Tue, 21 May 2024

Package pathling updated to version 7.0.0 with previous version 6.4.2 dated 2023-12-20

Title: A Library for using 'Pathling'
Description: R API for 'Pathling', a tool for querying and transforming electronic health record data that is represented using the 'Fast Healthcare Interoperability Resources' (FHIR) standard - see <https://pathling.csiro.au/docs>.
Author: Australian e-Health Research Centre, CSIRO [cph, cre], Piotr Szul [aut], John Grimes [aut]
Maintainer: "Australian e-Health Research Centre, CSIRO" <ontoserver-support@csiro.au>

Diff between pathling versions 6.4.2 dated 2023-12-20 and 7.0.0 dated 2024-05-21

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Package packageRank updated to version 0.9.1 with previous version 0.9.0 dated 2024-04-23

Title: Computation and Visualization of Package Download Counts and Percentiles
Description: Compute and visualize the cross-sectional and longitudinal number and rank percentile of package downloads from Posit/RStudio's CRAN mirror.
Author: Peter Li [aut, cre]
Maintainer: Peter Li <lindbrook@gmail.com>

Diff between packageRank versions 0.9.0 dated 2024-04-23 and 0.9.1 dated 2024-05-21

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Package torch updated to version 0.13.0 with previous version 0.12.0 dated 2023-12-15

Title: Tensors and Neural Networks with 'GPU' Acceleration
Description: Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.
Author: Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], Hamada S. Badr [ctb], Sebastian Fischer [ctb], RStudio [cph]
Maintainer: Daniel Falbel <daniel@rstudio.com>

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New package EloOptimized with initial version 0.3.2
Package: EloOptimized
Title: Optimized Elo Rating Method for Obtaining Dominance Ranks
Version: 0.3.2
Date: 2024-05-21
Description: Provides an implementation of the maximum likelihood methods for deriving Elo scores as published in Foerster, Franz et al. (2016) <DOI:10.1038/srep35404>.
Depends: R (>= 3.3.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: dplyr, reshape2, BAMMtools, magrittr, methods, lubridate, rlang
URL: https://github.com/jtfeld/EloOptimized
BugReports: https://github.com/jtfeld/EloOptimized/issues
Suggests: knitr, rmarkdown, ggplot2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-05-21 18:01:40 UTC; jtf9
Author: Joseph Feldblum [aut, cre], Steffen Foerster [aut], Mathias Franz [aut]
Maintainer: Joseph Feldblum <jtf9@duke.edu>
Repository: CRAN
Date/Publication: 2024-05-21 22:20:02 UTC

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New package baorista with initial version 0.1.2
Package: baorista
Title: Bayesian Aoristic Analyses
Version: 0.1.2
Description: Provides an alternative approach to aoristic analyses for archaeological datasets by fitting Bayesian parametric growth models and non-parametric random-walk Intrinsic Conditional Autoregressive (ICAR) models on time frequency data (Crema (2024)<doi:10.1111/arcm.12984>). It handles event typo-chronology based timespans defined by start/end date as well as more complex user-provided vector of probabilities.
Depends: R (>= 3.5.0), nimble (>= 0.12.0)
Imports: stats,coda,graphics
Suggests: knitr, rmarkdown
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
Language: en-GB
NeedsCompilation: no
Packaged: 2024-05-19 14:30:25 UTC; erc
Author: Enrico Crema [aut, cre]
Maintainer: Enrico Crema <enrico.crema@gmail.com>
Repository: CRAN
Date/Publication: 2024-05-21 22:10:03 UTC

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Package opticut updated to version 0.1-3 with previous version 0.1-2 dated 2018-02-01

Title: Likelihood Based Optimal Partitioning and Indicator Species Analysis
Description: Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>. The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations.
Author: Peter Solymos [cre, aut] , Ermias T. Azeria [ctb]
Maintainer: Peter Solymos <psolymos@gmail.com>

Diff between opticut versions 0.1-2 dated 2018-02-01 and 0.1-3 dated 2024-05-21

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Package mlmhelpr updated to version 0.1.1 with previous version 0.1.0 dated 2022-11-04

Title: Multilevel/Mixed Model Helper Functions
Description: A collection of miscellaneous helper function for running multilevel/mixed models in 'lme4'. This package aims to provide functions to compute common tasks when estimating multilevel models such as computing the intraclass correlation and design effect, centering variables, estimating the proportion of variance explained at each level, pseudo-R squared, random intercept and slope reliabilities, tests for homogeneity of variance at level-1, and cluster robust and bootstrap standard errors. The tests and statistics reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049), Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012, ISBN:9781849202015).
Author: Louis Rocconi [aut, cre] , Anthony Schmidt [aut]
Maintainer: Louis Rocconi <lrocconi@utk.edu>

Diff between mlmhelpr versions 0.1.0 dated 2022-11-04 and 0.1.1 dated 2024-05-21

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Package gimme updated to version 0.7-17 with previous version 0.7-16 dated 2024-01-31

Title: Group Iterative Multiple Model Estimation
Description: Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.
Author: Stephanie Lane [aut, trl], Kathleen Gates [aut, cre, ccp], Zachary Fisher [aut], Cara Arizmendi [aut], Peter Molenaar [aut, ccp], Edgar Merkle [ctb], Michael Hallquist [ctb], Hallie Pike [ctb], Teague Henry [ctb], Kelly Duffy [ctb], Lan Luo [ctb], Ad [...truncated...]
Maintainer: Kathleen M Gates <gateskm@email.unc.edu>

Diff between gimme versions 0.7-16 dated 2024-01-31 and 0.7-17 dated 2024-05-21

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Package gausscov updated to version 1.1.3 with previous version 1.1.2 dated 2024-03-19

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 1.1.2 dated 2024-03-19 and 1.1.3 dated 2024-05-21

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Package copre updated to version 0.2.1 with previous version 0.2.0 dated 2023-01-31

Title: Tools for Nonparametric Martingale Posterior Sampling
Description: Performs Bayesian nonparametric density estimation using Martingale posterior distributions including the Copula Resampling (CopRe) algorithm. Also included are a Gibbs sampler for the marginal Gibbs-type mixture model and an extension to include full uncertainty quantification via a predictive sequence resampling (SeqRe) algorithm. The CopRe and SeqRe samplers generate random nonparametric distributions as output, leading to complete nonparametric inference on posterior summaries. Routines for calculating arbitrary functionals from the sampled distributions are included as well as an important algorithm for finding the number and location of modes, which can then be used to estimate the clusters in the data using, for example, k-means. Implements work developed in Moya B., Walker S. G. (2022). <doi:10.48550/arxiv.2206.08418>, Fong, E., Holmes, C., Walker, S. G. (2021) <doi:10.48550/arxiv.2103.15671>, and Escobar M. D., West, M. (1995) <doi:10.1080/01621459.1995.10476550 [...truncated...]
Author: Blake Moya [cre, aut], The University of Texas at Austin [cph, fnd]
Maintainer: Blake Moya <blakemoya@utexas.edu>

Diff between copre versions 0.2.0 dated 2023-01-31 and 0.2.1 dated 2024-05-21

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Package vegan updated to version 2.6-6.1 with previous version 2.6-6 dated 2024-05-14

Title: Community Ecology Package
Description: Ordination methods, diversity analysis and other functions for community and vegetation ecologists.
Author: Jari Oksanen [aut, cre], Gavin L. Simpson [aut], F. Guillaume Blanchet [aut], Roeland Kindt [aut], Pierre Legendre [aut], Peter R. Minchin [aut], R.B. O'Hara [aut], Peter Solymos [aut], M. Henry H. Stevens [aut], Eduard Szoecs [aut], Helene Wagner [a [...truncated...]
Maintainer: Jari Oksanen <jhoksane@gmail.com>

Diff between vegan versions 2.6-6 dated 2024-05-14 and 2.6-6.1 dated 2024-05-21

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Package mvtnorm updated to version 1.2-5 with previous version 1.2-4 dated 2023-11-27

