Thu, 05 Dec 2024

Package stoppingrule updated to version 0.5.1 with previous version 0.5.0 dated 2024-09-14

Title: Create and Evaluate Stopping Rules for Safety Monitoring
Description: Provides functions for creating, displaying, and evaluating stopping rules for safety monitoring in clinical studies. Implements stopping rule methods described in Goldman (1987) <doi:10.1016/0197-2456(87)90153-X>; Geller et al. (2003, ISBN:9781135524388); Ivanova, Qaqish, and Schell (2005) <doi:10.1111/j.1541-0420.2005.00311.x>; Chen and Chaloner (2006) <doi:10.1002/sim.2429>; and Kulldorff et al. (2011) <doi:10.1080/07474946.2011.539924>.
Author: Michael J. Martens [aut, cre], Qinghua Lian [aut], Brent R. Logan [ctb]
Maintainer: Michael J. Martens <mmartens@mcw.edu>

Diff between stoppingrule versions 0.5.0 dated 2024-09-14 and 0.5.1 dated 2024-12-05

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Package appnn updated to version 1.0-1 with previous version 1.0-0 dated 2015-07-12

Title: Amyloid Propensity Prediction Neural Network
Description: Amyloid propensity prediction neural network (APPNN) is an amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation.
Author: Carlos Familia [aut, cre], Sarah R. Dennison [aut], Alexandre Quintas [aut], David A. Phoenix [aut]
Maintainer: Carlos Familia <carlosfamilia@gmail.com>

Diff between appnn versions 1.0-0 dated 2015-07-12 and 1.0-1 dated 2024-12-05

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Package scCustomize updated to version 3.0.0 with previous version 2.1.2 dated 2024-02-28

Title: Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing
Description: Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'. 'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions. For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" <doi:10.5281/zenodo.5706430> RRID:SCR_024675.
Author: Samuel Marsh [aut, cre] , Ming Tang [ctb], Velina Kozareva [ctb], Lucas Graybuck [ctb]
Maintainer: Samuel Marsh <samuel.marsh@childrens.harvard.edu>

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Package rstanemax updated to version 0.1.6 with previous version 0.1.5 dated 2023-09-12

Title: Emax Model Analysis with 'Stan'
Description: Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code.
Author: Kenta Yoshida [aut, cre] , Danielle Navarro [aut], Trustees of Columbia University [cph]
Maintainer: Kenta Yoshida <yoshida.kenta.6@gmail.com>

Diff between rstanemax versions 0.1.5 dated 2023-09-12 and 0.1.6 dated 2024-12-05

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Package REMLA updated to version 1.2.0 with previous version 1.1 dated 2024-05-11

Title: Robust Expectation-Maximization Estimation for Latent Variable Models
Description: Traditional latent variable models assume that the population is homogeneous, meaning that all individuals in the population are assumed to have the same latent structure. However, this assumption is often violated in practice given that individuals may differ in their age, gender, socioeconomic status, and other factors that can affect their latent structure. The robust expectation maximization (REM) algorithm is a statistical method for estimating the parameters of a latent variable model in the presence of population heterogeneity as recommended by Nieser & Cochran (2023) <doi:10.1037/met0000413>. The REM algorithm is based on the expectation-maximization (EM) algorithm, but it allows for the case when all the data are generated by the assumed data generating model.
Author: Bryan Ortiz-Torres [aut, cre], Kenneth Nieser [aut]
Maintainer: Bryan Ortiz-Torres <bortiztorres@wisc.edu>

Diff between REMLA versions 1.1 dated 2024-05-11 and 1.2.0 dated 2024-12-05

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More information about REMLA at CRAN
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Package seriation updated to version 1.5.7 with previous version 1.5.6 dated 2024-08-19

Title: Infrastructure for Ordering Objects Using Seriation
Description: Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) <doi:10.18637/jss.v025.i03>.
Author: Michael Hahsler [aut, cre, cph] , Christian Buchta [aut, cph], Kurt Hornik [aut, cph] , David Barnett [ctb], Michael Brusco [ctb, cph], Michael Friendly [ctb], Hans-Friedrich Koehn [ctb, cph], Fionn Murtagh [ctb, cph], Stephanie Stahl [ctb, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

Diff between seriation versions 1.5.6 dated 2024-08-19 and 1.5.7 dated 2024-12-05

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 seriation-1.5.7/seriation/R/seriate_BEA.R                       |   25 ++-
 seriation-1.5.7/seriation/R/seriate_BK.R                        |only
 seriation-1.5.7/seriation/R/seriate_PCA.R                       |   26 +--
 seriation-1.5.7/seriation/R/seriate_best.R                      |    3 
 seriation-1.5.7/seriation/README.md                             |    9 -
 seriation-1.5.7/seriation/build/partial.rdb                     |binary
 seriation-1.5.7/seriation/build/vignette.rds                    |binary
 seriation-1.5.7/seriation/inst/README_files/configuration-1.png |binary
 seriation-1.5.7/seriation/inst/README_files/seriation-1.png     |binary
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 seriation-1.5.7/seriation/inst/doc/seriation.Rnw                |   12 -
 seriation-1.5.7/seriation/inst/doc/seriation.pdf                |binary
 seriation-1.5.7/seriation/man/hmap.Rd                           |   18 --
 seriation-1.5.7/seriation/man/register_DendSer.Rd               |    2 
 seriation-1.5.7/seriation/man/register_GA.Rd                    |    2 
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 seriation-1.5.7/seriation/man/register_tsne.Rd                  |    4 
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 seriation-1.5.7/seriation/man/seriate.Rd                        |   15 +-
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 seriation-1.5.7/seriation/src/stress.c                          |    4 
 seriation-1.5.7/seriation/tests/testthat/test-seriate.R         |   73 ++++++++--
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 37 files changed, 264 insertions(+), 170 deletions(-)

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New package robqda with initial version 1.0
Package: robqda
Title: Robust Quadratic Discriminant Analysis
Version: 1.0
Date: 2024-12-03
Author: Michail Tsagris [aut, cre]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>
Depends: R (>= 4.0)
Imports: MASS, Rfast, Rfast2
Suggests: mvcauchy
License: GPL (>= 2)
Description: The minimum covariance determinant estimator is used to perform robust quadratic discriminant analysis, including cross-validation. References: Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>.
NeedsCompilation: no
Packaged: 2024-12-03 19:44:27 UTC; mtsag
Repository: CRAN
Date/Publication: 2024-12-05 18:40:07 UTC

More information about robqda at CRAN
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New package RegDDM with initial version 1.0
Package: RegDDM
Title: Generalized Linear Regression with DDM
Version: 1.0
Description: Drift-Diffusion Model (DDM) has been widely used to model binary decision-making tasks, and many research studies the relationship between DDM parameters and other characteristics of the subject. This package uses 'RStan' to perform generalized liner regression analysis over DDM parameters via a single Bayesian Hierarchical model. Compared to estimating DDM parameters followed by a separate regression model, 'RegDDM' reduces bias and improves statistical power.
License: GPL (>= 3)
Encoding: UTF-8
URL: https://github.com/biorabbit/RegDDM
BugReports: https://github.com/biorabbit/RegDDM/issues
Imports: rstan, stringr, dplyr, tidyr, purrr, rtdists, rlang, stats,
Suggests: testthat (>= 3.0.0)
Depends: R (>= 2.10)
LazyData: true
NeedsCompilation: no
Packaged: 2024-12-04 05:08:59 UTC; Rabbit
Author: Zekai Jin [aut, cre]
Maintainer: Zekai Jin <Jin.Zekai@nyspi.columbia.edu>
Repository: CRAN
Date/Publication: 2024-12-05 19:00:12 UTC

