Mon, 11 May 2026

Package fdth updated to version 1.3-4 with previous version 1.3-0 dated 2023-11-17

Title: Frequency Distribution Tables, Histograms and Polygons
Description: Perform frequency distribution tables, associated histograms and polygons from vector, data.frame and matrix objects for numerical and categorical variables.
Author: Jose C. Faria [aut, cre], Ivan B. Allaman [aut], Enio G. Jelihovschi [aut]
Maintainer: Jose C. Faria <joseclaudio.faria@gmail.com>

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Package psborrow2 updated to version 0.0.5.0 with previous version 0.0.4.0 dated 2025-02-12

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

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Package OmopStudyBuilder updated to version 0.1.1 with previous version 0.1.0 dated 2026-04-28

Title: Build Reproducible Network Studies for OMOP Common Data Model
Description: Streamlines the setup and execution of network studies using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Creates standardised project structures with template code, manages dependencies with 'renv', provides code review utilities, and supports containerised execution with 'Docker' for reproducible multi-site studies. Includes 'GitHub' integration for collaboration and version control.
Author: Folu Akintola [aut, cre] , Edward Burn [aut] , Marti Catala [aut] , Marta Alcalde-Herraiz [ctb]
Maintainer: Folu Akintola <folu.akintola@ndorms.ox.ac.uk>

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Package multiview updated to version 1.0 with previous version 0.8 dated 2023-03-31

Title: Cooperative Learning for Multi-View Analysis
Description: Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).
Author: Daisy Yi Ding [aut], Robert J. Tibshirani [aut], Balasubramanian Narasimhan [aut, cre], Trevor Hastie [aut], Kenneth Tay [aut], James Yang [aut], Jonathan Taylor [aut]
Maintainer: Balasubramanian Narasimhan <naras@stanford.edu>

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Package f1pits updated to version 1.3.0 with previous version 1.2.0 dated 2026-04-02

Title: F1 Pit Stop Datasets
Description: Formula 1 pit stop data. The package provides information on teams and drivers across seasons (2019 or higher). It also includes a function to visualize pit stop performance.
Author: Jose Jordan-Soria [aut, cre]
Maintainer: Jose Jordan-Soria <jjose.jjordan@gmail.com>

Diff between f1pits versions 1.2.0 dated 2026-04-02 and 1.3.0 dated 2026-05-11

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Package bpca updated to version 1.4-3 with previous version 1.3-9 dated 2026-04-21

Title: Biplot Analysis for Multivariate Data Using Principal Components
Description: Provides tools for creating 2D and 3D biplots of multivariate data based on principal components analysis, together with diagnostics for reduction quality and enhanced visualization of variables and objects.
Author: Jose C. Faria [aut, cre], Ivan B. Allaman [aut], Clarice G. B. Demetrio [aut]
Maintainer: Jose C. Faria <joseclaudio.faria@gmail.com>

Diff between bpca versions 1.3-9 dated 2026-04-21 and 1.4-3 dated 2026-05-11

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New package stepssurvey with initial version 0.1.0
Package: stepssurvey
Title: Analyse WHO STEPS Survey Data
Version: 0.1.0
Description: Provides a complete analysis pipeline for the WHO STEPwise Approach to NCD Risk Factor Surveillance (STEPS) as described in Riley et al. (2016) <doi:10.2105/AJPH.2015.302962>. Imports raw survey data ('CSV', 'Excel', 'Stata', 'SPSS'), applies WHO-standard cleaning and recoding, sets up complex survey designs, computes all standard NCD indicators (tobacco, alcohol, diet, physical activity, anthropometry, blood pressure, biochemical), and generates publication-ready tables, visualisations, and 'Word'/'HTML' reports (fact sheet, data book, country report).
License: MIT + file LICENSE
URL: https://github.com/drpakhare/stepssurvey
BugReports: https://github.com/drpakhare/stepssurvey/issues
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: bslib, dplyr, DT, flextable, ggplot2, glue, haven, janitor, patchwork, purrr, readr, readxl, rmarkdown, shiny, survey, tools
Suggests: knitr, remotes, testthat (>= 3.0.0), withr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-06 08:20:10 UTC; drpakhare
Author: Abhijit Pakhare [aut, cre], Ankur Joshi [aut], Lena Charlette [aut], WHO STEPS R Pipeline Contributors [ctb]
Maintainer: Abhijit Pakhare <abhijit.cfm@aiimsbhopal.edu.in>
Repository: CRAN
Date/Publication: 2026-05-11 19:10:03 UTC

More information about stepssurvey at CRAN
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New package spatialRegroup with initial version 0.1.0
Package: spatialRegroup
Title: Iterative Spatial Regrouping of Administrative Units by Attributive Affinity
Version: 0.1.0
Description: Evaluates the statistical coherence of existing administrative partitions (e.g. inter-municipal groupings, districts) by identifying spatial units whose attributive profile is more similar to a neighbouring group than to their own. Border units are iteratively reassigned to the group they are most affine with, based on Euclidean or Mahalanobis distance computed on user-supplied numeric variables, with optional per-variable weighting and standardisation. Spatial contiguity is enforced throughout: isolated candidates are reintegrated into their original group, disconnected fragments are resolved, and empty groups are restored. Convergence is monitored via an eta-squared cohesion criterion. The resulting partition can be compared to the original administrative delineation using multilevel models, providing a quantitative measure of boundary inefficiency.
License: GPL-3
Depends: R (>= 4.1.0)
Encoding: UTF-8
Imports: dplyr, sf, spdep, igraph, rlang
NeedsCompilation: no
Packaged: 2026-05-06 08:49:27 UTC; User
Author: Nicolas Ausello [aut, cre]
Maintainer: Nicolas Ausello <nicolasausello@yahoo.fr>
Repository: CRAN
Date/Publication: 2026-05-11 19:10:09 UTC

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Package SmoothWin readmission to version 3.0.1 with previous version 3.0.0 dated 2019-07-27

Title: Soft Windowing for Linear and Non-Linear Models
Description: Fits symmetric soft windowing to linear and non-linear models by assigning exponential weights over time around specified modes; bandwidth and sharpness of the windows are chosen by a grid search and comparison diagnostics (Hamed Haselimashhadi et al (2019) <doi:10.1093/bioinformatics/btz744>).
Author: Hamed Haselimashhadi [aut, cre]
Maintainer: Hamed Haselimashhadi <hamedhaseli@gmail.com>

This is a re-admission after prior archival of version 3.0.0 dated 2019-07-27

Diff between SmoothWin versions 3.0.0 dated 2019-07-27 and 3.0.1 dated 2026-05-11

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Package rstudiothemes updated to version 1.1.1 with previous version 1.1.0 dated 2026-04-07

Title: Create 'RStudio' Themes from Visual Studio Code, Positron and 'TextMate' Themes
Description: Create and install 'RStudio' themes derived from Visual Studio Code, Positron and 'TextMate' themes. Provides functions to convert between 'TextMate' and Visual Studio Code or Positron themes, as well as ports of several Visual Studio Code themes.
Author: Diego Hernangomez [aut, cre, cph] , Garrick Aden-Buie [cph] function)
Maintainer: Diego Hernangomez <diego.hernangomezherrero@gmail.com>

Diff between rstudiothemes versions 1.1.0 dated 2026-04-07 and 1.1.1 dated 2026-05-11

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 NEWS.md                                                                 |   68 ++++----
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 R/convert-to-rs.R                                                       |   44 +----
 R/convert-vs-to-tm.R                                                    |   40 ++--
 R/on-rstudio.R                                                          |   84 +++++-----
 R/read-tm-theme.R                                                       |   26 +--
 R/read-vs-theme.R                                                       |   29 ---
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 inst/schemaorg.json                                                     |    4 
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 man/convert_vs_to_tm_theme.Rd                                           |    6 
 man/generate_uuid.Rd                                                    |    6 
 man/on_rstudio.Rd                                                       |   48 ++---
 man/read_tm_theme.Rd                                                    |    6 
 man/read_vs_theme.Rd                                                    |    6 
 man/rstudiothemes-package.Rd                                            |    5 
 tests/testthat.R                                                        |   24 +-
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Package mwcsr updated to version 0.1.11 with previous version 0.1.10 dated 2025-11-05