Title: Multivariate Normal and t Distributions
Description: Computes multivariate normal and t probabilities, quantiles, random deviates, and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae parameterised by Cholesky factors of covariance or precision matrices are implemented for interval-censored and exact data, or a mix thereof. Score functions for these log-likelihoods are available. A class representing multiple lower triangular matrices and corresponding methods are part of this package.
Author: Alan Genz [aut], Frank Bretz [aut], Tetsuhisa Miwa [aut], Xuefei Mi [aut], Friedrich Leisch [ctb], Fabian Scheipl [ctb], Bjoern Bornkamp [ctb] , Martin Maechler [ctb] , Torsten Hothorn [aut, cre]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between mvtnorm versions 1.2-4 dated 2023-11-27 and 1.2-5 dated 2024-05-21

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Package keras3 updated to version 1.0.0 with previous version 0.2.0 dated 2024-04-18

Title: R Interface to 'Keras'
Description: Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.
Author: Tomasz Kalinowski [aut, cph, cre], Daniel Falbel [ctb, cph], JJ Allaire [aut, cph], Francois Chollet [aut, cph], Posit Software, PBC [cph, fnd], Google [cph, fnd], Yuan Tang [ctb, cph] , Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph], Sigri [...truncated...]
Maintainer: Tomasz Kalinowski <tomasz@posit.co>