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New package rectpacker with initial version 1.0.0
Package: rectpacker
Title: Rectangle Packing
Version: 1.0.0
Maintainer: Mike Cheng <mikefc@coolbutuseless.com>
URL: https://github.com/coolbutuseless/rectpacker
BugReports: https://github.com/coolbutuseless/rectpacker/issues
Description: Rectangle packing is a packing problem where rectangles are placed into a larger rectangular region (without overlapping) in order to maximise the use space. Rectangles are packed using the skyline heuristic as discussed in Lijun et al (2011) 'A Skyline-Based Heuristic for the 2D Rectangular Strip Packing Problem' <doi:10.1007/978-3-642-21827-9_29>. A function is also included for determining a good small-sized box for containing a given set of rectangles.
License: MIT + file LICENSE
Encoding: UTF-8
Copyright: The included 'stb_rect_pack.h' header (v1.01) is Copyright (c) 2017 Sean Barrett and licensed under the MIT license. See COPYRIGHTS file for more details.
Suggests: testthat (>= 3.0.0)
NeedsCompilation: yes
Packaged: 2024-12-04 06:32:19 UTC; mike
Author: Mike Cheng [aut, cre, cph], Sean Barrett [aut, cph]
Repository: CRAN
Date/Publication: 2024-12-05 19:00:02 UTC

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New package recforest with initial version 1.0.0
Package: recforest
Title: Random Survival Forest for Recurrent Events
Version: 1.0.0
Description: Analyze recurrent events with right-censored data and the potential presence of a terminal event (that prevents further occurrences, like death). 'recofest' extends the random survival forest algorithm, adapting splitting rules and node estimators to handle complexities of recurrent events. The methodology is fully described in Murris, J., Bouaziz, O., Jakubczak, M., Katsahian, S., & Lavenu, A. (2024) (<https://hal.science/hal-04612431v1/document>).
License: Apache License (>= 2)
Encoding: UTF-8
Suggests: frailtypack, knitr, rmarkdown, testthat (>= 3.0.0), withr
Imports: cli, dplyr, future, future.apply, magrittr, methods, mets, purrr, reda, stats, survival, tibble, timereg
VignetteBuilder: knitr
Depends: R (>= 2.10)
LazyData: true
NeedsCompilation: no
Packaged: 2024-12-03 10:30:40 UTC; colinfay
Author: Juliette Murris [aut, cre] , Guillaume Desachy [aut] , Colin Fay [aut] , Yohann Mansiaux [aut] , Audrey Lavenu [aut] , Sandrine Katsahian [aut]
Maintainer: Juliette Murris <murris.juliette@gmail.com>
Repository: CRAN
Date/Publication: 2024-12-05 18:30:02 UTC

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New package RapidFuzz with initial version 1.0
Package: RapidFuzz
Title: String Similarity Computation Using 'RapidFuzz'
Version: 1.0
Date: 2024-11-30
Description: Provides a high-performance interface for calculating string similarities and distances, leveraging the efficient library 'RapidFuzz' <https://github.com/rapidfuzz/rapidfuzz-cpp>. This package integrates the 'C++' implementation, allowing 'R' users to access cutting-edge algorithms for fuzzy matching and text analysis.
URL: <https://github.com/StrategicProjects/RapidFuzz>
Note: This package makes use of the 'RapidFuzz' source code created by Max Bachmann and Adam Cohen (<https://github.com/rapidfuzz/rapidfuzz-cpp>).
License: MIT + file LICENSE
Encoding: UTF-8
Imports: Rcpp (>= 1.0.13)
LinkingTo: Rcpp
SystemRequirements: C++17
NeedsCompilation: yes
Packaged: 2024-12-03 20:17:05 UTC; leite
Author: Andre Leite [aut, cre], Hugo Vaconcelos [aut], Max Bachmann [ctb], Adam Cohen [ctb]
Maintainer: Andre Leite <leite@castlab.org>
Repository: CRAN
Date/Publication: 2024-12-05 18:40:14 UTC

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Package omopgenerics updated to version 0.4.1 with previous version 0.4.0 dated 2024-11-22

Title: Methods and Classes for the OMOP Common Data Model
Description: Provides definitions of core classes and methods used by analytic pipelines that query the OMOP (Observational Medical Outcomes Partnership) common data model.
Author: Marti Catala [aut, cre] , Edward Burn [aut] , Mike Du [ctb] , Yuchen Guo [ctb] , Adam Black [ctb] , Marta Alcalde-Herraiz [ctb]
Maintainer: Marti Catala <marti.catalasabate@ndorms.ox.ac.uk>

Diff between omopgenerics versions 0.4.0 dated 2024-11-22 and 0.4.1 dated 2024-12-05

 DESCRIPTION                     |    6 
 MD5                             |   20 
 R/classAchillesTable.R          |    4 
 R/classSummarisedResult.R       |   12 
 R/columns.R                     |   11 
 R/filter.R                      |    4 
 R/pivot.R                       |   21 
 R/split.R                       |    7 
 R/utilities.R                   |    3 
 inst/doc/summarised_result.html |  880 ++++++++++++++++++++--------------------
 tests/testthat/test-utilities.R |   14 
 11 files changed, 506 insertions(+), 476 deletions(-)

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Package mlpack updated to version 4.5.1 with previous version 4.5.0 dated 2024-10-05

Title: 'Rcpp' Integration for the 'mlpack' Library
Description: A fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) <doi:10.21105/joss.05026>.
Author: Yashwant Singh Parihar [aut, ctb, cph], Ryan Curtin [aut, ctb, cph, cre], Dirk Eddelbuettel [aut, ctb, cph], James Balamuta [aut, ctb, cph], Bill March [ctb, cph], Dongryeol Lee [ctb, cph], Nishant Mehta [ctb, cph], Parikshit Ram [ctb, cph], James Cl [...truncated...]
Maintainer: Ryan Curtin <ryan@ratml.org>