Title: Solvers for Maximum Weight Connected Subgraph Problem and Its Variants
Description: Algorithms for solving various Maximum Weight Connected Subgraph Problems, including variants with budget constraints, cardinality constraints, weighted edges and signals. The package represents an R interface to high-efficient solvers based on relax-and-cut approach (Álvarez-Miranda E., Sinnl M. (2017) <doi:10.1016/j.cor.2017.05.015>) mixed-integer programming (Loboda A., Artyomov M., and Sergushichev A. (2016) <doi:10.1007/978-3-319-43681-4_17>) and simulated annealing.
Author: Alexander Loboda [aut, cre], Nikolay Poperechnyi [aut], Eduardo Alvarez-Miranda [aut], Markus Sinnl [aut], Alexey Sergushichev [aut], Paul Hosler Jr. [cph] , www.hamcrest.org [cph] , Barak Naveh and Contributors [cph] , The Apache Software Foundation [...truncated...]
Maintainer: Alexander Loboda <aleks.loboda@gmail.com>

Diff between mwcsr versions 0.1.10 dated 2025-11-05 and 0.1.11 dated 2026-05-11

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New package gcomputation with initial version 0.34
Package: gcomputation
Title: Causal Inference by using G-Computation
Version: 0.34
Depends: R (>= 4.0.0), survival, hdnom, glmnet, MASS, mice
Imports: graphics, utils, methods, grDevices, stats
Description: Several functions and S3 methods for G-computation and emulation of clinical trials. It allows for flexible estimation of the outcome model, especially penalized regressions (Lasso, Ridge, or Elasticnet) for binary, continuous, counting, or right-censored time-to-event outcomes. Average treatment effect among the entire population (ATE) or among the treated population (ATT) can be estimated. The method for time-to-events is described by Chatton et al. (2020) <doi:10.1038/s41598-020-65917-x>. For a binary outcome, details are available in the paper proposed by Chatton et al. (2022) <doi:10.1177/09622802211047345>.
License: GPL (>= 2)
Encoding: UTF-8
LazyLoad: yes
NeedsCompilation: no
Maintainer: Yohann Foucher <yohann.foucher@univ-poitiers.fr>
BugReports: https://github.com/chupverse/gcomputation/issues
Packaged: 2026-05-06 13:05:33 UTC; foucher-y
Author: Yohann Foucher [aut, cre] , Joe De Keizer [aut]
Repository: CRAN
Date/Publication: 2026-05-11 19:20:02 UTC

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New package gamutil with initial version 0.8.1
Package: gamutil
Title: Utilities to Facilitate Modeling Routines with Generalized Additive Models
Version: 0.8.1
Description: After fitting a Generalized Additive (Mixed) Model, the next step is often to obtain predicted values for certain combinations of predictors for visualization of estimated effects in the model. It involves constructing a new data frame, add predicted values, and finally makes a (contour) plot. This package is intended to facilitate these steps to visualize estimated effects in a generalized additive model. The underlying modeling methodology is described in Wood (2017, ISBN:9781498728331).
Imports: ggplot2, lifecycle, metR, mgcv, RColorBrewer
License: MIT + file LICENSE
URL: https://github.com/msaito8623/gamutil
BugReports: https://github.com/msaito8623/gamutil/issues
Encoding: UTF-8
Language: en-US
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, RhpcBLASctl
Depends: R (>= 3.5)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-06 08:18:14 UTC; motoki
Author: Motoki Saito [aut, cre]
Maintainer: Motoki Saito <motokisaito.8623@gmail.com>
Repository: CRAN
Date/Publication: 2026-05-11 19:10:19 UTC

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Package dataquieR updated to version 2.8.9 with previous version 2.8.7 dated 2026-01-08

Title: Data Quality in Epidemiological Research
Description: Data quality assessments guided by a 'data quality framework introduced by Schmidt and colleagues, 2021' <doi:10.1186/s12874-021-01252-7> target the data quality dimensions integrity, completeness, consistency, and accuracy. The scope of applicable functions rests on the availability of extensive metadata which can be provided in spreadsheet tables. Either standardized (e.g. as 'html5' reports) or individually tailored reports can be generated. For an introduction into the specification of corresponding metadata, please refer to the 'package website' <https://dataquality.qihs.uni-greifswald.de/VIN_Annotation_of_Metadata.html>.
Author: University Medicine Greifswald [cph], Elisa Kasbohm [aut] , Elena Salogni [aut] , Joany Marino [aut] , Adrian Richter [aut] , Carsten Oliver Schmidt [aut] , Stephan Struckmann [aut, cre] , German Research Foundation [fnd], National Research Data Inf [...truncated...]
Maintainer: Stephan Struckmann <stephan.struckmann@uni-greifswald.de>

Diff between dataquieR versions 2.8.7 dated 2026-01-08 and 2.8.9 dated 2026-05-11