Diff between keras3 versions 0.2.0 dated 2024-04-18 and 1.0.0 dated 2024-05-21

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 keras3-1.0.0/keras3/NAMESPACE                                                       |   14 
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 keras3-1.0.0/keras3/R/install.R                                                     |    4 
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 keras3-1.0.0/keras3/R/layers-core.R                                                 |    7 
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 keras3-1.0.0/keras3/R/ops-image.R                                                   |   75 
 keras3-1.0.0/keras3/R/ops-linalg.R                                                  |   38 
 keras3-1.0.0/keras3/R/ops-nn.R                                                      |   38 
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 keras3-1.0.0/keras3/R/optimizers-schedules.R                                        |    2 
 keras3-1.0.0/keras3/R/optimizers.R                                                  |    2 
 keras3-1.0.0/keras3/R/package.R                                                     |    1 
 keras3-1.0.0/keras3/R/py-classes.R                                                  |    8 
 keras3-1.0.0/keras3/R/r-utils.R                                                     |    4 
 keras3-1.0.0/keras3/R/random.R                                                      |    4 
 keras3-1.0.0/keras3/R/reexports.R                                                   |    9 
 keras3-1.0.0/keras3/R/shape.R                                                       |   52 
 keras3-1.0.0/keras3/R/utils.R                                                       |   20 
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 keras3-1.0.0/keras3/man/Constraint.Rd                                               |    1 
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 keras3-1.0.0/keras3/man/Loss.Rd                                                     |    2 
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 keras3-1.0.0/keras3/man/get_file.Rd                                                 |    2 
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 keras3-1.0.0/keras3/man/image_array_save.Rd                                         |    3 
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 keras3-1.0.0/keras3/man/image_from_array.Rd                                         |    3 
 keras3-1.0.0/keras3/man/image_load.Rd                                               |    3 
 keras3-1.0.0/keras3/man/image_smart_resize.Rd                                       |    3 
 keras3-1.0.0/keras3/man/image_to_array.Rd                                           |    3 
 keras3-1.0.0/keras3/man/install_keras.Rd                                            |    4 
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 keras3-1.0.0/keras3/man/layer_cropping_1d.Rd                                        |    2 
 keras3-1.0.0/keras3/man/layer_embedding.Rd                                          |    5 
 keras3-1.0.0/keras3/man/layer_feature_space.Rd                                      |    2 
 keras3-1.0.0/keras3/man/layer_random_zoom.Rd                                        |    2 
 keras3-1.0.0/keras3/man/layer_resizing.Rd                                           |   15 
 keras3-1.0.0/keras3/man/layer_tfsm.Rd                                               |    4 
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 keras3-1.0.0/keras3/man/loss_categorical_hinge.Rd                                   |    2 
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 keras3-1.0.0/keras3/man/loss_ctc.Rd                                                 |   48 
 keras3-1.0.0/keras3/man/loss_dice.Rd                                                |    2 
 keras3-1.0.0/keras3/man/loss_hinge.Rd                                               |    2 
 keras3-1.0.0/keras3/man/loss_huber.Rd                                               |    2 
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 keras3-1.0.0/keras3/man/loss_log_cosh.Rd                                            |    2 
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 keras3-1.0.0/keras3/man/loss_poisson.Rd                                             |    2 
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 keras3-1.0.0/keras3/man/loss_squared_hinge.Rd                                       |    2 
 keras3-1.0.0/keras3/man/loss_tversky.Rd                                             |only
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 keras3-1.0.0/keras3/man/metric_hinge.Rd                                             |    2 
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 keras3-1.0.0/keras3/man/metric_log_cosh.Rd                                          |    2 
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 keras3-1.0.0/keras3/man/metric_poisson.Rd                                           |    2 
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 keras3-1.0.0/keras3/man/metric_squared_hinge.Rd                                     |    2 
 keras3-1.0.0/keras3/man/normalize.Rd                                                |    2 
 keras3-1.0.0/keras3/man/op_abs.Rd                                                   |   10 
 keras3-1.0.0/keras3/man/op_add.Rd                                                   |   10 
 keras3-1.0.0/keras3/man/op_all.Rd                                                   |   12 
 keras3-1.0.0/keras3/man/op_any.Rd                                                   |   10 
 keras3-1.0.0/keras3/man/op_append.Rd                                                |   10 
 keras3-1.0.0/keras3/man/op_arange.Rd                                                |   12 
 keras3-1.0.0/keras3/man/op_arccos.Rd                                                |   10 
 keras3-1.0.0/keras3/man/op_arccosh.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_arcsin.Rd                                                |   10 
 keras3-1.0.0/keras3/man/op_arcsinh.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_arctan.Rd                                                |   10 
 keras3-1.0.0/keras3/man/op_arctan2.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_arctanh.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_argmax.Rd                                                |   15 
 keras3-1.0.0/keras3/man/op_argmin.Rd                                                |   15 
 keras3-1.0.0/keras3/man/op_argsort.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_array.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_average.Rd                                               |   23 
 keras3-1.0.0/keras3/man/op_average_pool.Rd                                          |    8 
 keras3-1.0.0/keras3/man/op_batch_normalization.Rd                                   |    8 
 keras3-1.0.0/keras3/man/op_binary_crossentropy.Rd                                   |    8 
 keras3-1.0.0/keras3/man/op_bincount.Rd                                              |   10 
 keras3-1.0.0/keras3/man/op_broadcast_to.Rd                                          |   10 
 keras3-1.0.0/keras3/man/op_cast.Rd                                                  |    9 
 keras3-1.0.0/keras3/man/op_categorical_crossentropy.Rd                              |    8 
 keras3-1.0.0/keras3/man/op_ceil.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_cholesky.Rd                                              |    9 
 keras3-1.0.0/keras3/man/op_clip.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_concatenate.Rd                                           |   10 
 keras3-1.0.0/keras3/man/op_cond.Rd                                                  |    7 
 keras3-1.0.0/keras3/man/op_conj.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_conv.Rd                                                  |    8 
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 keras3-1.0.0/keras3/man/op_convert_to_numpy.Rd                                      |    7 
 keras3-1.0.0/keras3/man/op_convert_to_tensor.Rd                                     |    7 
 keras3-1.0.0/keras3/man/op_copy.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_correlate.Rd                                             |   10 
 keras3-1.0.0/keras3/man/op_cos.Rd                                                   |   10 
 keras3-1.0.0/keras3/man/op_cosh.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_count_nonzero.Rd                                         |   10 
 keras3-1.0.0/keras3/man/op_cross.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_ctc_decode.Rd                                            |only
 keras3-1.0.0/keras3/man/op_ctc_loss.Rd                                              |    8 
 keras3-1.0.0/keras3/man/op_cumprod.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_cumsum.Rd                                                |   10 
 keras3-1.0.0/keras3/man/op_custom_gradient.Rd                                       |    7 
 keras3-1.0.0/keras3/man/op_depthwise_conv.Rd                                        |    8 
 keras3-1.0.0/keras3/man/op_det.Rd                                                   |    9 
 keras3-1.0.0/keras3/man/op_diag.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_diagonal.Rd                                              |   10 
 keras3-1.0.0/keras3/man/op_diff.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_digitize.Rd                                              |   10 
 keras3-1.0.0/keras3/man/op_divide.Rd                                                |   16 
 keras3-1.0.0/keras3/man/op_divide_no_nan.Rd                                         |   10 
 keras3-1.0.0/keras3/man/op_dot.Rd                                                   |   10 
 keras3-1.0.0/keras3/man/op_eig.Rd                                                   |    9 
 keras3-1.0.0/keras3/man/op_eigh.Rd                                                  |only
 keras3-1.0.0/keras3/man/op_einsum.Rd                                                |   40 
 keras3-1.0.0/keras3/man/op_elu.Rd                                                   |    8 
 keras3-1.0.0/keras3/man/op_empty.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_equal.Rd                                                 |   12 
 keras3-1.0.0/keras3/man/op_erf.Rd                                                   |    7 
 keras3-1.0.0/keras3/man/op_erfinv.Rd                                                |    7 
 keras3-1.0.0/keras3/man/op_exp.Rd                                                   |   10 
 keras3-1.0.0/keras3/man/op_expand_dims.Rd                                           |   10 
 keras3-1.0.0/keras3/man/op_expm1.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_extract_sequences.Rd                                     |    7 
 keras3-1.0.0/keras3/man/op_eye.Rd                                                   |   10 
 keras3-1.0.0/keras3/man/op_fft.Rd                                                   |    7 
 keras3-1.0.0/keras3/man/op_fft2.Rd                                                  |    7 
 keras3-1.0.0/keras3/man/op_flip.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_floor.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_floor_divide.Rd                                          |   16 
 keras3-1.0.0/keras3/man/op_fori_loop.Rd                                             |    7 
 keras3-1.0.0/keras3/man/op_full.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_full_like.Rd                                             |   10 
 keras3-1.0.0/keras3/man/op_gelu.Rd                                                  |    8 
 keras3-1.0.0/keras3/man/op_get_item.Rd                                              |   10 
 keras3-1.0.0/keras3/man/op_greater.Rd                                               |   12 
 keras3-1.0.0/keras3/man/op_greater_equal.Rd                                         |   12 
 keras3-1.0.0/keras3/man/op_hard_sigmoid.Rd                                          |    8 
 keras3-1.0.0/keras3/man/op_hard_silu.Rd                                             |    8 
 keras3-1.0.0/keras3/man/op_hstack.Rd                                                |   10 
 keras3-1.0.0/keras3/man/op_identity.Rd                                              |   10 
 keras3-1.0.0/keras3/man/op_imag.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_image_affine_transform.Rd                                |    9 
 keras3-1.0.0/keras3/man/op_image_crop.Rd                                            |    9 
 keras3-1.0.0/keras3/man/op_image_extract_patches.Rd                                 |    9 
 keras3-1.0.0/keras3/man/op_image_map_coordinates.Rd                                 |    9 
 keras3-1.0.0/keras3/man/op_image_pad.Rd                                             |    9 
 keras3-1.0.0/keras3/man/op_image_resize.Rd                                          |   33 
 keras3-1.0.0/keras3/man/op_image_rgb_to_grayscale.Rd                                |only
 keras3-1.0.0/keras3/man/op_in_top_k.Rd                                              |    7 
 keras3-1.0.0/keras3/man/op_inv.Rd                                                   |    9 
 keras3-1.0.0/keras3/man/op_irfft.Rd                                                 |    7 
 keras3-1.0.0/keras3/man/op_is_tensor.Rd                                             |    7 
 keras3-1.0.0/keras3/man/op_isclose.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_isfinite.Rd                                              |   10 
 keras3-1.0.0/keras3/man/op_isinf.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_isnan.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_istft.Rd                                                 |    7 
 keras3-1.0.0/keras3/man/op_leaky_relu.Rd                                            |    8 
 keras3-1.0.0/keras3/man/op_less.Rd                                                  |   12 
 keras3-1.0.0/keras3/man/op_less_equal.Rd                                            |   12 
 keras3-1.0.0/keras3/man/op_linspace.Rd                                              |   12 
 keras3-1.0.0/keras3/man/op_log.Rd                                                   |   10 
 keras3-1.0.0/keras3/man/op_log10.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_log1p.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_log2.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_log_sigmoid.Rd                                           |    8 
 keras3-1.0.0/keras3/man/op_log_softmax.Rd                                           |    8 
 keras3-1.0.0/keras3/man/op_logaddexp.Rd                                             |   10 
 keras3-1.0.0/keras3/man/op_logical_and.Rd                                           |   10 
 keras3-1.0.0/keras3/man/op_logical_not.Rd                                           |   10 
 keras3-1.0.0/keras3/man/op_logical_or.Rd                                            |   10 
 keras3-1.0.0/keras3/man/op_logical_xor.Rd                                           |   10 
 keras3-1.0.0/keras3/man/op_logspace.Rd                                              |   12 
 keras3-1.0.0/keras3/man/op_logsumexp.Rd                                             |    7 
 keras3-1.0.0/keras3/man/op_lu_factor.Rd                                             |    9 
 keras3-1.0.0/keras3/man/op_matmul.Rd                                                |   10 
 keras3-1.0.0/keras3/man/op_max.Rd                                                   |   14 
 keras3-1.0.0/keras3/man/op_max_pool.Rd                                              |    8 
 keras3-1.0.0/keras3/man/op_maximum.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_mean.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_median.Rd                                                |   10 
 keras3-1.0.0/keras3/man/op_meshgrid.Rd                                              |   10 
 keras3-1.0.0/keras3/man/op_min.Rd                                                   |   14 
 keras3-1.0.0/keras3/man/op_minimum.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_mod.Rd                                                   |   16 
 keras3-1.0.0/keras3/man/op_moments.Rd                                               |    8 
 keras3-1.0.0/keras3/man/op_moveaxis.Rd                                              |   10 
 keras3-1.0.0/keras3/man/op_multi_hot.Rd                                             |    8 
 keras3-1.0.0/keras3/man/op_multiply.Rd                                              |   16 
 keras3-1.0.0/keras3/man/op_nan_to_num.Rd                                            |   41 
 keras3-1.0.0/keras3/man/op_ndim.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_negative.Rd                                              |   16 
 keras3-1.0.0/keras3/man/op_nonzero.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_norm.Rd                                                  |    9 
 keras3-1.0.0/keras3/man/op_normalize.Rd                                             |    8 
 keras3-1.0.0/keras3/man/op_not_equal.Rd                                             |   12 
 keras3-1.0.0/keras3/man/op_one_hot.Rd                                               |    8 
 keras3-1.0.0/keras3/man/op_ones.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_ones_like.Rd                                             |   10 
 keras3-1.0.0/keras3/man/op_outer.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_pad.Rd                                                   |   10 
 keras3-1.0.0/keras3/man/op_power.Rd                                                 |   16 
 keras3-1.0.0/keras3/man/op_prod.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_psnr.Rd                                                  |only
 keras3-1.0.0/keras3/man/op_qr.Rd                                                    |    7 
 keras3-1.0.0/keras3/man/op_quantile.Rd                                              |   10 
 keras3-1.0.0/keras3/man/op_ravel.Rd                                                 |   10 
 keras3-1.0.0/keras3/man/op_real.Rd                                                  |   10 
 keras3-1.0.0/keras3/man/op_reciprocal.Rd                                            |   10 
 keras3-1.0.0/keras3/man/op_relu.Rd                                                  |    8 
 keras3-1.0.0/keras3/man/op_relu6.Rd                                                 |    8 
 keras3-1.0.0/keras3/man/op_repeat.Rd                                                |   10 
 keras3-1.0.0/keras3/man/op_reshape.Rd                                               |   10 
 keras3-1.0.0/keras3/man/op_rfft.Rd                                                  |    7 
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More information about keras3 at CRAN
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Package EdSurvey updated to version 4.0.6 with previous version 4.0.4 dated 2023-10-27