Diff between mlpack versions 4.5.0 dated 2024-10-05 and 4.5.1 dated 2024-12-05

 mlpack-4.5.0/mlpack/inst/include/mlpack/methods/decision_tree/split_functions                                    |only
 mlpack-4.5.1/mlpack/DESCRIPTION                                                                                  |   18 
 mlpack-4.5.1/mlpack/MD5                                                                                          |  706 +++++-----
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/cereal/is_loading.hpp                                               |   14 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/cereal/is_saving.hpp                                                |   16 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/cv/cv_base.hpp                                                      |   18 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/cv/cv_base_impl.hpp                                                 |   37 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/cv/k_fold_cv.hpp                                                    |    8 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/cv/meta_info_extractor.hpp                                          |   14 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/cv/simple_cv.hpp                                                    |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/check_categorical_param.hpp                                    |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/dataset_mapper.hpp                                             |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/dataset_mapper_impl.hpp                                        |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/has_serialize.hpp                                              |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/load_arff_impl.hpp                                             |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/load_numeric_csv.hpp                                           |   10 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/map_policies/increment_policy.hpp                              |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/map_policies/missing_policy.hpp                                |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/split_data.hpp                                                 |  130 +
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/string_encoding.hpp                                            |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/string_encoding_impl.hpp                                       |   22 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/data/string_encoding_policies/tf_idf_encoding_policy.hpp            |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/distances/iou_distance.hpp                                          |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/distances/lmetric.hpp                                               |    8 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/distances/mahalanobis_distance.hpp                                  |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/distributions/diagonal_gaussian_distribution.hpp                    |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/distributions/discrete_distribution.hpp                             |   10 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/distributions/discrete_distribution_impl.hpp                        |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/distributions/gamma_distribution.hpp                                |   12 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/distributions/gaussian_distribution.hpp                             |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/distributions/laplace_distribution.hpp                              |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/distributions/regression_distribution.hpp                           |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/hpt/cv_function.hpp                                                 |   16 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/hpt/deduce_hp_types.hpp                                             |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/hpt/fixed.hpp                                                       |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/hpt/hpt.hpp                                                         |   11 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/hpt/hpt_impl.hpp                                                    |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/kernels/cosine_similarity.hpp                                       |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/math/ccov_impl.hpp                                                  |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/math/digamma.hpp                                                    |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/math/log_add_impl.hpp                                               |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/math/rand_vector.hpp                                                |    3 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/math/range.hpp                                                      |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/math/trigamma.hpp                                                   |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/metrics/bleu_impl.hpp                                               |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/metrics/iou_metric_impl.hpp                                         |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/metrics/non_maximal_suppression_impl.hpp                            |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/address.hpp                                                    |   28 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/ballbound.hpp                                                  |    7 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/binary_space_tree.hpp                        |   25 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/binary_space_tree_impl.hpp                   |   48 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/breadth_first_dual_tree_traverser.hpp        |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/mean_split_impl.hpp                          |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/midpoint_split.hpp                           |    1 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/rp_tree_max_split.hpp                        |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/rp_tree_mean_split.hpp                       |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/rp_tree_mean_split_impl.hpp                  |   10 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/typedef.hpp                                  |   33 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/ub_tree_split.hpp                            |    5 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/vantage_point_split.hpp                      |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/binary_space_tree/vantage_point_split_impl.hpp                 |    1 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/build_tree.hpp                                                 |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/cellbound.hpp                                                  |    5 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/cellbound_impl.hpp                                             |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/cosine_tree/cosine_tree.hpp                                    |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/cover_tree/cover_tree.hpp                                      |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/cover_tree/cover_tree_impl.hpp                                 |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/cover_tree/dual_tree_traverser_impl.hpp                        |    1 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/cover_tree/single_tree_traverser_impl.hpp                      |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/hollow_ball_bound.hpp                                          |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/hollow_ball_bound_impl.hpp                                     |    8 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/hrectbound.hpp                                                 |    3 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/hrectbound_impl.hpp                                            |   15 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/octree/octree.hpp                                              |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/perform_split.hpp                                              |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/discrete_hilbert_value.hpp                      |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/discrete_hilbert_value_impl.hpp                 |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/hilbert_r_tree_auxiliary_information.hpp        |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/minimal_coverage_sweep.hpp                      |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/minimal_coverage_sweep_impl.hpp                 |    8 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/minimal_splits_number_sweep.hpp                 |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/minimal_splits_number_sweep_impl.hpp            |    2 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/r_plus_tree_descent_heuristic_impl.hpp          |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/r_plus_tree_split.hpp                           |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/r_plus_tree_split_impl.hpp                      |    9 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/r_star_tree_descent_heuristic_impl.hpp          |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/r_star_tree_split_impl.hpp                      |   12 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/r_tree_descent_heuristic_impl.hpp               |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/r_tree_split_impl.hpp                           |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/rectangle_tree.hpp                              |    8 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/x_tree_auxiliary_information.hpp                |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/rectangle_tree/x_tree_split_impl.hpp                           |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/space_split/hyperplane.hpp                                     |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/tree/spill_tree/spill_tree.hpp                                      |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/arma_traits.hpp                                                |   34 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/ens_traits.hpp                                                 |   12 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/first_element_is_arma.hpp                                      |    8 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/gitversion.hpp                                                 |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/io.hpp                                                         |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/params.hpp                                                     |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/prefixedoutstream.hpp                                          |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/prefixedoutstream_impl.hpp                                     |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/sfinae_utility.hpp                                             |   36 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/size_checks.hpp                                                |    9 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/timers.hpp                                                     |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/timers_impl.hpp                                                |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/using.hpp                                                      |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/core/util/version.hpp                                                    |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/adaboost/adaboost.hpp                                            |   11 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/adaboost/adaboost_impl.hpp                                       |   15 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/amf/amf.hpp                                                      |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/amf/update_rules/incremental_iterators.hpp                       |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/amf/update_rules/svd_incomplete_incremental_learning.hpp         |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/activation_functions/bipolar_sigmoid_function.hpp            |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/convolution_rules/fft_convolution.hpp                        |    8 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/convolution_rules/naive_convolution.hpp                      |   10 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/convolution_rules/svd_convolution.hpp                        |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/ffn.hpp                                                      |   10 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/ffn_impl.hpp                                                 |   10 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/init_rules/kathirvalavakumar_subavathi_init.hpp              |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/init_rules/orthogonal_init.hpp                               |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/adaptive_max_pooling.hpp                               |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/adaptive_mean_pooling.hpp                              |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/add.hpp                                                |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/add_merge.hpp                                          |    2 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/batch_norm.hpp                                         |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/c_relu.hpp                                             |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/celu.hpp                                               |    2 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/dropconnect.hpp                                        |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/dropout.hpp                                            |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/elu.hpp                                                |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/flexible_relu.hpp                                      |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/ftswish.hpp                                            |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/grouped_convolution.hpp                                |    7 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/hard_tanh.hpp                                          |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/identity.hpp                                           |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/layer_norm.hpp                                         |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/leaky_relu.hpp                                         |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/linear.hpp                                             |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/linear3d.hpp                                           |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/linear3d_impl.hpp                                      |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/linear_no_bias.hpp                                     |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/log_softmax.hpp                                        |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/lstm.hpp                                               |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/lstm_impl.hpp                                          |   20 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/max_pooling.hpp                                        |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/mean_pooling.hpp                                       |    2 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/padding.hpp                                            |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/parametric_relu.hpp                                    |    2 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/relu6.hpp                                              |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/repeat.hpp                                             |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/softmax.hpp                                            |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/layer/softmin.hpp                                            |    2 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/ann/regularizer/lregularizer.hpp                                 |    4 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/cf/cf.hpp                                                        |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/cf/cf_impl.hpp                                                   |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/cf/cf_model.hpp                                                  |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/cf/svd_wrapper.hpp                                               |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/dbscan/dbscan.hpp                                                |    4 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/decision_tree/utils.hpp                                          |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/det/dt_utils.hpp                                                 |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/det/dtree.hpp                                                    |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/det/dtree_impl.hpp                                               |   11 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/emst/dtb.hpp                                                     |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/emst/dtb_impl.hpp                                                |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/emst/dtb_rules.hpp                                               |    2 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/emst/edge_pair.hpp                                               |    1 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/gmm/em_fit_impl.hpp                                              |   20 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/gmm/gmm_train_main.cpp                                           |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/gmm/positive_definite_constraint.hpp                             |   12 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/hmm/hmm_impl.hpp                                                 |    5 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/kde/kde.hpp                                                      |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/kde/kde_impl.hpp                                                 |   10 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/kde/kde_model.hpp                                                |   14 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/kde/kde_rules.hpp                                                |    6 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/lars/lars.hpp                                                    |   86 -
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/lmnn/lmnn.hpp                                                    |   21 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/nca/nca.hpp                                                      |   23 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/pca/pca_impl.hpp                                                 |   22 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/radical/radical_impl.hpp                                         |   13 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/range_search/range_search_rules.hpp                              |    6 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/range_search/rs_model.hpp                                        |    2 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/rann/ra_model.hpp                                                |    8 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/rann/ra_search.hpp                                               |    2 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/rann/ra_search_rules.hpp                                         |    8 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/rann/ra_typedef.hpp                                              |    4 
 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/rann/ra_util_impl.hpp                                            |    2 
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 mlpack-4.5.1/mlpack/inst/include/mlpack/methods/sparse_coding/sparse_coding.hpp                                  |    4 
 mlpack-4.5.1/mlpack/man/mlpack.Rd                                                                                |    4 
 mlpack-4.5.1/mlpack/src/Makevars                                                                                 |    1 
 mlpack-4.5.1/mlpack/src/mlpack/bindings/R/get_printable_param.hpp                                                |   24 
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 mlpack-4.5.1/mlpack/src/mlpack/bindings/R/print_doc.hpp                                                          |    2 
 mlpack-4.5.1/mlpack/src/mlpack/bindings/R/print_input_param.hpp                                                  |    2 
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 mlpack-4.5.1/mlpack/src/mlpack/bindings/R/print_output_processing.hpp                                            |   24 
 mlpack-4.5.1/mlpack/src/mlpack/bindings/R/print_serialize_util.hpp                                               |   12 
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New package mixedbiastest with initial version 0.2.1
Package: mixedbiastest
Title: Bias Diagnostic for Linear Mixed Models
Version: 0.2.1
Date: 2024-12-03
Maintainer: Andrew T. Karl <akarl@asu.edu>
Description: Provides a function to perform bias diagnostics on linear mixed models fitted with 'lmer()' from the 'lme4' package. Implements permutation tests for assessing the bias of fixed effects, as described in Karl and Zimmerman (2021) <doi:10.1016/j.jspi.2020.06.004>. Karl and Zimmerman (2020) <doi:10.17632/tmynggddfm.1> provide R code for implementing the test using 'mvglmmRank' output. Development of this package was assisted by 'GPT o1-preview' for code structure and documentation.
Depends: R (>= 3.5.0)
Imports: lme4, Matrix, ggplot2, rlang
Suggests: plm
License: GPL-3
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2024-12-04 01:24:33 UTC; andre
Author: Andrew T. Karl [cre, aut]
Repository: CRAN
Date/Publication: 2024-12-05 18:50:01 UTC

