R/wald0.R                        |    2 
 VGAM-1.1-10/VGAM/demo/binom2.or.R                 |   30 
 VGAM-1.1-10/VGAM/demo/cqo.R                       |   71 
 VGAM-1.1-10/VGAM/demo/distributions.R             |    8 
 VGAM-1.1-10/VGAM/demo/lmsqreg.R                   |   31 
 VGAM-1.1-10/VGAM/demo/vgam.R                      |   20 
 VGAM-1.1-10/VGAM/demo/zipoisson.R                 |   33 
 VGAM-1.1-10/VGAM/man/CM.equid.Rd                  |only
 VGAM-1.1-10/VGAM/man/Coef.qrrvglm-class.Rd        |    1 
 VGAM-1.1-10/VGAM/man/Coef.qrrvglm.Rd              |    1 
 VGAM-1.1-10/VGAM/man/CommonVGAMffArguments.Rd     | 1033 +++++---
 VGAM-1.1-10/VGAM/man/Links.Rd                     |  235 +
 VGAM-1.1-10/VGAM/man/QvarUC.Rd                    |    2 
 VGAM-1.1-10/VGAM/man/Trunc.Rd                     |    5 
 VGAM-1.1-10/VGAM/man/UtilitiesVGAM.Rd             |    4 
 VGAM-1.1-10/VGAM/man/VGAM-package.Rd              |  215 +
 VGAM-1.1-10/VGAM/man/acat.Rd                      |   15 
 VGAM-1.1-10/VGAM/man/alogitlink.Rd                |only
 VGAM-1.1-10/VGAM/man/alteredvglm.Rd               |    4 
 VGAM-1.1-10/VGAM/man/asinlink.Rd                  |only
 VGAM-1.1-10/VGAM/man/betabinomUC.Rd               |    1 
 VGAM-1.1-10/VGAM/man/betabinomial.Rd              |   90 
 VGAM-1.1-10/VGAM/man/betabinomialff.Rd            |   16 
 VGAM-1.1-10/VGAM/man/biclaytoncopUC.Rd            |  224 -
 VGAM-1.1-10/VGAM/man/binomialff.Rd                |    8 
 VGAM-1.1-10/VGAM/man/biplot-methods.Rd            |    7 
 VGAM-1.1-10/VGAM/man/bistudentt.Rd                |    4 
 VGAM-1.1-10/VGAM/man/calibrate.qrrvglm.Rd         |    1 
 VGAM-1.1-10/VGAM/man/calibrate.qrrvglm.control.Rd |    2 
 VGAM-1.1-10/VGAM/man/calibrate.rrvglm.control.Rd  |    1 
 VGAM-1.1-10/VGAM/man/cao.Rd                       |    1 
 VGAM-1.1-10/VGAM/man/cao.control.Rd               |    2 
 VGAM-1.1-10/VGAM/man/chisq.Rd                     |   36 
 VGAM-1.1-10/VGAM/man/clogloglink.Rd               |   82 
 VGAM-1.1-10/VGAM/man/constraints.Rd               |    2 
 VGAM-1.1-10/VGAM/man/cops.Rd                      |   33 
 VGAM-1.1-10/VGAM/man/cratio.Rd                    |   13 
 VGAM-1.1-10/VGAM/man/cumulative.Rd                |  205 +
 VGAM-1.1-10/VGAM/man/deplot.lmscreg.Rd            |    7 
 VGAM-1.1-10/VGAM/man/dgaitdplot.Rd                |  162 -
 VGAM-1.1-10/VGAM/man/extbetabinomUC.Rd            |only
 VGAM-1.1-10/VGAM/man/extbetabinomial.Rd           |only
 VGAM-1.1-10/VGAM/man/foldsqrtlink.Rd              |  135 -
 VGAM-1.1-10/VGAM/man/gaitdbinomUC.Rd              |    4 
 VGAM-1.1-10/VGAM/man/gaitdlog.Rd                  |    4 
 VGAM-1.1-10/VGAM/man/gaitdlogUC.Rd                |    5 
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 VGAM-1.1-10/VGAM/man/gaitdnbinomial.Rd            |    5 
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 VGAM-1.1-10/VGAM/man/gaitdzeta.Rd                 |    4 
 VGAM-1.1-10/VGAM/man/gaitdzetaUC.Rd               |    5 
 VGAM-1.1-10/VGAM/man/gamma2.Rd                    |    4 
 VGAM-1.1-10/VGAM/man/garma.Rd                     |    4 
 VGAM-1.1-10/VGAM/man/genpoisson1.Rd               |  117 
 VGAM-1.1-10/VGAM/man/genpoisson2.Rd               |  119 
 VGAM-1.1-10/VGAM/man/gensh.Rd                     |only
 VGAM-1.1-10/VGAM/man/genshUC.Rd                   |only
 VGAM-1.1-10/VGAM/man/guplot.Rd                    |    3 
 VGAM-1.1-10/VGAM/man/hdeff.Rd                     |   10 
 VGAM-1.1-10/VGAM/man/hdeffsev.Rd                  |    1 
 VGAM-1.1-10/VGAM/man/hypersecant.Rd               |    1 
 VGAM-1.1-10/VGAM/man/logistic.Rd                  |    1 