Title: Analysis of NCES Education Survey and Assessment Data
Description: Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/>, including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) <https://www.oecd.org/>, including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) <https://www.iea.nl/>, including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education S [...truncated...]
Author: Paul Bailey [aut, cre] , Ahmad Emad [aut], Huade Huo [aut] , Michael Lee [aut] , Yuqi Liao [aut] , Alex Lishinski [aut] , Trang Nguyen [aut] , Qingshu Xie [aut], Jiao Yu [aut], Ting Zhang [aut] , Eric Buehler [aut] , Sun-joo Lee [aut], Blue Webb [aut [...truncated...]
Maintainer: Paul Bailey <pbailey@air.org>

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Package fsemipar updated to version 1.1.1 with previous version 1.1.0 dated 2024-04-27

Title: Estimation, Variable Selection and Prediction for Functional Semiparametric Models
Description: Routines for the estimation or simultaneous estimation and variable selection in several functional semiparametric models with scalar responses are provided. These models include the functional single-index model, the semi-functional partial linear model, and the semi-functional partial linear single-index model. Additionally, the package offers algorithms for handling scalar covariates with linear effects that originate from the discretization of a curve. This functionality is applicable in the context of the linear model, the multi-functional partial linear model, and the multi-functional partial linear single-index model.
Author: German Aneiros [aut], Silvia Novo [aut, cre]
Maintainer: Silvia Novo <snovo@est-econ.uc3m.es>

Diff between fsemipar versions 1.1.0 dated 2024-04-27 and 1.1.1 dated 2024-05-21

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New package eummd with initial version 0.1.9
Package: eummd
Title: Efficient Univariate Maximum Mean Discrepancy
Version: 0.1.9
Date: 2024-05-18
Description: Computes maximum mean discrepancy two-sample test for univariate data using the Laplacian kernel, as described in Bodenham and Kawahara (2023) <doi:10.1007/s11222-023-10271-x>. The p-value is computed using permutations. Also includes implementation for computing the robust median difference statistic 'Q_n' from Croux and Rousseeuw (1992) <doi:10.1007/978-3-662-26811-7_58> based on Johnson and Mizoguchi (1978) <doi:10.1137/0207013>.
Depends: R (>= 4.1.0), Rcpp (>= 1.0.0)
License: GPL-2 | GPL-3
Encoding: UTF-8
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2024-05-18 17:06:25 UTC; dean
Author: Dean Bodenham [aut, cre]
Maintainer: Dean Bodenham <deanbodenhampkgs@gmail.com>
Repository: CRAN
Date/Publication: 2024-05-21 16:20:02 UTC

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Package ClustBlock updated to version 4.0.0 with previous version 3.2.0 dated 2023-08-30

Title: Clustering of Datasets
Description: Hierarchical and partitioning algorithms to cluster blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data. Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available. Clustering of rows in multi-block context (notably with ClusMB stra [...truncated...]
Author: Fabien Llobell [aut, cre] , Evelyne Vigneau [ctb] , Veronique Cariou [ctb] , El Mostafa Qannari [ctb]
Maintainer: Fabien Llobell <fabienllobellresearch@gmail.com>

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New package tabulapdf with initial version 1.0.5-3
Package: tabulapdf
Title: Extract Tables from PDF Documents
Description: Bindings for the 'Tabula' <https://tabula.technology/> 'Java' library, which can extract tables from PDF files. This tool can reduce time and effort in data extraction processes in fields like investigative journalism. It allows for automatic and manual table extraction, the latter facilitated through a 'Shiny' interface, enabling manual areas selection\ with a computer mouse for data retrieval.
Version: 1.0.5-3
License: Apache License (>= 2)
URL: https://docs.ropensci.org/tabulapdf/ (website) https://github.com/ropensci/tabulapdf/
BugReports: https://github.com/ropensci/tabulapdf/issues/
Imports: png, readr, rJava, tools, utils
Suggests: graphics, grDevices, knitr, miniUI, shiny, testthat, rmarkdown, covr
SystemRequirements: Java (>= 7.0): openjdk-11-jdk (deb), java-11-openjdk.x86_64 (rpm), openjdk@11 (brew)
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-05-20 21:23:33 UTC; pacha
Author: Thomas J. Leeper [aut] , Mauricio Vargas Sepulveda [aut, cre] , Tom Paskhalis [aut] , Manuel Aristaran [ctb], David Gohel [ctb] , Lincoln Mullen [ctb]
Maintainer: Mauricio Vargas Sepulveda <m.sepulveda@mail.utoronto.ca>
Repository: CRAN
Date/Publication: 2024-05-21 15:20:03 UTC

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Package StepReg updated to version 1.5.1 with previous version 1.5.0 dated 2024-03-21

Title: Stepwise Regression Analysis
Description: The stepwise regression analysis is a statistical technique used to identify a subset of predictor variables essential for constructing predictive models. This package performs stepwise regression analysis across various regression models such as linear, logistic, Cox proportional hazards, Poisson, Gamma, and negative binomial regression. It incorporates diverse stepwise regression algorithms like forward selection, backward elimination, and bidirectional elimination alongside the best subset method. Additionally, it offers a wide range of selection criteria, including Akaike Information Criterion (AIC), Sawa Bayesian Information Criterion (BIC), and Significance Levels (SL). We validated the output accuracy of StepReg using public datasets within the SAS software environment. To facilitate efficient model comparison and selection, StepReg allows for multiple strategies and selection metrics to be executed in a single function call. Moreover, StepReg integrates a Shiny application for [...truncated...]
Author: Junhui Li [cre] , Junhui Li [aut] , Kai Hu [aut], Xiaohuan Lu [aut], Kun Cheng [ctb], Sushmita Nayak [ctb], Cesar Bautista Sotelo [ctb], Michael Lodato [ctb], Robert H Brown [ctb], Wenxin Liu [aut], Lihua Julie Zhu [aut]
Maintainer: Junhui Li <junhui.li11@umassmed.edu>

Diff between StepReg versions 1.5.0 dated 2024-03-21 and 1.5.1 dated 2024-05-21

 StepReg-1.5.0/StepReg/R/zzz.R                                                     |only
 StepReg-1.5.1/StepReg/DESCRIPTION                                                 |   18 
 StepReg-1.5.1/StepReg/MD5                                                         |   57 -
 StepReg-1.5.1/StepReg/NAMESPACE                                                   |   15 
 StepReg-1.5.1/StepReg/R/StepRegShinyApp.R                                         |    8 
 StepReg-1.5.1/StepReg/R/plot.StepReg.R                                            |  212 +++-
 StepReg-1.5.1/StepReg/R/print.StepReg.R                                           |   21 
 StepReg-1.5.1/StepReg/R/report.R                                                  |   38 
 StepReg-1.5.1/StepReg/R/stepwise.R                                                |   81 -
 StepReg-1.5.1/StepReg/R/stepwiseUtils.R                                           |  249 +++--
 StepReg-1.5.1/StepReg/R/validateUtils.R                                           |   23 
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 StepReg-1.5.1/StepReg/inst/shiny/report.Rmd                                       |   27 
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 StepReg-1.5.1/StepReg/man/report.Rd                                               |    4 
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New package rjdworkspace with initial version 1.1.7
Package: rjdworkspace
Title: Manipulate 'JDemetra+' Workspaces
Version: 1.1.7
Description: Set of tools to manipulate the 'JDemetra+' workspaces. Based on the 'RJDemetra' package (which interfaces with version 2 of the 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially recommended to the members of the European Statistical System (ESS) and the European System of Central Banks). This package provides access to additional workspace manipulation functions such as metadata manipulation, raw paths and wrangling of several workspaces simultaneously. These additional functionalities are useful as part of a CVS data production chain.
Depends: R (>= 3.1.1)
Imports: rJava (>= 0.9-8), RJDemetra, XML
License: EUPL
URL: https://github.com/InseeFrLab/rjdworkspace
BugReports: https://github.com/InseeFrLab/rjdworkspace/issues
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-05-20 14:29:48 UTC; UTZK0M
Author: Tanguy Barthelemy [aut, cre, art], Alain Quartier-la-Tente [aut] , Institut national de la statistique et des etudes economiques [cph] , Anna Smyk [aut]
Maintainer: Tanguy Barthelemy <tanguy.barthelemy@insee.fr>
Repository: CRAN
Date/Publication: 2024-05-21 15:10:02 UTC