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New package MIGEE with initial version 0.1.0
Package: MIGEE
Title: Impute Missing Values and Fitting Linear Mixed Effect Model
Version: 0.1.0
Maintainer: Atanu Bhattacharjee <atanustat@gmail.com>
Description: Implements methods for estimating generalized estimating equations (GEE) with advanced options for flexible modeling and handling missing data. This package provides tools to fit and analyze GEE models for longitudinal data, allowing users to address missingness using a variety of imputation techniques. It supports both univariate and multivariate modeling, visualization of missing data patterns, and facilitates the transformation of data for efficient statistical analysis. Designed for researchers working with complex datasets, it ensures robust estimation and inference in longitudinal and clustered data settings.
License: GPL-3
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
ByteCompile: Yes
Author: Atanu Bhattacharjee [aut, cre, ctb], Gajendra Kumar Vishwakarma [aut, ctb], Neelesh Kumar [aut, ctb]
Imports: mice,VIM,ggplot2,lme4,ggeffects,dplyr,readr,reshape2
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2024-12-03 23:34:04 UTC; atanubhattacharjee
Repository: CRAN
Date/Publication: 2024-12-05 18:50:09 UTC

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New package iclogcondist with initial version 1.0.1
Package: iclogcondist
Title: Log-Concave Distribution Estimation with Interval-Censored Data
Version: 1.0.1
Description: We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, <doi:10.48550/arXiv.2411.19878>).
License: GPL-3
Imports: Rcpp, flexsurv, ggplot2, icenReg, monotone, fdrtool
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
Depends: R (>= 3.5.0)
NeedsCompilation: yes
Packaged: 2024-12-04 02:43:33 UTC; ycyma
Author: Chi Wing Chu [aut], Hok Kan Ling [aut], Chaoyu Yuan [aut, cre]
Maintainer: Chaoyu Yuan <chaoyu.yuan@columbia.edu>
Repository: CRAN
Date/Publication: 2024-12-05 19:00:06 UTC

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New package GVS with initial version 0.0.1
Package: GVS
Title: 'Geocoordinate Validation Service'
Version: 0.0.1
Description: The 'Geocoordinate Validation Service' (GVS) runs checks of coordinates in latitude/longitude format. It returns annotated coordinates with additional flags and metadata that can be used in data cleaning. Additionally, the package has functions related to attribution and metadata information. More information can be found at <https://github.com/ojalaquellueva/gvs/tree/master/api>.
Depends: R (>= 3.5.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: jsonlite, httr
Suggests: knitr, rmarkdown, testthat, devtools, BIEN, vcr (>= 0.6.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-12-03 22:09:37 UTC; Brian Maitner
Author: Brian Maitner [aut, cre] , Brad Boyle [aut], Rethvick Sriram Yugendra Babu [aut]
Maintainer: Brian Maitner <bmaitner@gmail.com>
Repository: CRAN
Date/Publication: 2024-12-05 18:40:18 UTC

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New package GOLDprice with initial version 0.1.0
Package: GOLDprice
Title: Gold Price Data
Version: 0.1.0
Author: Fadhlul Mubarak [aut, cre], Nurniswah [aut], Vinny Yuliani Sundara [aut]
Maintainer: Fadhlul Mubarak <mubarakfadhlul@gmail.com>
Description: A collection of gold price data in various currencies in the form of USD, EUR, JPY, GBP, CAD, CHF, INR, CNY, TRY, SAR, IDR, AED, THB, VND, EGP, KRW, RUB, ZAR, and AUD. This data comes from the World Gold Council. In addition, the data is in the form of daily, weekly, monthly (average and the end of period), quarterly (average and the end of period), and yearly (average and the end of period).
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
Imports: readxl
NeedsCompilation: no
Packaged: 2024-12-04 01:36:08 UTC; HP
Repository: CRAN
Date/Publication: 2024-12-05 18:50:11 UTC