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 dataquieR-2.8.9/dataquieR/DESCRIPTION                                                                           |   16 
 dataquieR-2.8.9/dataquieR/MD5                                                                                   |  929 +-
 dataquieR-2.8.9/dataquieR/NAMESPACE                                                                             |   10 
 dataquieR-2.8.9/dataquieR/NEWS.md                                                                               |   39 
 dataquieR-2.8.9/dataquieR/R/000_globs.R                                                                         |   37 
 dataquieR-2.8.9/dataquieR/R/000_options.R                                                                       |   37 
 dataquieR-2.8.9/dataquieR/R/acc_cat_distributions.R                                                             |    3 
 dataquieR-2.8.9/dataquieR/R/acc_distributions.R                                                                 |   17 
 dataquieR-2.8.9/dataquieR/R/acc_distributions_ecdf.R                                                            |    4 
 dataquieR-2.8.9/dataquieR/R/acc_loess.R                                                                         |   64 
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 dataquieR-2.8.9/dataquieR/R/acc_mahalanobis_ratio.R                                                             |only
 dataquieR-2.8.9/dataquieR/R/acc_margins.R                                                                       |   40 
 dataquieR-2.8.9/dataquieR/R/acc_multivariate_outlier.R                                                          |    3 
 dataquieR-2.8.9/dataquieR/R/acc_shape_or_scale.R                                                                |   32 
 dataquieR-2.8.9/dataquieR/R/acc_univariate_outlier.R                                                            |    4 
 dataquieR-2.8.9/dataquieR/R/acc_varcomp.R                                                                       |   16 
 dataquieR-2.8.9/dataquieR/R/com_item_missingness.R                                                              |    3 
 dataquieR-2.8.9/dataquieR/R/com_qualified_item_missingness.R                                                    |   49 
 dataquieR-2.8.9/dataquieR/R/com_segment_missingness.R                                                           |   10 
 dataquieR-2.8.9/dataquieR/R/con_contradictions.R                                                                |    4 
 dataquieR-2.8.9/dataquieR/R/con_contradictions_redcap.R                                                         |    4 
 dataquieR-2.8.9/dataquieR/R/con_inadmissible_categorical.R                                                      |   11 
 dataquieR-2.8.9/dataquieR/R/con_limit_deviations.R                                                              |    6 
 dataquieR-2.8.9/dataquieR/R/des_scatterplot_matrix.R                                                            |   28 
 dataquieR-2.8.9/dataquieR/R/dq_report2.R                                                                        |  136 
 dataquieR-2.8.9/dataquieR/R/dq_report_by.R                                                                      |  318 
 dataquieR-2.8.9/dataquieR/R/html_dependency_dataquieR.R                                                         |    6 
 dataquieR-2.8.9/dataquieR/R/int_all_datastructure_segment.R                                                     |   29 
 dataquieR-2.8.9/dataquieR/R/int_datatype_matrix.R                                                               |    6 
 dataquieR-2.8.9/dataquieR/R/int_encoding_errors.R                                                               |    3 
 dataquieR-2.8.9/dataquieR/R/menu_env.R                                                                          |   25 
 dataquieR-2.8.9/dataquieR/R/meta_data_env.R                                                                     |    4 
 dataquieR-2.8.9/dataquieR/R/plot.dataquieR_summary.R                                                            |   48 
 dataquieR-2.8.9/dataquieR/R/prep_add_computed_variables.R                                                       |    9 
 dataquieR-2.8.9/dataquieR/R/prep_add_data_frames.R                                                              |   16 
 dataquieR-2.8.9/dataquieR/R/prep_add_missing_codes.R                                                            |  190 
 dataquieR-2.8.9/dataquieR/R/prep_get_data_frame.R                                                               |   17 
 dataquieR-2.8.9/dataquieR/R/prep_load_folder_with_metadata.R                                                    |    8 
 dataquieR-2.8.9/dataquieR/R/prep_load_workbook_like_file.R                                                      |   44 
 dataquieR-2.8.9/dataquieR/R/prep_map_labels.R                                                                   |    4 
 dataquieR-2.8.9/dataquieR/R/prep_meta_data_v1_to_item_level_meta_data.R                                         |    4 
 dataquieR-2.8.9/dataquieR/R/prep_pmap.R                                                                         |    2 
 dataquieR-2.8.9/dataquieR/R/prep_prepare_dataframes.R                                                           |   51 
 dataquieR-2.8.9/dataquieR/R/prep_purge_data_frame_cache.R                                                       |    2 
 dataquieR-2.8.9/dataquieR/R/prep_render_pie_chart_from_summaryclasses_ggplot2.R                                 |   10 
 dataquieR-2.8.9/dataquieR/R/prep_render_pie_chart_from_summaryclasses_plotly.R                                  |   12 
 dataquieR-2.8.9/dataquieR/R/print.ReportSummaryTable.R                                                          |   28 
 dataquieR-2.8.9/dataquieR/R/print.dataquieR_result.R                                                            |    5 
 dataquieR-2.8.9/dataquieR/R/print.dataquieR_resultset2.R                                                        | 1429 +++
 dataquieR-2.8.9/dataquieR/R/print.dataquieR_summary.R                                                           |    6 
 dataquieR-2.8.9/dataquieR/R/pro_applicability_matrix.R                                                          |    4 
 dataquieR-2.8.9/dataquieR/R/reflection.R                                                                        |   26 
 dataquieR-2.8.9/dataquieR/R/summary.dataquieR_resultset2.R                                                      |    2 
 dataquieR-2.8.9/dataquieR/R/util_acc_loess_bin.R                                                                |   74 
 dataquieR-2.8.9/dataquieR/R/util_acc_loess_continuous.R                                                         |   76 
 dataquieR-2.8.9/dataquieR/R/util_acc_varcomp.R                                                                  |   10 
 dataquieR-2.8.9/dataquieR/R/util_add_computed_internals.R                                                       |   24 
 dataquieR-2.8.9/dataquieR/R/util_bar_plot.R                                                                     |    2 
 dataquieR-2.8.9/dataquieR/R/util_call_with_only_existing_formals.R                                              |only
 dataquieR-2.8.9/dataquieR/R/util_combine_list_report_summaries.R                                                |   26 
 dataquieR-2.8.9/dataquieR/R/util_combine_res.R                                                                  |   13 
 dataquieR-2.8.9/dataquieR/R/util_condition_constructor_factory.R                                                |   30 
 dataquieR-2.8.9/dataquieR/R/util_correct_variable_use.R                                                         | 1588 ++-
 dataquieR-2.8.9/dataquieR/R/util_create_mahalanobis_ggplot.R                                                    |only
 dataquieR-2.8.9/dataquieR/R/util_create_page_file.R                                                             |   17 
 dataquieR-2.8.9/dataquieR/R/util_create_report_by_overview.R                                                    | 1083 +-
 dataquieR-2.8.9/dataquieR/R/util_dashboard_table.R                                                              |   70 
 dataquieR-2.8.9/dataquieR/R/util_df_escape.R                                                                    |    3 
 dataquieR-2.8.9/dataquieR/R/util_duplicated_inclding_first.R                                                    |    2 
 dataquieR-2.8.9/dataquieR/R/util_ensure_label.R                                                                 |   32 
 dataquieR-2.8.9/dataquieR/R/util_ensure_suggested.R                                                             |   20 
 dataquieR-2.8.9/dataquieR/R/util_error.R                                                                        |    2 
 dataquieR-2.8.9/dataquieR/R/util_eval_rule.R                                                                    |    2 
 dataquieR-2.8.9/dataquieR/R/util_evaluate_calls.R                                                               |   11 
 dataquieR-2.8.9/dataquieR/R/util_expand_pattern_rules.R                                                         |only
 dataquieR-2.8.9/dataquieR/R/util_expect_data_frame.R                                                            |   17 
 dataquieR-2.8.9/dataquieR/R/util_expect_scalar.R                                                                |   22 
 dataquieR-2.8.9/dataquieR/R/util_extract_all_ids.R                                                              |    2 
 dataquieR-2.8.9/dataquieR/R/util_filter_names_by_regexps.R                                                      |   55 
 dataquieR-2.8.9/dataquieR/R/util_finalize_sizing_hints.R                                                        |  231 
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 dataquieR-2.8.9/dataquieR/R/util_fix_encoding.R                                                                 |only
 dataquieR-2.8.9/dataquieR/R/util_fix_merge_dups.R                                                               |    9 
 dataquieR-2.8.9/dataquieR/R/util_float_index_menu.R                                                             |    3 
 dataquieR-2.8.9/dataquieR/R/util_generate_calls.R                                                               |   18 
 dataquieR-2.8.9/dataquieR/R/util_generate_calls_for_function.R                                                  |   10 
 dataquieR-2.8.9/dataquieR/R/util_generate_mahalanobis_dist.R                                                    |only
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 dataquieR-2.8.9/dataquieR/R/util_geom_pointrange_robust.R                                                       |only
 dataquieR-2.8.9/dataquieR/R/util_get_concept_links.R                                                            |only
 dataquieR-2.8.9/dataquieR/R/util_get_redcap_rule_env.R                                                          |   39 
 dataquieR-2.8.9/dataquieR/R/util_get_thresholds.R                                                               |    2 
 dataquieR-2.8.9/dataquieR/R/util_heatmap_1th.R                                                                  |    4 
 dataquieR-2.8.9/dataquieR/R/util_histogram.R                                                                    |    6 
 dataquieR-2.8.9/dataquieR/R/util_html_for_dims.R                                                                |   46 
 dataquieR-2.8.9/dataquieR/R/util_html_for_var.R                                                                 |  117 
 dataquieR-2.8.9/dataquieR/R/util_html_table.R                                                                   | 1205 +-
 dataquieR-2.8.9/dataquieR/R/util_iframe_it_if_needed.R                                                          |   22 
 dataquieR-2.8.9/dataquieR/R/util_is_numeric_in.R                                                                |   23 
 dataquieR-2.8.9/dataquieR/R/util_is_try_error.R                                                                 |    2 
 dataquieR-2.8.9/dataquieR/R/util_map_labels.R                                                                   |    3 
 dataquieR-2.8.9/dataquieR/R/util_margins_bin.R                                                                  |  140 
 dataquieR-2.8.9/dataquieR/R/util_margins_lm.R                                                                   |   48 
 dataquieR-2.8.9/dataquieR/R/util_margins_nom.R                                                                  |    6 
 dataquieR-2.8.9/dataquieR/R/util_margins_ord.R                                                                  |    8 
 dataquieR-2.8.9/dataquieR/R/util_margins_poi.R                                                                  |   48 
 dataquieR-2.8.9/dataquieR/R/util_message.R                                                                      |    2 
 dataquieR-2.8.9/dataquieR/R/util_multinomial_ci.R                                                               |only
 dataquieR-2.8.9/dataquieR/R/util_normalize_clt.R                                                                |   23 
 dataquieR-2.8.9/dataquieR/R/util_normalize_cross_item.R                                                         |  171 
 dataquieR-2.8.9/dataquieR/R/util_normalize_path.R                                                               |only
 dataquieR-2.8.9/dataquieR/R/util_odm2dataquieR.R                                                                |only
 dataquieR-2.8.9/dataquieR/R/util_overwrite_if_requested.R                                                       |only
 dataquieR-2.8.9/dataquieR/R/util_pairs_matrix.R                                                                 |   41 
 dataquieR-2.8.9/dataquieR/R/util_par_pmap.R                                                                     |    2 
 dataquieR-2.8.9/dataquieR/R/util_plot_figure_plotly.R                                                           |    2 
 dataquieR-2.8.9/dataquieR/R/util_pretty_print.R                                                                 |  227 
 dataquieR-2.8.9/dataquieR/R/util_rbind.R                                                                        |   62 
 dataquieR-2.8.9/dataquieR/R/util_recycle.R                                                                      |only
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 dataquieR-2.8.9/dataquieR/R/util_render_table_dataquieR_summary.R                                               |  139 
 dataquieR-2.8.9/dataquieR/R/util_rio_import.R                                                                   |   19 
 dataquieR-2.8.9/dataquieR/R/util_round_to_decimal_places.R                                                      |   45 
 dataquieR-2.8.9/dataquieR/R/util_setup_dashboard.R                                                              |  114 
 dataquieR-2.8.9/dataquieR/R/util_setup_rstudio_job.R                                                            |  110 
 dataquieR-2.8.9/dataquieR/R/util_sort_by_order.R                                                                |   27 
 dataquieR-2.8.9/dataquieR/R/util_standardize_ordinal_codes.R                                                    |only
 dataquieR-2.8.9/dataquieR/R/util_subsample_cases.R                                                              |only
 dataquieR-2.8.9/dataquieR/R/util_table_rotator.R                                                                |    2 
 dataquieR-2.8.9/dataquieR/R/util_translate.R                                                                    |  189 
 dataquieR-2.8.9/dataquieR/R/util_verify_names.R                                                                 |    1 
 dataquieR-2.8.9/dataquieR/R/util_view_file.R                                                                    |   17 
 dataquieR-2.8.9/dataquieR/R/util_warning.R                                                                      |    2 
 dataquieR-2.8.9/dataquieR/R/zzz_globs.R                                                                         |  114 
 dataquieR-2.8.9/dataquieR/README.md                                                                             |    6 
 dataquieR-2.8.9/dataquieR/build/vignette.rds                                                                    |binary
 dataquieR-2.8.9/dataquieR/inst/WORDLIST                                                                         |    2 
 dataquieR-2.8.9/dataquieR/inst/abbreviationMetrics.rds                                                          |binary
 dataquieR-2.8.9/dataquieR/inst/computed_vars_ind_mapping.rds                                                    |binary
 dataquieR-2.8.9/dataquieR/inst/dqi.rds                                                                          |binary
 dataquieR-2.8.9/dataquieR/inst/grading_formats.xlsx                                                             |binary
 dataquieR-2.8.9/dataquieR/inst/grading_rulesets.xlsx                                                            |binary
 dataquieR-2.8.9/dataquieR/inst/hovertext.rds                                                                    |binary
 dataquieR-2.8.9/dataquieR/inst/implementations.rds                                                              |binary
 dataquieR-2.8.9/dataquieR/inst/menu/dataquieR_overview.css                                                      |only
 dataquieR-2.8.9/dataquieR/inst/menu/script.js                                                                   | 1736 +++-
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 dataquieR-2.8.9/dataquieR/inst/menu/script_toplevel.js                                                          | 4278 ++++++++--
 dataquieR-2.8.9/dataquieR/inst/menu/style.css                                                                   |  219 
 dataquieR-2.8.9/dataquieR/inst/menu/style_toplevel.css                                                          |  140 
 dataquieR-2.8.9/dataquieR/inst/mimetypes/media-types.RDS                                                        |binary
 dataquieR-2.8.9/dataquieR/inst/report-dt-style/report-dt-style.css                                              |    8 
 dataquieR-2.8.9/dataquieR/inst/report-dt-style/report_dt.js                                                     |  154 
 dataquieR-2.8.9/dataquieR/inst/ssi.rds                                                                          |binary
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 dataquieR-2.8.9/dataquieR/inst/templates/default/overview.html                                                  |    4 
 dataquieR-2.8.9/dataquieR/inst/templates/default/report.html                                                    |   84 
 dataquieR-2.8.9/dataquieR/inst/templates/default/single_indicator.html                                          |    4 
 dataquieR-2.8.9/dataquieR/inst/templates/default/single_indicator_with_menu.html                                |    8 
 dataquieR-2.8.9/dataquieR/inst/translations.rds                                                                 |binary
 dataquieR-2.8.9/dataquieR/inst/voc.rds                                                                          |binary
 dataquieR-2.8.9/dataquieR/man/API_VERSION.Rd                                                                    |    4 
 dataquieR-2.8.9/dataquieR/man/ASSOCIATION_DIRECTION.Rd                                                          |    8 
 dataquieR-2.8.9/dataquieR/man/ASSOCIATION_FORM.Rd                                                               |    8 
 dataquieR-2.8.9/dataquieR/man/ASSOCIATION_METRIC.Rd                                                             |    8 
 dataquieR-2.8.9/dataquieR/man/ASSOCIATION_RANGE.Rd                                                              |    8 
 dataquieR-2.8.9/dataquieR/man/CHECK_ID.Rd                                                                       |    8 
 dataquieR-2.8.9/dataquieR/man/CHECK_LABEL.Rd                                                                    |    8 
 dataquieR-2.8.9/dataquieR/man/CODE_CLASSES.Rd                                                                   |    5 
 dataquieR-2.8.9/dataquieR/man/CODE_LIST_TABLE.Rd                                                                |    7 
 dataquieR-2.8.9/dataquieR/man/CODE_ORDER.Rd                                                                     |    5 
 dataquieR-2.8.9/dataquieR/man/COMPUTATION_RULE.Rd                                                               |    5 
 dataquieR-2.8.9/dataquieR/man/COMPUTED_VARIABLE_ROLES.Rd                                                        |    6 
 dataquieR-2.8.9/dataquieR/man/CONTRADICTION_TERM.Rd                                                             |    8 
 dataquieR-2.8.9/dataquieR/man/CONTRADICTION_TYPE.Rd                                                             |    8 
 dataquieR-2.8.9/dataquieR/man/DATA_PREPARATION.Rd                                                               |    8 
 dataquieR-2.8.9/dataquieR/man/DATA_TYPES.Rd                                                                     |    5 
 dataquieR-2.8.9/dataquieR/man/DATA_TYPES_OF_R_TYPE.Rd                                                           |    5 
 dataquieR-2.8.9/dataquieR/man/DF_CODE.Rd                                                                        |    5 
 dataquieR-2.8.9/dataquieR/man/DF_ELEMENT_COUNT.Rd                                                               |    5 
 dataquieR-2.8.9/dataquieR/man/DF_ID_REF_TABLE.Rd                                                                |    5 
 dataquieR-2.8.9/dataquieR/man/DF_ID_VARS.Rd                                                                     |    5 
 dataquieR-2.8.9/dataquieR/man/DF_NAME.Rd                                                                        |    5 
 dataquieR-2.8.9/dataquieR/man/DF_RECORD_CHECK.Rd                                                                |    5 
 dataquieR-2.8.9/dataquieR/man/DF_RECORD_COUNT.Rd                                                                |    5 
 dataquieR-2.8.9/dataquieR/man/DF_UNIQUE_ID.Rd                                                                   |    5 
 dataquieR-2.8.9/dataquieR/man/DF_UNIQUE_ROWS.Rd                                                                 |    5 
 dataquieR-2.8.9/dataquieR/man/DISTRIBUTIONS.Rd                                                                  |    5 
 dataquieR-2.8.9/dataquieR/man/Descriptor.Rd                                                                     |    3 
 dataquieR-2.8.9/dataquieR/man/GOLDSTANDARD.Rd                                                                   |    8 
 dataquieR-2.8.9/dataquieR/man/IRV.Rd                                                                            |    6 
 dataquieR-2.8.9/dataquieR/man/Indicator.Rd                                                                      |    3 
 dataquieR-2.8.9/dataquieR/man/MAHALANOBIS_RATIO.Rd                                                              |only
 dataquieR-2.8.9/dataquieR/man/MAHALANOBIS_THRESHOLD.Rd                                                          |    8 
 dataquieR-2.8.9/dataquieR/man/MAXIMUM_LONG_STRING.Rd                                                            |    6 
 dataquieR-2.8.9/dataquieR/man/MISSING_CODE_RULES.Rd                                                             |only
 dataquieR-2.8.9/dataquieR/man/MISS_RESP.Rd                                                                      |    6 
 dataquieR-2.8.9/dataquieR/man/MULTIVARIATE_OUTLIER_CHECK.Rd                                                     |    8 
 dataquieR-2.8.9/dataquieR/man/MULTIVARIATE_OUTLIER_CHECKTYPE.Rd                                                 |    8 
 dataquieR-2.8.9/dataquieR/man/RELCOMPL_SPEED.Rd                                                                 |   44 
 dataquieR-2.8.9/dataquieR/man/REL_VAL.Rd                                                                        |    8 
 dataquieR-2.8.9/dataquieR/man/RESPT_PER_ITEM.Rd                                                                 |    6 
 dataquieR-2.8.9/dataquieR/man/SCALE_ACRONYM.Rd                                                                  |    8 
 dataquieR-2.8.9/dataquieR/man/SCALE_LEVELS.Rd                                                                   |    5 
 dataquieR-2.8.9/dataquieR/man/SCALE_NAME.Rd                                                                     |    8 
 dataquieR-2.8.9/dataquieR/man/SEGMENT_ID_REF_TABLE.Rd                                                           |    5 
 dataquieR-2.8.9/dataquieR/man/SEGMENT_ID_TABLE.Rd                                                               |    5 
 dataquieR-2.8.9/dataquieR/man/SEGMENT_ID_VARS.Rd                                                                |    5 
 dataquieR-2.8.9/dataquieR/man/SEGMENT_MISS.Rd                                                                   |    5 
 dataquieR-2.8.9/dataquieR/man/SEGMENT_PART_VARS.Rd                                                              |    5 
 dataquieR-2.8.9/dataquieR/man/SEGMENT_RECORD_CHECK.Rd                                                           |    5 
 dataquieR-2.8.9/dataquieR/man/SEGMENT_RECORD_COUNT.Rd                                                           |    5 
 dataquieR-2.8.9/dataquieR/man/SEGMENT_UNIQUE_ID.Rd                                                              |    5 
 dataquieR-2.8.9/dataquieR/man/SEGMENT_UNIQUE_ROWS.Rd                                                            |    5 
 dataquieR-2.8.9/dataquieR/man/SPLIT_CHAR.Rd                                                                     |    5 
 dataquieR-2.8.9/dataquieR/man/TOTRESPT.Rd                                                                       |    6 
 dataquieR-2.8.9/dataquieR/man/UNITS.Rd                                                                          |    2 
 dataquieR-2.8.9/dataquieR/man/UNIT_IS_COUNT.Rd                                                                  |    2 
 dataquieR-2.8.9/dataquieR/man/UNIT_PREFIXES.Rd                                                                  |    4 
 dataquieR-2.8.9/dataquieR/man/UNIT_PREFIX_FACTORS.Rd                                                            |    4 
 dataquieR-2.8.9/dataquieR/man/UNIT_SOURCES.Rd                                                                   |    2 
 dataquieR-2.8.9/dataquieR/man/VARATT_REQUIRE_LEVELS.Rd                                                          |    5 
 dataquieR-2.8.9/dataquieR/man/VARIABLE_LIST.Rd                                                                  |    8 
 dataquieR-2.8.9/dataquieR/man/VARIABLE_LIST_ORDER.Rd                                                            |    8 
 dataquieR-2.8.9/dataquieR/man/VARIABLE_ROLES.Rd                                                                 |    5 
 dataquieR-2.8.9/dataquieR/man/WELL_KNOWN_META_VARIABLE_NAMES.Rd                                                 |    7 
 dataquieR-2.8.9/dataquieR/man/acc_loess.Rd                                                                      |    5 
 dataquieR-2.8.9/dataquieR/man/acc_mahalanobis.Rd                                                                |   71 
 dataquieR-2.8.9/dataquieR/man/acc_mahalanobis_ratio.Rd                                                          |only
 dataquieR-2.8.9/dataquieR/man/acc_margins.Rd                                                                    |   13 
 dataquieR-2.8.9/dataquieR/man/acc_multivariate_outlier.Rd                                                       |    2 
 dataquieR-2.8.9/dataquieR/man/acc_shape_or_scale.Rd                                                             |    2 
 dataquieR-2.8.9/dataquieR/man/as.character.dataquieR_translated.Rd                                              |only
 dataquieR-2.8.9/dataquieR/man/com_qualified_item_missingness.Rd                                                 |    8 
 dataquieR-2.8.9/dataquieR/man/con_contradictions_redcap.Rd                                                      |    4 
 dataquieR-2.8.9/dataquieR/man/con_limit_deviations.Rd                                                           |    4 
 dataquieR-2.8.9/dataquieR/man/contradiction_functions.Rd                                                        |    4 
 dataquieR-2.8.9/dataquieR/man/contradiction_functions_descriptions.Rd                                           |    5 
 dataquieR-2.8.9/dataquieR/man/dataquieR.CONDITIONS_LEVEL_TRHESHOLD.Rd                                           |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.CONDITIONS_WITH_STACKTRACE.Rd                                           |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.ELEMENT_MISSMATCH_CHECKTYPE.Rd                                          |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.ERRORS_WITH_CALLER.Rd                                                   |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.GAM_for_LOESS.Rd                                                        |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.MAHALANOBIS_THRESHOLD.Rd                                                |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.MAX_LABEL_LEN.Rd                                                        |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.MAX_LONG_LABEL_LEN.Rd                                                   |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.MAX_VALUE_LABEL_LEN.Rd                                                  |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.MESSAGES_WITH_CALLER.Rd                                                 |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.MULTIVARIATE_OUTLIER_CHECK.Rd                                           |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.Rd                                                                      |   13 
 dataquieR-2.8.9/dataquieR/man/dataquieR.VALUE_LABELS_htmlescaped.Rd                                             |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.WARNINGS_WITH_CALLER.Rd                                                 |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.acc_loess.exclude_constant_subgroups.Rd                                 |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.acc_loess.mark_time_points.Rd                                           |    6 
 dataquieR-2.8.9/dataquieR/man/dataquieR.acc_loess.min_bw.Rd                                                     |    6 
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More information about dataquieR at CRAN
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New package combss with initial version 0.1.0
Package: combss
Title: Continuous Optimisation Towards Best Subset Selection
Version: 0.1.0
Author: Benoit Liquet [aut, cre] , Anant Mathur [aut], Sarat Moka [aut]
Maintainer: Benoit Liquet <benoit.liquet@univ-pau.fr>
Description: Best subset selection in generalised linear models via continuous optimisation. Reformulates the NP-hard discrete subset selection problem as a continuous optimisation over the hypercube [0,1]^p, solved via a Frank-Wolfe homotopy algorithm with closed-form ridge inner solves. Supports linear (Gaussian), binary logistic, and multinomial regression. For methodological details see Moka, Liquet, Zhu and Muller (2024) <doi:10.1007/s11222-024-10387-8> and Mathur, Liquet, Muller and Moka (2026) <doi:10.48550/arXiv.2603.21952>.
License: GPL-3
URL: https://github.com/benoit-liquet/combss
Encoding: UTF-8
Imports: glmnet (>= 4.0), stats
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-06 06:20:54 UTC; benoit
Repository: CRAN
Date/Publication: 2026-05-11 19:10:26 UTC