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Package reticulate updated to version 1.37.0 with previous version 1.36.1 dated 2024-04-22

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

Diff between reticulate versions 1.36.1 dated 2024-04-22 and 1.37.0 dated 2024-05-21

 reticulate-1.36.1/reticulate/inst/python/rpytools/signals.py                            |only
 reticulate-1.37.0/reticulate/DESCRIPTION                                                |    6 
 reticulate-1.37.0/reticulate/MD5                                                        |   45 
 reticulate-1.37.0/reticulate/NEWS.md                                                    |   28 
 reticulate-1.37.0/reticulate/R/RcppExports.R                                            |   16 
 reticulate-1.37.0/reticulate/R/conda.R                                                  |    2 
 reticulate-1.37.0/reticulate/R/package.R                                                |   18 
 reticulate-1.37.0/reticulate/R/python.R                                                 |   81 
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 reticulate-1.37.0/reticulate/src/libpython.cpp                                          |   19 
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 reticulate-1.37.0/reticulate/src/tinythread.h                                           |    3 
 reticulate-1.37.0/reticulate/tests/testthat/_snaps/python-knitr-engine/test-chunking.md |    2 
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 reticulate-1.37.0/reticulate/tests/testthat/test-interrupts.R                           |  171 +
 reticulate-1.37.0/reticulate/tests/testthat/test-python-scipy-sparse-matrix.R           |   10 
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Package polyCub updated to version 0.9.1 with previous version 0.9.0 dated 2023-10-25

Title: Cubature over Polygonal Domains
Description: Numerical integration of continuously differentiable functions f(x,y) over simple closed polygonal domains. The following cubature methods are implemented: product Gauss cubature (Sommariva and Vianello, 2007, <doi:10.1007/s10543-007-0131-2>), the simple two-dimensional midpoint rule (wrapping 'spatstat.geom' functions), and adaptive cubature for radially symmetric functions via line integrate() along the polygon boundary (Meyer and Held, 2014, <doi:10.1214/14-AOAS743>, Supplement B). For simple integration along the axes, the 'cubature' package is more appropriate.
Author: Sebastian Meyer [aut, cre, trl] , Leonhard Held [ths], Michael Hoehle [ths]
Maintainer: Sebastian Meyer <seb.meyer@fau.de>

Diff between polyCub versions 0.9.0 dated 2023-10-25 and 0.9.1 dated 2024-05-21

 DESCRIPTION                |   14 
 MD5                        |   40 -
 NEWS.md                    |    7 
 R/polyCub.R                |    5 
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 man/polyCub.exact.Gauss.Rd |    4 
 man/polyCub.iso.Rd         |    4 
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 tests/test-polyCub.R       |    2 
 tests/test-regression.R    |    2 
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Package pmartR updated to version 2.4.5 with previous version 2.4.4 dated 2024-02-27

Title: Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data
Description: Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups. Implements methods described in: Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>. Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>. Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>. Matzke et al. (2013) <doi:10.1002/pmic.201200269>. Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>. Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.
Author: Lisa Bramer [aut, cre], Kelly Stratton [aut], Daniel Claborne [aut], Evan Glasscock [ctb], Rachel Richardson [ctb], David Degnan [ctb], Evan Martin [ctb]
Maintainer: Lisa Bramer <lisa.bramer@pnnl.gov>

Diff between pmartR versions 2.4.4 dated 2024-02-27 and 2.4.5 dated 2024-05-21

 DESCRIPTION                                 |    8 +-
 MD5                                         |   52 +++++++++---------
 NAMESPACE                                   |    1 
 R/applyFilt.R                               |    8 --
 R/as.omicsData.R                            |    4 -
 R/group_designation.R                       |    6 +-
 R/imd_anova.R                               |   63 +---------------------
 R/normalize_zero_one.R                      |only
 R/plot_fns.R                                |   79 +++++++++++++++++-----------
 R/protein_quant.R                           |   53 +++++++++---------
 R/report_dataRes.R                          |    2 
 R/seqData_wrappers.R                        |    8 ++
 README.md                                   |    2 
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 tests/testthat/test_as_trelliData_edata.R   |    1 
 tests/testthat/test_as_trelliData_summary.R |    2 
 tests/testthat/test_dispersion_est.R        |    6 +-
 tests/testthat/test_edata_structure.R       |    8 +-
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 tests/testthat/test_group_designation.R     |    4 -
 tests/testthat/test_imd_anova.R             |   42 ++++++++++++++
 tests/testthat/test_plots.R                 |    2 
 tests/testthat/test_quant_protein.R         |    6 +-
 tests/testthat/test_trelli_pvalue_filter.R  |    2 
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New package infoelectoral with initial version 1.0.1
Package: infoelectoral
Title: Download Spanish Election Results
Version: 1.0.1
Maintainer: Hector Meleiro <hmeleiros@gmail.com>
Description: Download official election results for Spain at polling station, municipality and province level from the Ministry of Interior (<https://infoelectoral.interior.gob.es/es/elecciones-celebradas/area-de-descargas/>), format them and import them to the R environment.
Depends: R (>= 3.5.0)
License: GPL-2
URL: https://github.com/rOpenSpain/infoelectoral
Encoding: UTF-8
LazyData: true
Imports: dplyr (>= 1.0.0), stringr (>= 1.0.0)
Suggests: mapSpain, tmap, tidyr, preferably, knitr, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-05-20 09:32:29 UTC; hmele
Author: Hector Meleiro [aut, cre]
Repository: CRAN
Date/Publication: 2024-05-21 15:50:12 UTC

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Package COINr updated to version 1.1.14 with previous version 1.1.7 dated 2023-10-09

Title: Composite Indicator Construction and Analysis
Description: A comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.
Author: William Becker [aut, cre, cph]
Maintainer: William Becker <william.becker@bluefoxdata.eu>

Diff between COINr versions 1.1.7 dated 2023-10-09 and 1.1.14 dated 2024-05-21

 DESCRIPTION                        |   10 
 MD5                                |  109 ++---
 NAMESPACE                          |    5 
 NEWS.md                            |   29 +
 R/aggregate.R                      |  260 ++++++++++--
 R/audit_tools.R                    |    2 
 R/compare.R                        |   72 +++
 R/custom.R                         |only
 R/impute.R                         |  207 ++++++++--
 R/new_coin.R                       |   43 +-
 R/normalise.R                      |  234 +++++++++--
 R/results.R                        |    6 
 R/treat.R                          |   77 +++
 R/utils.R                          |   33 +
 R/write_to_coins.R                 |    4 
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 man/n_minmax.Rd                    |    6 
 man/n_scaled.Rd                    |   12 
 man/n_zscore.Rd                    |    6 
 man/new_coin.Rd                    |    6 
 tests/testthat/test-Impute.R       |   45 ++
 tests/testthat/test-aggregate.R    |  121 +++++
 tests/testthat/test-custom.R       |only
 tests/testthat/test-impute_panel.R |   42 ++
 tests/testthat/test-normalise.R    |  102 ++++
 tests/testthat/test-treat.R        |   13 
 vignettes/aggregate.Rmd            |   49 +-
 vignettes/imputation.Rmd           |    9 
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 59 files changed, 2889 insertions(+), 639 deletions(-)

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New package scorematchingad with initial version 0.0.60
Package: scorematchingad
Title: Score Matching Estimation by Automatic Differentiation
Version: 0.0.60
Description: Hyvärinen's score matching (Hyvärinen, 2005) <https://jmlr.org/papers/v6/hyvarinen05a.html> is a useful estimation technique when the normalising constant for a probability distribution is difficult to compute. This package implements score matching estimators using automatic differentiation in the 'CppAD' library <https://github.com/coin-or/CppAD> and is designed for quickly implementing score matching estimators for new models. Also available is general robustification (Windham, 1995) <https://www.jstor.org/stable/2346159>. Already in the package are estimators for directional distributions (Mardia, Kent and Laha, 2016) <doi:10.48550/arXiv.1604.08470> and the flexible Polynomially-Tilted Pairwise Interaction model for compositional data. The latter estimators perform well when there are zeros in the compositions (Scealy and Wood, 2023) <doi:10.1080/01621459.2021.2016422>, even many zeros (Scealy, Hingee, Kent, and Wood, 2024) <doi:10.1007/s11222-024-1 [...truncated...]
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Suggests: testthat, ks, movMF, cubature, simdd, numDeriv
Enhances: RcppEigen (>= 0.3.3.7), RcppXPtrUtils
Depends: R (>= 3.5.0)
Imports: MCMCpack, optimx, FixedPoint, Rdpack, Rcpp (>= 1.0.9), methods, stats, R6, utils, rlang, ellipsis
LinkingTo: Rcpp (>= 1.0.9), RcppEigen (>= 0.3.3.7)
NeedsCompilation: yes
Packaged: 2024-05-20 02:01:18 UTC; kassel
Author: Kassel Liam Hingee [aut, cre] , Janice Scealy [aut] , Bradley M. Bell [cph]
Maintainer: Kassel Liam Hingee <kassel.hingee@anu.edu.au>
Repository: CRAN
Date/Publication: 2024-05-21 14:50:03 UTC