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New package GDILM.SEIRS with initial version 0.0.1
Package: GDILM.SEIRS
Title: Spatial Individual Level Modeling of Infectious Disease Transmission with Reinfection Dynamics
Version: 0.0.1
Description: Geographically Dependent Individual Level Models (GDILMs) within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model infectious disease transmission, incorporating reinfection dynamics. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. It also provides tools for GDILM fitting, parameter estimation, AIC calculation on real pandemic data, and simulation studies customized to user-defined model settings.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: MASS, mvtnorm, ngspatial, stats
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2024-12-03 20:19:14 UTC; aminabed
Author: Amin Abed [aut, cre, cph] , Mahmoud Torabi [ths], Zeinab Mashreghi [ths]
Maintainer: Amin Abed <abeda@myumanitoba.ca>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2024-12-05 18:40:21 UTC

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New package ErlangC with initial version 0.1.0
Package: ErlangC
Title: Solve Erlang-C Model
Version: 0.1.0
Maintainer: Damonsoul <chenmaowei96@gmail.com>
Description: Provides a set of functions to solve Erlang-C model. The Erlang C formula was invented by the Danish Mathematician A.K. Erlang and is used to calculate the number of advisors and the service level.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: bsicons, bslib, DT, gmp, lubridate, shiny, tidyr
Depends: R (>= 2.10)
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2024-12-03 08:56:01 UTC; Administrator
Author: Damonsoul [aut, cre]
Repository: CRAN
Date/Publication: 2024-12-05 18:30:15 UTC

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New package educationR with initial version 0.1.0
Package: educationR
Title: A Comprehensive Collection of Educational Datasets
Version: 0.1.0
Maintainer: Renzo Caceres Rossi <arenzocaceresrossi@gmail.com>
Description: Provides a comprehensive collection of datasets related to education, covering topics such as student performance, learning methods, test scores, absenteeism, and other educational metrics. This package serves as a resource for educational researchers, data analysts, and statisticians to explore and analyze data in the field of education.
License: GPL-3
URL: https://github.com/lightbluetitan/educationr, https://lightbluetitan.github.io/educationr/
BugReports: https://github.com/lightbluetitan/educationr/issues
Encoding: UTF-8
LazyData: true
Suggests: ggplot2, dplyr, knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-12-03 23:09:29 UTC; renzocrossi
Author: Renzo Caceres Rossi [aut, cre]
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2024-12-05 18:50:04 UTC

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New package drhutools with initial version 1.0.0
Package: drhutools
Title: Political Science Academic Research Gears
Version: 1.0.0
Maintainer: Yue Hu <yuehu@tsinghua.edu.cn>
Description: Using these tools to simplify the research process of political science and other social sciences. The current version can create folder system for academic project in political science, calculate psychological trait scores, visualize experimental and spatial data, and set up color-blind palette, functions used in academic research of political psychology or political science in general.
URL: https://www.drhuyue.site/software/drhutools/
BugReports: https://github.com/sammo3182/drhutools/issues
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R(>= 3.6.0)
Imports: dplyr, ggplot2, purrr, htmltools, sf, htmlwidgets, jsonlite, leaflet, sp, stats, methods, gganimate, magick, mapview, webshot, animation, png, grDevices
LazyData: true
Suggests: knitr, remotes, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-12-03 09:51:25 UTC; yuehu
Author: Yue Hu [aut, cre], Wen Deng [ctb]
Repository: CRAN
Date/Publication: 2024-12-05 18:30:12 UTC

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New package BayesianFitForecast with initial version 1.0.0
Package: BayesianFitForecast
Title: Bayesian Parameter Estimation and Forecasting for Epidemiological Models
Version: 1.0.0
Description: Methods for Bayesian parameter estimation and forecasting in epidemiological models. Functions enable model fitting using Bayesian methods and generate forecasts with uncertainty quantification. Implements approaches described in <doi:10.48550/arXiv.2411.05371> and <doi:10.1002/sim.9164>.
License: CC0
Encoding: UTF-8
Imports: bayesplot, readxl, openxlsx, rstan, ggplot2, dplyr
Depends: R (>= 3.5.0)
URL: https://github.com/gchowell/BayesianFitForecast
BugReports: https://github.com/gchowell/BayesianFitForecast/issues
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-12-03 05:55:12 UTC; hamedkp
Author: Hamed Karami [aut], Amanda Bleichrodt [aut], Ruiyan Luo [aut], Gerardo Chowell [aut, cre]
Maintainer: Gerardo Chowell <gchowell@gsu.edu>
Repository: CRAN
Date/Publication: 2024-12-05 18:20:07 UTC

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New package airship with initial version 1.4.3
Package: airship
Title: Visualization of Simulated Datasets with Multiple Simulation Input Dimensions
Version: 1.4.3
Description: Plots simulation results of clinical trials. Its main feature is allowing users to simultaneously investigate the impact of several simulation input dimensions through dynamic filtering of the simulation results. A more detailed description of the app can be found in Meyer et al. <DOI:10.1016/j.softx.2023.101347> or the vignettes on 'GitHub'.
BugReports: https://github.com/el-meyer/airship/issues
License: GPL-3
Encoding: UTF-8
Depends: R (>= 2.10), shiny, shinyBS
Imports: DT, shinybusy, plotly, dplyr, tidyselect, tidyr, stringr, colourpicker, shinyWidgets, shinydashboard, scales, Cairo, ggplot2, rlang, magrittr, shinyjs, data.table, shinyalert, vctrs, mvtnorm
LazyData: true
Suggests: knitr, rmarkdown, ggpubr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-12-03 20:01:01 UTC; elias
Author: Elias Laurin Meyer [aut, cre] , Constantin Kumaus [aut, ctb], Michal Majka [aut, ctb] , Franz Koenig [aut, ctb]
Maintainer: Elias Laurin Meyer <elias@berryconsultants.net>
Repository: CRAN
Date/Publication: 2024-12-05 18:40:10 UTC

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Package trtswitch updated to version 0.1.2 with previous version 0.1.1 dated 2024-11-01

Title: Treatment Switching
Description: Implements rank-preserving structural failure time model (RPSFTM), iterative parameter estimation (IPE), inverse probability of censoring weights (IPCW), and two-stage estimation (TSE) methods for treatment switching in randomized clinical trials.
Author: Kaifeng Lu [aut, cre]
Maintainer: Kaifeng Lu <kaifenglu@gmail.com>

Diff between trtswitch versions 0.1.1 dated 2024-11-01 and 0.1.2 dated 2024-12-05

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Package neonUtilities updated to version 2.4.3 with previous version 2.4.2 dated 2024-04-30

Title: Utilities for Working with NEON Data
Description: NEON data packages can be accessed through the NEON Data Portal <https://www.neonscience.org> or through the NEON Data API (see <https://data.neonscience.org/data-api> for documentation). Data delivered from the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and converting formats. For more information, see the readme file at <https://github.com/NEONScience/NEON-utilities>.
Author: Claire Lunch [aut, cre, ctb], Christine Laney [aut, ctb], Nathan Mietkiewicz [aut, ctb], Eric Sokol [aut, ctb], Kaelin Cawley [aut, ctb], NEON [aut]
Maintainer: Claire Lunch <clunch@battelleecology.org>

Diff between neonUtilities versions 2.4.2 dated 2024-04-30 and 2.4.3 dated 2024-12-05