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New package cgvR with initial version 0.1.2
Package: cgvR
Title: Interactive 3D Visualization of Large Cayley Graphs via Vulkan
Version: 0.1.2
Description: Provides interactive 3D visualization for large-scale Cayley graphs. Specifically designed for analyzing state spaces of the 'TopSpin' puzzle. Leverages the 'Datoviz' library and Vulkan-based GPU rendering for smooth real-time exploration of large graphs and complex state transitions. Implements efficient coordinate mapping for high-dimensional permutation groups, allowing users to visualize the connectivity and structural properties of the puzzle's state space. The rendering engine provides high-performance visuals and interactive camera controls, making it suitable for mathematical analysis of group-theoretic puzzles within the R environment.
Depends: R (>= 4.1.0)
Imports: grDevices, stats
Suggests: cayleyR, knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
License: MIT + file LICENSE
Encoding: UTF-8
SystemRequirements: C17, C++17, GNU make, pkg-config, libvulkan-dev (Linux) or LunarG 'Vulkan' 'SDK' (Windows, macOS), libglfw3-dev (Linux) or glfw via Homebrew (macOS), ffmpeg (optional, for cgv_record_*). Optional configure flags: --with-vulkan / --without-vulkan, --with-simd for SSE4.1 + PCLMUL fpng.
URL: https://github.com/Zabis13/cgvR
BugReports: https://github.com/Zabis13/cgvR/issues
NeedsCompilation: yes
Packaged: 2026-05-06 09:08:39 UTC; yuri
Author: Yuri Baramykov [aut, cre] , Cyrille Rossant [ctb, cph]
Maintainer: Yuri Baramykov <lbsbmsu@mail.ru>
Repository: CRAN
Date/Publication: 2026-05-11 19:20:07 UTC