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Package rflsgen updated to version 1.2.2 with previous version 1.2.1 dated 2022-11-17

Title: Neutral Landscape Generator with Targets on Landscape Indices
Description: Interface to the 'flsgen' neutral landscape generator <https://github.com/dimitri-justeau/flsgen>. It allows to - Generate fractal terrain; - Generate landscape structures satisfying user targets over landscape indices; - Generate landscape raster from landscape structures.
Author: Dimitri Justeau-Allaire, Gregoire Blanchard, Thomas Ibanez, Xavier Lorca, Ghislain Vieilledent, Philippe Birnbaum
Maintainer: Dimitri Justeau-Allaire <dimitri.justeau@gmail.com>

Diff between rflsgen versions 1.2.1 dated 2022-11-17 and 1.2.2 dated 2024-05-21

 DESCRIPTION                                                  |    8 +-
 MD5                                                          |   30 +++++------
 NEWS.md                                                      |    6 ++
 R/flsgen_generate.R                                          |    2 
 build/vignette.rds                                           |binary
 inst/CITATION                                                |   16 ++---
 inst/doc/FAQ.pdf                                             |binary
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 tests/testthat/test_generate_landscape_with_square_patches.R |    2 
 vignettes/UseCase2.Rmd                                       |    2 
 vignettes/UseCase3.Rmd                                       |    7 +-
 16 files changed, 45 insertions(+), 37 deletions(-)

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New package pql with initial version 0.1.0
Package: pql
Title: A Partitioned Quasi-Likelihood for Distributed Statistical Inference
Version: 0.1.0
Author: Guangbao Guo [aut, cre], Jiarui Li [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>
Description: In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: parallel,pracma
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2024-05-16 11:30:36 UTC; LJR
Repository: CRAN
Date/Publication: 2024-05-21 14:20:02 UTC

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New package MPCR with initial version 1.1.1
Package: MPCR
Title: Multi- And Mixed-Precision Computations
Version: 1.1.1
Date: 2024-05-01
Author: David Helmy [aut], Sameh Abdulah [cre], KAUST King Abdullah University of Science and Technology [fnd, cph]
Maintainer: Sameh Abdulah <sameh.abdulah@kaust.edu.sa>
Description: Designed for multi- and mixed-precision computations, accommodating 64-bit and 32-bit data structures. This flexibility enables fast execution across various applications. The package enhances performance by optimizing operations in both precision levels, which is achieved by integrating with high-speed 'BLAS' and 'LAPACK' libraries like 'MKL' and 'OpenBLAS'. Including a 32-bit option caters to applications where high precision is unnecessary, accelerating computational processes whenever feasible. The package also provides support for tile-based algorithms in three linear algebra operations: CHOL(), TRSM(), and GEMM(). The tile-based algorithm splits the matrix into smaller tiles, facilitating parallelization through a predefined Directed Acyclic Graph (DAG) for each operation. Enabling 'OpenMP' enhances the efficiency of these operations, leveraging multi-core parallelism. In this case, 'MPCR' facilitates mixed-precision execution by permitting varying precision levels for different [...truncated...]
License: GPL (>= 3)
Imports: methods, Rcpp (>= 1.0.9)
Depends: R (>= 3.6.0)
SystemRequirements: CMake (>= 3.19.4) , C++ (>= 11) , git (>= 2.0.0)
NeedsCompilation: yes
OS_type: unix
URL: https://github.com/stsds/MPCR
BugReports: https://github.com/stsds/MPCR/issues
Packaged: 2024-05-01 09:17:54 UTC; david
Repository: CRAN
Date/Publication: 2024-05-21 14:10:06 UTC

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New package ggdaynight with initial version 0.1.1
Package: ggdaynight
Title: Add Day/Night Patterns to 'ggplot2' Plots
Version: 0.1.1
Description: It provides a custom 'ggplot2' geom to add day/night patterns to plots. It visually distinguishes daytime and nighttime periods. It is useful for visualizing data that spans multiple days and for highlighting diurnal patterns.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
URL: https://github.com/GabrielSlPires/ggdaynight
BugReports: https://github.com/GabrielSlPires/ggdaynight/issues
Imports: ggplot2, grid
Depends: R (>= 2.10)
Suggests: testthat (>= 3.0.0), vdiffr
NeedsCompilation: no
Packaged: 2024-05-18 19:44:30 UTC; gabriel
Author: Gabriel S. Pires [aut, cre, cph]
Maintainer: Gabriel S. Pires <gabriel.slpires@gmail.com>
Repository: CRAN
Date/Publication: 2024-05-21 14:10:02 UTC

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New package eq5dsuite with initial version 1.0.0
Package: eq5dsuite
Title: Manipulating and Analysing EQ-5d Data
Version: 1.0.0
Maintainer: Kim Rand <krand@mathsinhealth.com>
Description: The EQ-5D is a widely-used standarized instrument for measuring Health Related Quality Of Life (HRQOL), developed by the EuroQol group <https://euroqol.org/>. It assesses five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, using either a three-level (EQ-5D-3L) or five-level (EQ-5D-5L) scale. Scores from these dimensions are commonly converted into a single utility index using country-specific value sets, which are critical in clinical and economic evaluations of healthcare and in population health surveys. The eq5dsuite package enables users to calculate utility index values for the EQ-5D instruments, including crosswalk utilities using the original crosswalk developed by van Hout et al. (2012) <doi:10.1016/j.jval.2012.02.008> (mapping EQ-5D-5L responses to EQ-5D-3L index values), or the recently developed reverse crosswalk by van Hout et al. (2021) <doi:10.1016/j.jval.2021.03.009> (mapping EQ-5D-3L responses to EQ-5D-5L in [...truncated...]
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5)
Imports: dplyr, ggplot2, moments, RColorBrewer, rlang, scales, stringr, tidyr, rappdirs
NeedsCompilation: no
Packaged: 2024-05-15 04:08:04 UTC; aeste
Author: Kim Rand [aut, cre] , Iryna Schlackow [aut] , Anabel Estevez-Carrillo [aut]
Repository: CRAN
Date/Publication: 2024-05-21 15:00:03 UTC

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New package Rnest with initial version 0.0.0.1
Package: Rnest
Title: Next Eigenvalue Sufficiency Test
Version: 0.0.0.1
Description: Determine the number of dimensions to retain in exploratory factor analysis. The main function, nest(), returns the solution and the plot(nest()) returns a plot.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: crayon (>= 1.4.0), ggplot2 (>= 3.3.0), scales (>= 1.0.0), EFA.MRFA (>= 1.1.2), fungible (>= 2.3), MASS (>= 7.3-58.1)
NeedsCompilation: no
Packaged: 2024-05-17 17:21:15 UTC; pocar
Author: P.-O. Caron [aut, cre, cph]
Maintainer: P.-O. Caron <pocaron19@gmail.com>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2024-05-21 13:30:03 UTC