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Package bWGR updated to version 2.2.13 with previous version 2.2.12 dated 2024-11-18

Title: Bayesian Whole-Genome Regression
Description: Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>, <doi:10.1093/genetics/iyae179>), with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).
Author: Alencar Xavier [aut, cre] , William Muir [aut], David Habier [aut], Kyle Kocak [aut], Shizhong Xu [aut], Katy Rainey [aut]
Maintainer: Alencar Xavier <alenxav@gmail.com>

Diff between bWGR versions 2.2.12 dated 2024-11-18 and 2.2.13 dated 2024-12-05

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Package pomdp updated to version 1.2.4 with previous version 1.2.3 dated 2024-05-05

Title: Infrastructure for Partially Observable Markov Decision Processes (POMDP)
Description: Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Smallwood and Sondik (1973) <doi:10.1287/opre.21.5.1071>.
Author: Michael Hahsler [aut, cph, cre] , Hossein Kamalzadeh [ctb]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

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Package jarbes updated to version 2.2.3 with previous version 2.2.2 dated 2024-10-07

Title: Just a Rather Bayesian Evidence Synthesis
Description: Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).
Author: Pablo Emilio Verde [aut, cre]
Maintainer: Pablo Emilio Verde <pabloemilio.verde@hhu.de>

Diff between jarbes versions 2.2.2 dated 2024-10-07 and 2.2.3 dated 2024-12-05

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Package cowsay updated to version 1.0.0 with previous version 0.9.0 dated 2023-11-02

Title: Messages, Warnings, Strings with Ascii Animals
Description: Allows printing of character strings as messages/warnings/etc. with ASCII animals, including cats, cows, frogs, chickens, ghosts, and more.
Author: Scott Chamberlain [aut, cre], Amanda Dobbyn [aut], Tyler Rinker [ctb], Thomas Leeper [ctb], Noam Ross [ctb], Rich FitzJohn [ctb], Carson Sievert [ctb], Kiyoko Gotanda [ctb], Andy Teucher [ctb], Karl Broman [ctb], Franz-Sebastian Krah [ctb], Lucy D'Ag [...truncated...]
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package RSquaredMI updated to version 0.2.0 with previous version 0.1.1 dated 2024-09-19

Title: R-Squared with Multiply Imputed Data
Description: Provides R-squared values and standardized regression coefficients for linear models applied to multiply imputed datasets as obtained by 'mice'. Confidence intervals, zero-order correlations, and alternative adjusted R-squared estimates are also available. The methods are described in Van Ginkel and Karch (2024) <doi:10.1111/bmsp.12344> and in Van Ginkel (2020) <doi:10.1007/s11336-020-09696-4>.
Author: Julian D. Karch [aut, cph, cre] , Joost van Ginkel [aut, cph]
Maintainer: Julian D. Karch <j.d.karch@fsw.leidenuniv.nl>

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Package phylolm.hp updated to version 0.0-3 with previous version 0.0-2 dated 2024-11-23

Title: Hierarchical Partitioning of R2 for Phylogenetic Linear Regression
Description: Conducts hierarchical partitioning to calculate individual contributions of phylogenetic tree and predictors (groups) towards total R2 for phylogenetic linear regression models.
Author: Jiangshan Lai [aut, cre] , Gang Wang [aut]
Maintainer: Jiangshan Lai <lai@njfu.edu.cn>

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Package lavaangui updated to version 0.2.1 with previous version 0.2.0 dated 2024-10-16

Title: Graphical User Interface with Integrated 'Diagrammer' for 'Lavaan'
Description: Provides a graphical user interface with an integrated diagrammer for latent variables from the 'lavaan' package. It offers two core functions: first, lavaangui() launches a web application that allows users to specify models by drawing path diagrams, fitting them, assessing model fit, and more; second, plot_lavaan() creates interactive path diagrams from models specified in 'lavaan'. Karch (2024) <doi: 10.31234/osf.io/f4ary> contains a tutorial.
Author: Julian D. Karch [aut, cre, cph]
Maintainer: Julian D. Karch <j.d.karch@fsw.leidenuniv.nl>

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Package gosset updated to version 1.4 with previous version 1.3 dated 2024-05-27

Title: Tools for Data Analysis in Experimental Agriculture
Description: Methods to analyse experimental agriculture data, from data synthesis to model selection and visualisation. The package is named after W.S. Gosset aka ‘Student’, a pioneer of modern statistics in small sample experimental design and analysis.
Author: Kaue de Sousa [aut, cre] , Jacob van Etten [aut] , David Brown [aut] , Jonathan Steinke [aut] , Joost van Heerwaarden [aut]
Maintainer: Kaue de Sousa <desousa.kaue@gmail.com>

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Package ebvcube updated to version 0.3.1 with previous version 0.2.3 dated 2024-07-10

Title: Working with netCDF for Essential Biodiversity Variables
Description: The concept of Essential Biodiversity Variables (EBV, <https://geobon.org/ebvs/what-are-ebvs/>) comes with a data structure based on the Network Common Data Form (netCDF). The 'ebvcube' 'R' package provides functionality to easily create, access and visualise this data. The EBV netCDFs can be downloaded from the EBV Data Portal: Christian Langer/ iDiv (2020) <https://portal.geobon.org/>.
Author: Luise Quoss [aut, cre] , Nestor Fernandez [aut] , Christian Langer [aut] , Jose Valdez [aut] , Henrique Miguel Pereira [aut]
Maintainer: Luise Quoss <luise.quoss@idiv.de>

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Package clickR updated to version 0.9.45 with previous version 0.9.43 dated 2024-09-17

Title: Semi-Automatic Preprocessing of Messy Data with Change Tracking for Dataset Cleaning
Description: Tools for assessing data quality, performing exploratory analysis, and semi-automatic preprocessing of messy data with change tracking for integral dataset cleaning.
Author: David Hervas Marin [aut, cre]
Maintainer: David Hervas Marin <ddhervas@yahoo.es>

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Package ETRep updated to version 1.1.0 with previous version 0.1.0 dated 2024-11-04

Title: Analysis of Elliptical Tubes Under the Relative Curvature Condition
Description: Analysis of elliptical tubes with applications in biological modeling. The package is based on the references: Taheri, M., Pizer, S. M., & Schulz, J. (2024) "The Mean Shape under the Relative Curvature Condition." arXiv <doi:10.48550/arXiv.2404.01043>. Mohsen Taheri Shalmani (2024) "Shape Statistics via Skeletal Structures", PhD Thesis, University of Stavanger, Norway <doi:10.13140/RG.2.2.34500.23685>. Key features include constructing discrete elliptical tubes, calculating transformations, validating structures under the Relative Curvature Condition (RCC), computing means, and generating simulations. Supports intrinsic and non-intrinsic mean calculations and transformations, size estimation, plotting, and random sample generation based on a reference tube. The intrinsic approach relies on the interior path of the original non-convex space, incorporating the RCC, while the non-intrinsic approach uses a basic robotic arm transformation that disregards the RCC.
Author: Mohsen Taheri Shalmani [aut, cre] , Joern Schulz [aut], Stephen M. Pizer [aut]
Maintainer: Mohsen Taheri Shalmani <MohsenTaheriShalmani@gmail.com>

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Package iClusterVB (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:

2024-12-04 0.1.3

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Package mobsim updated to version 0.3.2 with previous version 0.3.1 dated 2024-03-22

Title: Spatial Simulation and Scale-Dependent Analysis of Biodiversity Changes
Description: Simulation, analysis and sampling of spatial biodiversity data (May, Gerstner, McGlinn, Xiao & Chase 2017) <doi:10.1111/2041-210x.12986>. In the simulation tools user define the numbers of species and individuals, the species abundance distribution and species aggregation. Functions for analysis include species rarefaction and accumulation curves, species-area relationships and the distance decay of similarity.
Author: Felix May [aut, cre, cph] , Alban Sagouis [aut]
Maintainer: Felix May <felix.may@fu-berlin.de>

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Package bgms updated to version 0.1.4.2 with previous version 0.1.4.1 dated 2024-11-12

Title: Bayesian Analysis of Networks of Binary and/or Ordinal Variables
Description: Bayesian variable selection methods for analyzing the structure of a Markov Random Field model for a network of binary and/or ordinal variables. Details of the implemented methods can be found in: Marsman, van den Bergh, and Haslbeck (in press) <doi:10.31234/osf.io/ukwrf>.
Author: Maarten Marsman [aut, cre] , Karoline Huth [ctb] , Nikola Sekulovski [ctb] , Don van den Bergh [ctb]
Maintainer: Maarten Marsman <m.marsman@uva.nl>

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Package XDNUTS updated to version 1.5.4 with previous version 1.5.3 dated 2024-11-29

Title: Discontinuous Hamiltonian Monte Carlo with Varying Trajectory Length
Description: Hamiltonian Monte Carlo for both continuous and discontinuous posterior distributions with customisable trajectory length termination criterion. See Nishimura et al. (2020) <doi:10.1093/biomet/asz083> for the original Discontinuous Hamiltonian Monte Carlo, Hoffman et al. (2014) <doi:10.48550/arXiv.1111.4246> and Betancourt (2016) <doi:10.48550/arXiv.1601.00225> for the definition of possible Hamiltonian Monte Carlo termination criteria.
Author: Paolo Manildo [aut, cre]
Maintainer: Paolo Manildo <paolo.manildo@studenti.unipd.it>

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Package text updated to version 1.3.0 with previous version 1.2.3 dated 2024-07-29

Title: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning
Description: Link R with Transformers from Hugging Face to transform text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see <https://www.r-text.org>.
Author: Oscar Kjell [aut, cre] , Salvatore Giorgi [aut] , Andrew Schwartz [aut]
Maintainer: Oscar Kjell <oscar.kjell@psy.lu.se>

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Package StepReg updated to version 1.5.6 with previous version 1.5.5 dated 2024-11-04

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 N Nayak [ctb], Cesar Bautista Sotelo [ctb], Michael A Lodato [ctb], Robert H Brown [ctb], Wenxin Liu [aut], Lihua Julie Zhu [aut]
Maintainer: Junhui Li <junhui.li11@umassmed.edu>

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Package spmoran updated to version 0.3.3 with previous version 0.3.2 dated 2024-12-03

Title: Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors
Description: A collection of functions for estimating spatial and spatio-temporal regression models. Moran eigenvectors are used as spatial basis functions to efficiently approximate spatially dependent Gaussian processes (i.e., random effects eigenvector spatial filtering; see Murakami and Griffith 2015 <doi: 10.1007/s10109-015-0213-7>). The implemented models include linear regression with residual spatial dependence, spatially/spatio-temporally varying coefficient models (Murakami et al., 2017, 2024; <doi:10.1016/j.spasta.2016.12.001>,<doi:10.48550/arXiv.2410.07229>), spatially filtered unconditional quantile regression (Murakami and Seya, 2019 <doi:10.1002/env.2556>), Gaussian and non-Gaussian spatial mixed models through compositionally-warping (Murakami et al. 2021, <doi:10.1016/j.spasta.2021.100520>).
Author: Daisuke Murakami [aut, cre]
Maintainer: Daisuke Murakami <dmuraka@ism.ac.jp>

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Package sate updated to version 2.2.1 with previous version 2.2.0 dated 2024-09-11

Title: Scientific Analysis of Trial Errors (SATE)
Description: Bundles functions used to analyze the harmfulness of trial errors in criminal trials. Functions in the Scientific Analysis of Trial Errors ('SATE') package help users estimate the probability that a jury will find a defendant guilty given jurors' preferences for a guilty verdict and the uncertainty of that estimate. Users can also compare actual and hypothetical trial conditions to conduct harmful error analysis. The relationship between individual jurors' verdict preferences and the probability that a jury returns a guilty verdict has been studied by Davis (1973) <doi:10.1037/h0033951>; MacCoun & Kerr (1988) <doi:10.1037/0022-3514.54.1.21>, and Devine et el. (2001) <doi:10.1037/1076-8971.7.3.622>, among others.
Author: Barry Edwards [aut, cre]
Maintainer: Barry Edwards <bce@uga.edu>

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Package RCPA3 updated to version 1.3.1 with previous version 1.2.1 dated 2024-01-15

Title: Data and Functions for R Companion to Political Analysis 3rd Ed
Description: Bundles the datasets and functions featured in Philip H. Pollock and Barry C. Edwards<https://edge.sagepub.com/pollock>, "An R Companion to Political Analysis, 3rd Edition," Thousand Oaks, CA: Sage Publications.
Author: Barry Edwards [aut, cre]
Maintainer: Barry Edwards <bce@uga.edu>

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Package pipeflow updated to version 0.2.1 with previous version 0.1.1 dated 2024-12-02

Title: Lightweight, General-Purpose Data Analysis Pipelines
Description: A lightweight yet powerful framework for building robust data analysis pipelines. With 'pipeflow', you initialize a pipeline with your dataset and construct your workflow step by step by seamlessly adding R functions. Modify, remove, or insert steps at any stage while 'pipeflow' ensures the integrity and correctness of your pipeline. Designed to help you focus on the 'what' rather than the 'how', this package simplifies the implementation of complex workflows, making even large-scale data analysis projects manageable, adaptable, and reusable with ease.
Author: Roman Pahl [aut, cre]
Maintainer: Roman Pahl <roman.pahl@gmail.com>

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Package lmodel2 updated to version 1.7-4 with previous version 1.7-3 dated 2018-02-05

Title: Model II Regression
Description: Computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis (RMA).
Author: Pierre Legendre [aut], Jari Oksanen [cre]
Maintainer: Jari Oksanen <jhoksane@gmail.com>

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Package IDLFM updated to version 0.0.2 with previous version 0.0.1 dated 2024-12-02

Title: Individual Dynamic Latent Factor Model
Description: A personalized dynamic latent factor model (Zhang et al. (2024) <doi:10.1093/biomet/asae015>) for irregular multi-resolution time series data, to interpolate unsampled measurements from low-resolution time series.
Author: Siyang Liu [aut, cre], Jiuchen Zhang [aut], Annie Qu [aut]
Maintainer: Siyang Liu <liusiyang.lucia@gmail.com>

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Package Directional updated to version 7.0 with previous version 6.9 dated 2024-10-29