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New package warmthcompetence with initial version 0.1.5
Package: warmthcompetence
Title: Warmth and Competence Detectors
Version: 0.1.5
Description: Detects perceptions of warmth and competence in American English self-presentation language. Using trained elastic net regression models, this package provides a numerical representation of warmth and competence perceptions. Methods are described here:<https://github.com/bushraguenoun/warmthcompetence/tree/master/paper>.
License: AGPL (>= 3)
Encoding: UTF-8
URL: https://github.com/bushraguenoun/warmthcompetence, https://bushraguenoun.github.io/warmthcompetence/
BugReports: https://github.com/bushraguenoun/warmthcompetence/issues
Imports: spacyr, caret, dplyr (>= 1.2.0), lexicon, ngram, qdap, politeness, qdapDictionaries, quanteda (>= 4.0.2), sentimentr, stats, tidyr, tidytext, tm, quanteda.textstats
Depends: R (>= 4.1.0)
Suggests: rmarkdown, knitr
LazyData: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-05 18:39:49 UTC; bguenoun
Author: Bushra Guenoun [aut, cre] , Julian Zlatev [aut] , Noah Greifer [ctb]
Maintainer: Bushra Guenoun <bushraguenoun@gmail.com>
Repository: CRAN
Date/Publication: 2026-05-11 18:40:02 UTC