 VGAM-1.1-10/VGAM/man/logitlink.Rd                 |    9 
 VGAM-1.1-10/VGAM/man/loglaplace.Rd                |    2 
 VGAM-1.1-10/VGAM/man/logofflink.Rd                |   50 
 VGAM-1.1-10/VGAM/man/lvplot.Rd                    |    3 
 VGAM-1.1-10/VGAM/man/lvplot.qrrvglm.Rd            |    6 
 VGAM-1.1-10/VGAM/man/lvplot.rrvglm.Rd             |   21 
 VGAM-1.1-10/VGAM/man/meplot.Rd                    |    3 
 VGAM-1.1-10/VGAM/man/model.matrixqrrvglm.Rd       |    2 
 VGAM-1.1-10/VGAM/man/multinomial.Rd               |  130 -
 VGAM-1.1-10/VGAM/man/nakagami.Rd                  |    2 
 VGAM-1.1-10/VGAM/man/negbinomial.Rd               |   39 
 VGAM-1.1-10/VGAM/man/notdocumentedyet.Rd          |   32 
 VGAM-1.1-10/VGAM/man/persp.qrrvglm.Rd             |    3 
 VGAM-1.1-10/VGAM/man/plotdeplot.lmscreg.Rd        |    5 
 VGAM-1.1-10/VGAM/man/plotdgaitd.Rd                |   14 
 VGAM-1.1-10/VGAM/man/plotqrrvglm.Rd               |    6 
 VGAM-1.1-10/VGAM/man/plotqtplot.lmscreg.Rd        |    8 
 VGAM-1.1-10/VGAM/man/plotrcim0.Rd                 |    6 
 VGAM-1.1-10/VGAM/man/plotvgam.Rd                  |    3 
 VGAM-1.1-10/VGAM/man/plotvgam.control.Rd          |   24 
 VGAM-1.1-10/VGAM/man/plotvglm.Rd                  |    5 
 VGAM-1.1-10/VGAM/man/predictqrrvglm.Rd            |    1 
 VGAM-1.1-10/VGAM/man/predictvglm.Rd               |   44 
 VGAM-1.1-10/VGAM/man/profilevglm.Rd               |   52 
 VGAM-1.1-10/VGAM/man/propodds.Rd                  |   18 
 VGAM-1.1-10/VGAM/man/prplot.Rd                    |    6 
 VGAM-1.1-10/VGAM/man/qrrvglm.control.Rd           |    5 
 VGAM-1.1-10/VGAM/man/qtplot.gumbel.Rd             |    5 
 VGAM-1.1-10/VGAM/man/qtplot.lmscreg.Rd            |    5 
 VGAM-1.1-10/VGAM/man/residualsvglm.Rd             |    2 
 VGAM-1.1-10/VGAM/man/rlplot.gevff.Rd              |    5 
 VGAM-1.1-10/VGAM/man/rrvglm-class.Rd              |   28 
 VGAM-1.1-10/VGAM/man/rrvglm.Rd                    |  129 -
 VGAM-1.1-10/VGAM/man/rrvglm.control.Rd            |  413 ++-
 VGAM-1.1-10/VGAM/man/rrvglm.optim.control.Rd      |    2 
 VGAM-1.1-10/VGAM/man/sinmad.Rd                    |   24 
 VGAM-1.1-10/VGAM/man/sloglink.Rd                  |only
 VGAM-1.1-10/VGAM/man/specialsvglm.Rd              |    6 
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 VGAM-1.1-10/VGAM/man/topple.Rd                    |    7 
 VGAM-1.1-10/VGAM/man/trplot.Rd                    |    3 
 VGAM-1.1-10/VGAM/man/trplot.qrrvglm.Rd            |    6 
 VGAM-1.1-10/VGAM/man/ucberk.Rd                    |    2 
 VGAM-1.1-10/VGAM/man/undocumented-methods.Rd      |   19 
 VGAM-1.1-10/VGAM/man/uninormal.Rd                 |   11 
 VGAM-1.1-10/VGAM/man/vcovvlm.Rd                   |   51 
 VGAM-1.1-10/VGAM/man/vgam-class.Rd                |    1 
 VGAM-1.1-10/VGAM/man/vgam.Rd                      |    2 
 VGAM-1.1-10/VGAM/man/vgam.control.Rd              |   18 
 VGAM-1.1-10/VGAM/man/vglm-class.Rd                |    1 
 VGAM-1.1-10/VGAM/man/vglm.Rd                      |    2 
 VGAM-1.1-10/VGAM/man/vglm.control.Rd              |    5 
 VGAM-1.1-10/VGAM/man/vglmff-class.Rd              |    1 
 VGAM-1.1-10/VGAM/man/vplot.profile.Rd             |    3 
 VGAM-1.1-10/VGAM/man/zeta.Rd                      |    2 
 VGAM-1.1-10/VGAM/man/zetaUC.Rd                    |    4 
 VGAM-1.1-10/VGAM/man/zipoisson.Rd                 |   26 
 VGAM-1.1-10/VGAM/src/rgam.f                       |   11 
 VGAM-1.1-10/VGAM/src/vgam.f                       |    8 
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 233 files changed, 9013 insertions(+), 8066 deletions(-)

More information about VGAM at CRAN
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