Title: A Collection of Functions for Directional Data Analysis
Description: A collection of functions for directional data (including massive data, with millions of observations) analysis. Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000). Other references include: a) Paine J.P., Preston S.P., Tsagris M. and Wood A.T.A. (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) Paine J.P., Preston S.P., Tsagris M. and Wood A.T.A. (2020). "Spherical regression models with general covariates and anisotropic errors". Statistics and Computing 30(1): 153--165. <doi:10.1007/s11222-019-09872 [...truncated...]
Author: Michail Tsagris [aut, cre], Giorgos Athineou [aut], Christos Adam [aut], Zehao Yu [aut], Anamul Sajib [ctb], Eli Amson [ctb], Micah J. Waldstein [ctb], Panagiotis Papastamoulis [ctb]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

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Package biogas updated to version 1.61 with previous version 1.23.2 dated 2020-01-08

Title: Process Biogas Data and Predict Biogas Production
Description: High- and low-level functions for processing biogas data and predicting biogas production. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to (possibly user-defined) standard temperature and pressure. Gas quantity can be converted between volume, mass, and moles. Gas composition, cumulative production, or other variables can be interpolated to a specified time. Cumulative biogas and methane production (and rates) can be calculated from raw data obtained using volumetric, manometric, gravimetric, or gas density methods for any number of bottles. With cumulative methane production data and data on bottle contents, biochemical methane potential (BMP) or specific methane production (SMP) can be calculated and summarized, including subtraction of the inoculum contribution and normalization by substrate mass. Cumulative production and production rates can be summarized in several different ways [...truncated...]
Author: Sasha D. Hafner [aut, cre] , Charlotte Rennuit [aut], Camilla Justesen [aut], Nanna Lojborg [aut], Jacob Mortensen [aut], Jonas Ohlsson [aut], Sergi Astals [ctb], Konrad Koch [ctb], Soeren Weinrich [ctb], Jin Mi Triolo [ctb], Ali Heidarzadeh Vazifehk [...truncated...]
Maintainer: Sasha D. Hafner <sasha.hafner@bce.au.dk>

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More information about biogas at CRAN
Permanent link

Package arrow updated to version 18.1.0 with previous version 17.0.0.1 dated 2024-08-21

Title: Integration to 'Apache' 'Arrow'
Description: 'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. This package provides an interface to the 'Arrow C++' library.
Author: Neal Richardson [aut], Ian Cook [aut], Nic Crane [aut], Dewey Dunnington [aut] , Romain Francois [aut] , Jonathan Keane [aut, cre], Dragoș Moldovan-Gruenfeld [aut], Jeroen Ooms [aut], Jacob Wujciak-Jens [aut], Javier Luraschi [ctb], Karl Dunkle Werner [...truncated...]
Maintainer: Jonathan Keane <jkeane@gmail.com>

Diff between arrow versions 17.0.0.1 dated 2024-08-21 and 18.1.0 dated 2024-12-05

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 arrow-18.1.0/arrow/DESCRIPTION                                                         |    8 
 arrow-18.1.0/arrow/MD5                                                                 |  802 +++----
 arrow-18.1.0/arrow/NEWS.md                                                             |   27 
 arrow-18.1.0/arrow/R/arrow-package.R                                                   |    2 
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 arrow-18.1.0/arrow/R/dplyr-funcs-doc.R                                                 |    2 
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 arrow-18.1.0/arrow/R/dplyr-slice.R                                                     |    2 
 arrow-18.1.0/arrow/R/dplyr-summarize.R                                                 |    2 
 arrow-18.1.0/arrow/R/duckdb.R                                                          |    8 
 arrow-18.1.0/arrow/R/metadata.R                                                        |   21 
 arrow-18.1.0/arrow/R/parquet.R                                                         |    7 
 arrow-18.1.0/arrow/README.md                                                           |   15 
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 arrow-18.1.0/arrow/man/to_arrow.Rd                                                     |    9 
 arrow-18.1.0/arrow/src/altrep.cpp                                                      |   14 
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 arrow-18.1.0/arrow/tools/cpp/cmake_modules/BuildUtils.cmake                            |    5 
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More information about arrow at CRAN
Permanent link

Package VBel updated to version 1.1.0 with previous version 1.0.1 dated 2024-06-04

Title: Variational Bayes for Fast and Accurate Empirical Likelihood Inference
Description: Computes the Gaussian variational approximation of the Bayesian empirical likelihood posterior. This is an implementation of the function found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.
Author: Weichang Yu [aut] , Jeremy Lim [cre, aut]
Maintainer: Jeremy Lim <jeremy.lim@unimelb.edu.au>

Diff between VBel versions 1.0.1 dated 2024-06-04 and 1.1.0 dated 2024-12-05

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More information about VBel at CRAN
Permanent link

Package PoolTestR updated to version 0.2.0 with previous version 0.1.3 dated 2022-07-01

Title: Prevalence and Regression for Pool-Tested (Group-Tested) Data
Description: An easy-to-use tool for working with presence/absence tests on 'pooled' or 'grouped' samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, 'PoolScreen' <https://sites.uab.edu/statgenetics/software/>. However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. <DOI:10.1016/j.envsoft.2021.105158>). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, [...truncated...]
Author: Angus McLure [aut, cre] , Caitlin Cherryh [ctb]
Maintainer: Angus McLure <angus.mclure@anu.edu.au>

Diff between PoolTestR versions 0.1.3 dated 2022-07-01 and 0.2.0 dated 2024-12-05

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More information about PoolTestR at CRAN
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Package double.truncation updated to version 1.8 with previous version 1.7 dated 2020-09-08

Title: Analysis of Doubly-Truncated Data
Description: Likelihood-based inference methods with doubly-truncated data are developed under various models. Nonparametric models are based on Efron and Petrosian (1999) <doi:10.1080/01621459.1999.10474187> and Emura, Konno, and Michimae (2015) <doi:10.1007/s10985-014-9297-5>. Parametric models from the special exponential family (SEF) are based on Hu and Emura (2015) <doi:10.1007/s00180-015-0564-z> and Emura, Hu and Konno (2017) <doi:10.1007/s00362-015-0730-y>. The parametric location-scale models are based on Dorre et al. (2021) <doi:10.1007/s00180-020-01027-6>.
Author: Takeshi Emura [aut, cre], Ya-Hsuan Hu [aut], Chung-Yan Huang [aut]
Maintainer: Takeshi Emura <takeshiemura@gmail.com>

Diff between double.truncation versions 1.7 dated 2020-09-08 and 1.8 dated 2024-12-05

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Package avesperu updated to version 0.0.3 with previous version 0.0.2 dated 2024-03-09

Title: Access to the List of Birds Species of Peru
Description: Allows access to the data found in the species list featured in the renowned 'List of the Birds of Peru' Plenge, M. A. (2023) <https://sites.google.com/site/boletinunop/checklist>. This publication stands as one of Peru's most comprehensive reviews of bird diversity. The dataset incorporates detailed species accounts and has been meticulously structured for effortless utilization within the R environment.
Author: Paul E. Santos Andrade [aut, cre]
Maintainer: Paul E. Santos Andrade <paulefrens@gmail.com>

Diff between avesperu versions 0.0.2 dated 2024-03-09 and 0.0.3 dated 2024-12-05

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More information about avesperu at CRAN
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