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New package TidyPanel with initial version 0.1.2
Package: TidyPanel
Title: Universal Messy Panel Data Cleaner
Version: 0.1.2
Description: A robust toolkit designed to standardize and clean complex tabular data from commercial enterprise systems, healthcare records, logistics software, and HR databases. Features include intelligent regex parsing for domain-specific noise (currencies, percentages), gap-based block clustering, and automated messy table resolution. Methods draw on tidy data principles described in Wickham (2014) <doi:10.18637/jss.v059.i10> and the 'readxl' parsing infrastructure described in Wickham & Bryan (2023) <https://readxl.tidyverse.org>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, stringr, readxl
Suggests: knitr, rmarkdown, testthat, writexl
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-06 03:35:31 UTC; TonyL
Author: Tony Lu [aut, cre]
Maintainer: Tony Lu <xulunt123@gmail.com>
Repository: CRAN
Date/Publication: 2026-05-11 19:00:09 UTC

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New package ROCsurvcomp with initial version 0.1.2
Package: ROCsurvcomp
Title: ROC-Based Methods for Comparing Survival Distributions with Right, Left, and Doubly Censored Data
Version: 0.1.2
Date: 2026-05-05
Maintainer: Mohammod Mahmudur Rahman <mahmudur.asif13@gmail.com>
Description: Implements nonparametric and semiparametric methods for comparing two survival distributions under non-proportional hazards (non-PH). The methods are based on the Receiver Operating Characteristic (ROC) curve length (Bantis et al. (2021) <doi:10.1002/sim.8869>) and the overlap coefficient (OVL) (Franco-Pereira et al. (2021) <doi:10.1177/09622802211046386>), as well as a joint ROC length-OVL-based approach. These methods do not require prior knowledge of the underlying non-PH pattern and can accommodate right, left, and doubly censored data.
License: GPL-3
Encoding: UTF-8
Imports: survival, interval, stats, utils, ggplot2
Suggests: knitr, rmarkdown, PWEXP, testthat (>= 3.0.0)
Depends: R (>= 3.5)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-05 20:42:34 UTC; mahmudur_asif
Author: Mohammod Mahmudur Rahman [aut, cre], Leonidas Bantis [aut]
Repository: CRAN
Date/Publication: 2026-05-11 18:50:31 UTC

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New package rankPlayedInference with initial version 1.0
Package: rankPlayedInference
Version: 1.0
Date: 2026-05-05
Title: Conditional Probability Distributions in Hearts Card Game
Maintainer: Barry Zeeberg <barryz2013@gmail.com>
Depends: R (>= 4.2.0)
Imports: grDevices, graphics
Description: For a given suit, if you add up the number of cards that you hold, plus the number that has been played so far, you can easily determine the number that remains in the combined hands of your three opponents. You can also determine the ("special") card of highest rank of the remaining cards for that suit. At some point, you notice that a certain opponent discards that special card. What can you infer about his holding in that suit? A series of simulation studies are reported here that allows a quantitative inference based on the conditional probability, given that the opponent has the special card. The same procedure is also used for the conditional probability, given that the opponent does not have the special card.
License: GPL (>= 2)
Encoding: UTF-8
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2026-05-05 21:11:44 UTC; barryzeeberg
Author: Barry Zeeberg [aut, cre]
Repository: CRAN
Date/Publication: 2026-05-11 18:50:02 UTC

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New package randomForestSGT with initial version 1.0.0
Package: randomForestSGT
Version: 1.0.0
Date: 2026-05-05
Title: Random Forest Super Greedy Trees
Author: Min Lu [aut], Udaya B. Kogalur [aut, cre], Hemant Ishwaran [aut]
Maintainer: Udaya B. Kogalur <ubk@kogalur.com>
BugReports: https://github.com/kogalur/randomForestSGT/issues/
Depends: R (>= 4.3.0)
Imports: randomForestSRC (>= 3.6.2), varPro (>= 3.1.0)
Suggests: mlbench, interp, glmnet
Description: Implements random forest Super Greedy Trees (SGTs) for regression. SGTs extend classification and regression tree splitting by fitting lasso-penalized local parametric models at tree nodes, producing sparse univariate and multivariate geometric cuts such as axis-aligned splits, hyperplanes, ellipsoids, hyperboloids, and interaction-based cuts. Trees are grown best-split-first by selecting cuts that reduce empirical risk, and ensembles provide out-of-bag error estimation, prediction on new data, variable filtering, tuning of the hcut complexity parameter, coordinate-descent lasso fitting, variable importance, and local coefficient summaries. For the underlying method, see Ishwaran (2026) <doi:10.1007/s10462-026-11541-6>.
License: GPL (>= 3)
URL: https://ishwaran.org/
NeedsCompilation: yes
Packaged: 2026-05-05 21:52:24 UTC; kogalur
Repository: CRAN
Date/Publication: 2026-05-11 18:50:07 UTC