More information about Rnest at CRAN
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New package GUEST with initial version 0.1.0
Package: GUEST
Title: Graphical Models in Ultrahigh-Dimensional and Error-Prone Data via Boosting Algorithm
Version: 0.1.0
Description: We consider the ultrahigh-dimensional and error-prone data. Our goal aims to estimate the precision matrix and identify the graphical structure of the random variables with measurement error corrected. We further adopt the estimated precision matrix to the linear discriminant function to do classification for multi-label classes.
License: GPL-2
Encoding: UTF-8
Depends: R (>= 3.5.0)
Suggests: sna
Imports: XICOR, network, GGally
LazyData: true
NeedsCompilation: no
Packaged: 2024-05-18 03:14:57 UTC; tsztingfong
Author: Hui-Shan Tsao [aut, cre], Li-Pang Chen [aut]
Maintainer: Hui-Shan Tsao <n410412@gmail.com>
Repository: CRAN
Date/Publication: 2024-05-21 13:30:08 UTC

More information about GUEST at CRAN
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Package zendown updated to version 0.1.0 with previous version 0.0.2 dated 2024-04-15

Title: Access Files from 'Zenodo' Deposits
Description: Access, download and locally cache files deposited on 'Zenodo' <https://zenodo.org>.
Author: Raphael Saldanha [aut, cre]
Maintainer: Raphael Saldanha <raphael.de-freitas-saldanha@inria.fr>

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More information about zendown at CRAN
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Package PaRe updated to version 0.1.13 with previous version 0.1.12 dated 2023-08-11

Title: A Way to Perform Code Review or QA on Other Packages
Description: Reviews other packages during code review by looking at their dependencies, code style, code complexity, and how internally defined functions interact with one another.
Author: Maarten van Kessel [aut, cre]
Maintainer: Maarten van Kessel <m.l.vankessel@erasmusmc.nl>

Diff between PaRe versions 0.1.12 dated 2023-08-11 and 0.1.13 dated 2024-05-21

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More information about PaRe at CRAN
Permanent link

Package TraMineR updated to version 2.2-10 with previous version 2.2-9 dated 2024-01-08

Title: Trajectory Miner: a Sequence Analysis Toolkit
Description: Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Author: Alexis Gabadinho [aut, cph], Matthias Studer [aut, cph] , Nicolas Mueller [aut], Reto Buergin [aut] , Pierre-Alexandre Fonta [aut], Gilbert Ritschard [aut, cre, cph]
Maintainer: Gilbert Ritschard <gilbert.ritschard@unige.ch>

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More information about TraMineR at CRAN
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Package shiny.blueprint updated to version 0.3.0 with previous version 0.2.0 dated 2023-03-15

Title: Palantir's 'Blueprint' for 'Shiny' Apps
Description: Easily use 'Blueprint', the popular 'React' library from Palantir, in your 'Shiny' app. 'Blueprint' provides a rich set of UI components for creating visually appealing applications and is optimized for building complex, data-dense web interfaces. This package provides most components from the underlying library, as well as special wrappers for some components to make it easy to use them in 'R' without writing 'JavaScript' code.
Author: Jakub Sobolewski [aut, cre], Kamil Zyla [aut], Filip Akkad [aut], Filip Stachura [aut], Pawel Chabros [aut], Appsilon Sp. z o.o. [cph]
Maintainer: Jakub Sobolewski <opensource+jakub.sobolewski@appsilon.com>

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More information about shiny.blueprint at CRAN
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Package rapiclient updated to version 0.1.5 with previous version 0.1.4 dated 2024-04-23

Title: Dynamic OpenAPI/Swagger Client
Description: Access services specified in OpenAPI (formerly Swagger) format. It is not a code generator. Client is generated dynamically as a list of R functions.
Author: Darko Bergant [aut], Marcel Ramos [cre, ctb] , Alexandru Mahmoud [ctb], Sean Davis [ctb], Martin Morgan [ctb]
Maintainer: Marcel Ramos <marcel.ramos@roswellpark.org>

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Package gptstudio updated to version 0.4.0 with previous version 0.3.0 dated 2023-07-11

Title: Use Large Language Models Directly in your Development Environment
Description: Large language models are readily accessible via API. This package lowers the barrier to use the API inside of your development environment. For more on the API, see <https://platform.openai.com/docs/introduction>.
Author: Michel Nivard [aut, cph], James Wade [aut, cre, cph] , Samuel Calderon [aut]
Maintainer: James Wade <github@jameshwade.com>

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Package shiny.fluent updated to version 0.4.0 with previous version 0.3.0 dated 2023-01-24

Title: Microsoft Fluent UI for Shiny Apps
Description: A rich set of UI components for building Shiny applications, including inputs, containers, overlays, menus, and various utilities. All components from Fluent UI (the underlying JavaScript library) are available and have usage examples in R.
Author: Jakub Sobolewski [aut, cre], Kamil Zyla [aut], Marek Rogala [aut], Appsilon Sp. z o.o. [cph]
Maintainer: Jakub Sobolewski <opensource+jakub.sobolewski@appsilon.com>

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Permanent link

Package sharx updated to version 1.0-6 with previous version 1.0-5 dated 2016-01-15

Title: Models and Data Sets for the Study of Species-Area Relationships
Description: Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012) <doi:10.1111/j.1466-8238.2011.00655.x>.
Author: Peter Solymos
Maintainer: Peter Solymos <psolymos@gmail.com>

Diff between sharx versions 1.0-5 dated 2016-01-15 and 1.0-6 dated 2024-05-21

 DESCRIPTION    |   12 ++++++------
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Permanent link

Package RestRserve updated to version 1.2.3 with previous version 1.2.2 dated 2024-04-15

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

Diff between RestRserve versions 1.2.2 dated 2024-04-15 and 1.2.3 dated 2024-05-21

 DESCRIPTION                   |   10 +++++-----
 MD5                           |    8 ++++----
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Permanent link

Package RcmdrPlugin.BWS3 updated to version 0.2-3 with previous version 0.2-2 dated 2024-03-06

Title: R Commander Plug-in for Case 3 Best-Worst Scaling
Description: Adds menu items for case 3 (multi-profile) best-worst scaling (BWS3) to the R Commander. BWS3 is a question-based survey method that designs various combinations of attribute levels (profiles), asks respondents to select the best and worst profiles in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on BWS3, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Author: Hideo Aizaki
Maintainer: Hideo Aizaki <azk-r@spa.nifty.com>

Diff between RcmdrPlugin.BWS3 versions 0.2-2 dated 2024-03-06 and 0.2-3 dated 2024-05-21

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Package RcmdrPlugin.BWS2 updated to version 0.2-2 with previous version 0.2-1 dated 2024-03-06

Title: R Commander Plug-in for Case 2 Best-Worst Scaling
Description: Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.
Author: Hideo Aizaki
Maintainer: Hideo Aizaki <azk-r@spa.nifty.com>

Diff between RcmdrPlugin.BWS2 versions 0.2-1 dated 2024-03-06 and 0.2-2 dated 2024-05-21

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Package ptm updated to version 1.0.1 with previous version 0.2.7 dated 2024-05-09

Title: Analyses of Protein Post-Translational Modifications
Description: Contains utilities for the analysis of post-translational modifications (PTMs) in proteins, with particular emphasis on the sulfoxidation of methionine residues. Features include the ability to download, filter and analyze data from the sulfoxidation database 'MetOSite'. Utilities to search and characterize S-aromatic motifs in proteins are also provided. In addition, functions to analyze sequence environments around modifiable residues in proteins can be found. For instance, 'ptm' allows to search for amino acids either overrepresented or avoided around the modifiable residues from the proteins of interest. Functions tailored to test statistical hypothesis related to these differential sequence environments are also implemented. Further and detailed information regarding the methods in this package can be found in (Aledo (2020) <https://metositeptm.com>).
Author: Juan Carlos Aledo [aut, cre]
Maintainer: Juan Carlos Aledo <caledo@uma.es>

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Package KSgeneral updated to version 2.0.0 with previous version 1.1.3 dated 2024-03-15