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New package mectx with initial version 1.1.1
Package: mectx
Title: MEchanistic Clustering - Treatment eXposure Framework
Version: 1.1.1
Description: Implements the MEC-TX (MEchanistic Clustering - Treatment eXposure) framework for encoding, clustering, and survival analysis of real-world oncology treatment timelines. Provides functions for normalising medication records, computing treatment intervals, performing k-means clustering in PCA space, assigning line-of-therapy labels, and comparing survival outcomes across treatment groups. Designed for use with registry-based cohorts such as the ORIEN AVATAR dataset. Methods follow the digital-twin framework described in Dhrubo and Spakowicz (2026) <https://github.com/spakowiczlab/mec-tx>. treatment timelines using the MEC-TX digital-twin framework.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
Depends: R (>= 4.4.0)
Imports: magrittr, broom, dplyr, forcats, ggnewscale, ggplot2, patchwork, purrr, stats, stringr, survival, tibble, tidyr, umap
Suggests: testthat (>= 3.0.0), devtools, roxygen2
Date: 2026-04-27
NeedsCompilation: no
Packaged: 2026-05-05 19:59:19 UTC; dipankor99
Author: Dipankor Dhrubo [aut, cre], Daniel Spakowicz [aut]
Maintainer: Dipankor Dhrubo <dhrubo.2@osu.edu>
Repository: CRAN
Date/Publication: 2026-05-11 18:40:08 UTC

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Package manureshed updated to version 0.1.5 with previous version 0.1.4 dated 2026-03-09

Title: Spatiotemporal Nutrient Balance Analysis Across Agricultural and Municipal Systems
Description: A comprehensive framework for analyzing agricultural nutrient balances across multiple spatial scales (county, 'HUC8', 'HUC2') with integration of wastewater treatment plant ('WWTP') effluent loads for both nitrogen and phosphorus. Supports classification of spatial units as nutrient sources, sinks, or balanced areas based on agricultural surplus and deficit calculations. Includes visualization tools, spatial transition probability analysis, and nutrient flow network mapping. Built-in datasets include agricultural nutrient balance data from the Nutrient Use Geographic Information System ('NuGIS'; The Fertilizer Institute and Plant Nutrition Canada, 1987-2016) <https://nugis.tfi.org/tabular_data/> and U.S. Environmental Protection Agency ('EPA') wastewater discharge data from the 'ECHO' Discharge Monitoring Report ('DMR') Loading Tool (2007-2016) <https://echo.epa.gov/trends/loading-tool/water-pollution-search>. Data are downloaded on demand from the Open Science Framework ( [...truncated...]
Author: Olatunde D. Akanbi [aut, cre, cph] , Vibha Mandayam [aut] , Atharva Gupta [aut] , K. Colton Flynn [aut] , Jeffrey Yarus [aut] , Erika I. Barcelos [aut, cph] , Roger H. French [aut, cph]
Maintainer: Olatunde D. Akanbi <olatunde.akanbi@case.edu>

Diff between manureshed versions 0.1.4 dated 2026-03-09 and 0.1.5 dated 2026-05-11

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New package imv with initial version 0.3
Package: imv
Title: Model Comparison via the 'InterModel Vigorish' ('IMV')
Version: 0.3
Description: Computes the 'InterModel Vigorish' ('IMV'), a metric for comparing the predictive accuracy of two models for binary outcomes. The 'IMV' is derived from the expected value of a bettor using one model's predicted probabilities against those of a competing model, and is estimated via k-fold cross-validation. Methods are provided for generalized linear models, mixed-effects models ('lme4'), and item response theory models ('mirt'). See <doi:10.1371/journal.pone.0316491>.
Depends: R (>= 3.5.0)
Suggests: lme4, mirt, testthat (>= 3.0.0)
License: MIT + file LICENSE
Language: en-US
NeedsCompilation: no
Packaged: 2026-05-05 20:49:05 UTC; ben
Author: Ben Domingue [aut, cre], Christian Jackson [ctb]
Maintainer: Ben Domingue <ben.domingue@gmail.com>
Repository: CRAN
Date/Publication: 2026-05-11 18:40:15 UTC

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Package hockeystick updated to version 0.8.7 with previous version 0.8.6 dated 2025-08-19

Title: Download and Visualize Essential Climate Change Data
Description: Provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined 'ggplot2' charts. Datasets include atmospheric CO2, methane, emissions, instrumental and proxy temperature records, sea levels, Arctic/Antarctic sea-ice, Hurricanes, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory <https://gml.noaa.gov/ccgg/trends/data.html>, Global Carbon Project <https://www.globalcarbonproject.org/carbonbudget/>, NASA GISTEMP <https://data.giss.nasa.gov/gistemp/>, National Snow and Sea Ice Data Center <https://nsidc.org/home>, CSIRO <https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/>, NOAA Laboratory for Satellite Altimetry <https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/> and HURDAT Atlantic Hurricane Database <https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html>, Vostok Paleo carbon dio [...truncated...]
Author: Hernando Cortina [aut, cre]
Maintainer: Hernando Cortina <hch@alum.mit.edu>

Diff between hockeystick versions 0.8.6 dated 2025-08-19 and 0.8.7 dated 2026-05-11

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Package DyadRatios updated to version 2.1 with previous version 2.0 dated 2026-05-08

Title: Dyad Ratios Algorithm for Latent Variable Estimation
Description: Implements the Dyad Ratios algorithm for estimating latent variables from time-series survey data. The algorithm estimates a latent mood dimension (or two dimensions) from a set of issue opinion series. Supports annual, quarterly, monthly, and daily aggregation intervals, optional exponential smoothing, and up to two latent dimensions. Input data can be provided as a data frame or read from delimited text files. Based on Stimson's 'MCalc' C++ program. See Stimson (2018) <doi:10.1177/0759106318761614> for more details.
Author: James Stimson [aut] , Dave Armstrong [cre, aut]
Maintainer: Dave Armstrong <davearmstrong.ps@gmail.com>

Diff between DyadRatios versions 2.0 dated 2026-05-08 and 2.1 dated 2026-05-11

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New package curves with initial version 0.4.0
Package: curves
Title: Model-Agnostic Response Curves for Fitted Models
Version: 0.4.0
Description: Create model-agnostic response-curve diagnostics for fitted prediction models. Supports profile curves, partial dependence, individual conditional expectation, and accumulated local effects; univariate curves, bivariate surfaces, ensemble summaries across multiple models, ALE-based interaction ranking, and optional raster-linked exploration with 'terra' and 'shiny'. Static displays are returned as 'ggplot2' plots. For more details on the methods see Molnar (2025) <https://christophm.github.io/interpretable-ml-book/>.
URL: https://github.com/rvalavi/curves
BugReports: https://github.com/rvalavi/curves/issues
Maintainer: Roozbeh Valavi <valavi.r@gmail.com>
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: cowplot, ggplot2 (>= 3.3.6)
Suggests: disdat, knitr, mgcv, plotly, randomForest, rmarkdown, shiny, terra (>= 1.6-41), testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-06 03:16:32 UTC; val085
Author: Roozbeh Valavi [aut, cre]
Repository: CRAN
Date/Publication: 2026-05-11 19:00:02 UTC

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New package contentValidity with initial version 0.1.0
Package: contentValidity
Title: Content Validity Indices for Instrument Development
Version: 0.1.0
Description: Computes content validity indices commonly used in instrument development and questionnaire validation, including the Item-level Content Validity Index (I-CVI), Scale-level Content Validity Index (S-CVI), modified kappa adjusted for chance agreement, Aiken's V, and Lawshe's Content Validity Ratio (CVR). Methods follow Lynn (1986) <doi:10.1097/00006199-198611000-00017>, Polit and Beck (2006) <doi:10.1002/nur.20147>, Aiken (1985) <doi:10.1177/0013164485451012>, and Lawshe (1975) <doi:10.1111/j.1744-6570.1975.tb01393.x>.
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://github.com/Rafhq1403/contentValidity
BugReports: https://github.com/Rafhq1403/contentValidity/issues
Depends: R (>= 3.5)
LazyData: true
NeedsCompilation: no
Packaged: 2026-05-05 20:54:51 UTC; rashedalqahtani
Author: Rashed Alqahtani [aut, cre]
Maintainer: Rashed Alqahtani <rashed.alqahtani@gmail.com>
Repository: CRAN
Date/Publication: 2026-05-11 18:40:20 UTC