Title: Computing P-Values of the One-Sample K-S Test and the Two-Sample K-S and Kuiper Tests for (Dis)Continuous Null Distribution
Description: Contains functions to compute p-values for the one-sample and two-sample Kolmogorov-Smirnov (KS) tests and the two-sample Kuiper test for any fixed critical level and arbitrary (possibly very large) sample sizes. For the one-sample KS test, this package implements a novel, accurate and efficient method named Exact-KS-FFT, which allows the pre-specified cumulative distribution function under the null hypothesis to be continuous, purely discrete or mixed. In the two-sample case, it is assumed that both samples come from an unspecified (unknown) continuous, purely discrete or mixed distribution, i.e. ties (repeated observations) are allowed, and exact p-values of the KS and the Kuiper tests are computed. Note, the two-sample Kuiper test is often used when data samples are on the line or on the circle (circular data). To cite this package in publication: (for the use of the one-sample KS test) Dimitrina S. Dimitrova, Vladimir K. Kaishev, and Senren Tan. Computing the Kolmogorov-Smirnov Dis [...truncated...]
Author: Dimitrina S. Dimitrova <D.Dimitrova@city.ac.uk>, Yun Jia <yunjia2019@gmail.com>, Vladimir K. Kaishev <Vladimir.Kaishev.1@city.ac.uk>, Senren Tan <raymondtsrtsr@outlook.com>
Maintainer: Dimitrina S. Dimitrova <D.Dimitrova@city.ac.uk>

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Package bSims updated to version 0.3-2 with previous version 0.3-0 dated 2021-10-07

Title: Agent-Based Bird Point Count Simulator
Description: A highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it (Solymos 2024 <doi:10.1007/s42977-023-00183-2>). The simulations follow time-removal and distance sampling models based on Matsuoka et al. (2012) <doi:10.1525/auk.2012.11190>, Solymos et al. (2013) <doi:10.1111/2041-210X.12106>, and Solymos et al. (2018) <doi:10.1650/CONDOR-18-32.1>, and sound attenuation experiments by Yip et al. (2017) <doi:10.1650/CONDOR-16-93.1>.
Author: Peter Solymos [aut, cre]
Maintainer: Peter Solymos <psolymos@gmail.com>

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Package batchmix updated to version 2.2.1 with previous version 2.2.0 dated 2024-04-18

Title: Semi-Supervised Bayesian Mixture Models Incorporating Batch Correction
Description: Semi-supervised and unsupervised Bayesian mixture models that simultaneously infer the cluster/class structure and a batch correction. Densities available are the multivariate normal and the multivariate t. The model sampler is implemented in C++. This package is aimed at analysis of low-dimensional data generated across several batches. See Coleman et al. (2022) <doi:10.1101/2022.01.14.476352> for details of the model.
Author: Stephen Coleman [aut, cre], Paul Kirk [aut], Chris Wallace [aut]
Maintainer: Stephen Coleman <stcolema@tcd.ie>

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Package RcmdrPlugin.BWS1 updated to version 0.2-2 with previous version 0.2-1 dated 2024-03-06

Title: R Commander Plug-in for Case 1 Best-Worst Scaling
Description: Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details on BWS1, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Author: Hideo Aizaki
Maintainer: Hideo Aizaki <azk-r@spa.nifty.com>

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Package psychonetrics updated to version 0.12 with previous version 0.11.6 dated 2024-04-05

Title: Structural Equation Modeling and Confirmatory Network Analysis
Description: Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
Author: Sacha Epskamp
Maintainer: Sacha Epskamp <mail@sachaepskamp.com>

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Package RcmdrPlugin.DCE updated to version 0.2-3 with previous version 0.2-2 dated 2024-03-06

Title: R Commander Plug-in for Discrete Choice Experiments
Description: Adds menu items for discrete choice experiments (DCEs) to the R Commander. DCE is a question-based survey method that designs various combinations (profiles) of attribute levels using the experimental designs, asks respondents to select the most preferred profile in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on DCEs, refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831>.
Author: Hideo Aizaki
Maintainer: Hideo Aizaki <azk-r@spa.nifty.com>

Diff between RcmdrPlugin.DCE versions 0.2-2 dated 2024-03-06 and 0.2-3 dated 2024-05-21

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Package editbl updated to version 1.0.5 with previous version 1.0.4 dated 2024-05-07

Title: 'DT' Extension for CRUD (Create, Read, Update, Delete) Applications in 'shiny'
Description: The core of this package is a function eDT() which enhances DT::datatable() such that it can be used to interactively modify data in 'shiny'. By the use of generic 'dplyr' methods it supports many types of data storage, with relational databases ('dbplyr') being the main use case.
Author: Jasper Schelfhout [aut, cre], Maxim Nazarov [rev], Daan Seynaeve [rev], Lennart Tuijnder [rev]
Maintainer: Jasper Schelfhout <jasper.schelfhout@openanalytics.eu>

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Package RcmdrPlugin.DCCV updated to version 0.1-5 with previous version 0.1-4 dated 2024-03-05

Title: R Commander Plug-in for Dichotomous Choice Contingent Valuation
Description: Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals' preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) <doi:10.1016/S1574-0099(05)02017-6> for DCCV.
Author: Hideo Aizaki
Maintainer: Hideo Aizaki <azk-r@spa.nifty.com>

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Package duckplyr updated to version 0.4.0 with previous version 0.3.2 dated 2024-03-17

Title: A 'DuckDB'-Backed Version of 'dplyr'
Description: A drop-in replacement for 'dplyr', powered by 'DuckDB' for performance. Also defines a set of generics that provide a low-level implementer's interface for the high-level user interface of 'dplyr'.
Author: Hannes Muehleisen [aut] , Kirill Mueller [aut, cre] , Posit Software, PBC [cph, fnd]
Maintainer: Kirill Mueller <kirill@cynkra.com>

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Package mark (with last version 0.7.0) was removed from CRAN

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

2023-10-22 0.7.0
2023-09-18 0.6.1
2023-05-06 0.6.0
2022-10-16 0.5.3
2022-10-01 0.5.2
2022-08-06 0.5.1
2022-03-09 0.5.0
2021-11-05 0.4.1
2021-10-22 0.4.0
2021-09-18 0.3.0
2021-08-23 0.2.0
2021-07-16 0.1.4

Permanent link
Package radiant updated to version 1.6.6 with previous version 1.6.1 dated 2023-12-17

Title: Business Analytics using R and Shiny
Description: A platform-independent browser-based interface for business analytics in R, based on the shiny package. The application combines the functionality of 'radiant.data', 'radiant.design', 'radiant.basics', 'radiant.model', and 'radiant.multivariate'.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

Diff between radiant versions 1.6.1 dated 2023-12-17 and 1.6.6 dated 2024-05-21

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Package ggsci updated to version 3.1.0 with previous version 3.0.3 dated 2024-03-25

Title: Scientific Journal and Sci-Fi Themed Color Palettes for 'ggplot2'
Description: A collection of 'ggplot2' color palettes inspired by plots in scientific journals, data visualization libraries, science fiction movies, and TV shows.
Author: Nan Xiao [aut, cre] , Joshua Cook [ctb], Clara Jegousse [ctb], Hui Chen [ctb], Miaozhu Li [ctb]
Maintainer: Nan Xiao <me@nanx.me>

Diff between ggsci versions 3.0.3 dated 2024-03-25 and 3.1.0 dated 2024-05-21

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Package deps updated to version 0.3.0 with previous version 0.2.0 dated 2023-06-27

Title: Dependency Management with 'roxygen'-Style Comments
Description: Manage your source code dependencies by decorating your existing R code with special, 'roxygen'-style comments.
Author: Peter Solymos [aut, cre] , Analythium Solutions Inc. [cph, fnd]
Maintainer: Peter Solymos <peter@analythium.io>

Diff between deps versions 0.2.0 dated 2023-06-27 and 0.3.0 dated 2024-05-21

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Package StanHeaders updated to version 2.32.8 with previous version 2.32.7 dated 2024-04-25

Title: C++ Header Files for Stan
Description: The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is primarily useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The [...truncated...]
Author: Ben Goodrich [cre, aut], Joshua Pritikin [ctb], Andrew Gelman [aut], Bob Carpenter [aut], Matt Hoffman [aut], Daniel Lee [aut], Michael Betancourt [aut], Marcus Brubaker [aut], Jiqiang Guo [aut], Peter Li [aut], Allen Riddell [aut], Marco Inacio [aut [...truncated...]
Maintainer: Ben Goodrich <benjamin.goodrich@columbia.edu>

Diff between StanHeaders versions 2.32.7 dated 2024-04-25 and 2.32.8 dated 2024-05-21

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