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New package ArvindSt with initial version 1.0.0
Package: ArvindSt
Title: Five Novel Stochastic Regression Models with Arvind-Distributed Errors and Effects
Version: 1.0.0
Description: Implements the 'Arvind' distribution and five novel stochastic regression models that replace the traditional Gaussian error assumption with 'Arvind'-distributed errors. The 'Arvind' distribution is a flexible single-parameter continuous distribution on the positive real line characterised by a polynomial numerator with Gaussian-type decay. The package provides complete distribution functions (darvind(), parvind(), qarvind(), rarvind()), maximum likelihood estimation via fit_arvind_mle(), and five model-fitting routines: Random Walk on Coefficients via fit_rw1(), Time-Varying Coefficient Linear Model via fit_tvlm(), Simulation-Extrapolation via fit_simex(), Mixed-Effects Regression via fit_mixed(), and Regime-Switching Hidden Markov Model via fit_hmm(). Additionally provides Monte Carlo forecasting with prediction intervals via forecast_arvind(), comprehensive goodness-of-fit diagnostics (21 metrics and 25 plots) via diagnostics_arvind() and plot_arvind(), k-fold and rolling-window cro [...truncated...]
License: MIT + file LICENSE
Depends: R (>= 4.0.0)
Imports: stats, graphics, grDevices, utils, ggplot2, forecast, tvReg, lme4, depmixS4, reshape2, rlang
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
Encoding: UTF-8
Language: en-US
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-05 17:16:50 UTC; 30017827
Author: Shikhar Tyagi [aut, cre] , Arvind Pandey [aut]
Maintainer: Shikhar Tyagi <shikhar1093tyagi@gmail.com>
Repository: CRAN
Date/Publication: 2026-05-11 18:20:02 UTC

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Package arete updated to version 0.2 with previous version 0.1 dated 2025-10-20

Title: Automated REtrieval from TExt
Description: A Python based pipeline for extraction of species occurrence data through the usage of large language models. Includes validation tools designed to handle model hallucinations for a scientific, rigorous use of LLM. Currently supports usage of GPT with more planned, including local and non-proprietary models. For more details on the methodology used please consult the references listed under each function, such as Kent, A. et al. (1995) <doi:10.1002/asi.5090060209>, van Rijsbergen, C.J. (1979, ISBN:978-0408709293, Levenshtein, V.I. (1966) <https://nymity.ch/sybilhunting/pdf/Levenshtein1966a.pdf> and Klaus Krippendorff (2011) <https://repository.upenn.edu/handle/20.500.14332/2089>.
Author: Vasco V. Branco [cre, aut] , Vaughn Shirey [ctb] , Thomas Merrien [ctb] , Pedro Cardoso [aut]
Maintainer: Vasco V. Branco <vasco.branco@helsinki.fi>

Diff between arete versions 0.1 dated 2025-10-20 and 0.2 dated 2026-05-11

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Package TwoStepSDFM updated to version 0.2.1 with previous version 0.2.0 dated 2026-04-21

Title: Estimate a Sparse Mixed Frequency Gaussian Factor Model Using a Two-Step Procedure
Description: Estimate a sparse Gaussian state-space model with mixed frequency data via sparse principal components analysis and the Kalman filter and smoother. For more details see Franjic and Schweikert (2024) <doi:10.2139/ssrn.4733872>.
Author: Domenic Franjic [aut, cre]
Maintainer: Domenic Franjic <franjic@uni-hohenheim.de>

Diff between TwoStepSDFM versions 0.2.0 dated 2026-04-21 and 0.2.1 dated 2026-05-11

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Permanent link

Package idarps updated to version 0.0.6 with previous version 0.0.5 dated 2025-05-02

Title: Datasets and Functions for the Class "Modelling and Data Analysis for Pharmaceutical Sciences"
Description: Provides datasets and functions for the class "Modelling and Data Analysis for Pharmaceutical Sciences". The datasets can be used to present various methods of data analysis and statistical modeling. Functions for data visualization are also implemented.
Author: Lionel Voirol [aut, cre], Stephane Guerrier [aut], Yuming Zhang [aut], Luca Insolia [aut]
Maintainer: Lionel Voirol <lionelvoirol@hotmail.com>

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Permanent link

Package ao updated to version 1.2.3 with previous version 1.2.2 dated 2025-12-15

Title: Alternating Optimization
Description: Implementation of an iterative process that optimizes a function by alternately performing restricted optimization over parameter subsets. Instead of solving one joint optimization problem, alternating optimization breaks it into smaller sub-problems. This approach can make optimization feasible when joint optimization is too difficult.
Author: Lennart Oelschlaeger [aut, cre] , Siddhartha Chib [ctb]
Maintainer: Lennart Oelschlaeger <oelschlaeger.lennart@gmail.com>

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Package XYomics updated to version 0.1.4 with previous version 0.1.3 dated 2026-01-08

Title: Analysis of Sex Differences in Omics Data for Complex Diseases
Description: Tools to analyze sex differences in omics data for complex diseases. It includes functions for differential expression analysis using the 'limma' method <doi:10.1093/nar/gkv007>, interaction testing between sex and disease, pathway enrichment with 'clusterProfiler' <doi:10.1089/omi.2011.0118>, and gene regulatory network (GRN) construction and analysis using 'igraph'. The package enables a reproducible workflow from raw data processing to biological interpretation.
Author: Enrico Glaab [aut, cre], Sophie Le Bars [aut], Mohamed Soudy [aut], Murodzhon Akhmedov [cph]
Maintainer: Enrico Glaab <enrico.glaab@uni.lu>

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More information about XYomics at CRAN
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Package wrds updated to version 0.1.1 with previous version 0.0.1 dated 2026-01-19

Title: Access 'Wharton Research Data Services' ('WRDS')
Description: Provides simple functions for accessing data from 'Wharton Research Data Services' ('WRDS'), a widely used financial database in academic research. Includes credential management via the system keyring, database tools, and functions for downloading generic tables, 'Compustat' fundamentals, and linking tables.
Author: Ulrich Atz [aut, cre, cph]
Maintainer: Ulrich Atz <ulrich.atz@unibocconi.it>

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Package pharmr updated to version 2.1.0 with previous version 1.7.2 dated 2025-05-22

Title: Interface to the 'Pharmpy' 'Pharmacometrics' Library
Description: Interface to the 'Pharmpy' 'pharmacometrics' library. The 'Reticulate' package is used to interface Python from R.
Author: Rikard Nordgren [aut, cre, cph], Stella Belin [aut, cph], Mats O. Karlsson [sad], Andrew C. Hooker [sad], Xiaomei Chen [sad], Sebastian Ueckert [sad] , Simon Buatois [rev], Joao A. Abrantes [rev], Emilie Schindler [rev], F. Hoffmann-La Roche Ltd. [fn [...truncated...]
Maintainer: Rikard Nordgren <rikard.nordgren@uu.se>

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Package iTensor updated to version 1.0.5 with previous version 1.0.4 dated 2026-05-08

Title: ICA-Based Matrix/Tensor Decomposition
Description: Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented. ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively. For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.
Author: Koki Tsuyuzaki [aut, cre]
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>

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Package feature updated to version 1.2.16 with previous version 1.2.15 dated 2021-02-10

Title: Local Inferential Feature Significance for Multivariate Kernel Density Estimation
Description: Local inferential feature significance for multivariate kernel density estimation.
Author: Tarn Duong [aut, cre] , Matt Wand [ctb]
Maintainer: Tarn Duong <tarn.duong@gmail.com>

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Package MLwrap updated to version 0.4.0 with previous version 0.3.0 dated 2025-12-15

Title: Machine Learning Modelling for Everyone
Description: A minimal library specifically designed to make the estimation of Machine Learning (ML) techniques as easy and accessible as possible, particularly within the framework of the Knowledge Discovery in Databases (KDD) process in data mining. The package provides essential tools to structure and execute each stage of a predictive or classification modeling workflow, aligning closely with the fundamental steps of the KDD methodology, from data selection and preparation, through model building and tuning, to the interpretation and evaluation of results using Sensitivity Analysis. The 'MLwrap' workflow is organized into four core steps; preprocessing(), build_model(), fine_tuning(), and sensitivity_analysis(). It also includes global and pairwise interaction analysis based on Friedman’s H-statistic to support a more detailed interpretation of complex feature relationships.These steps correspond, respectively, to data preparation and transformation, model construction, hyperparameter optimizat [...truncated...]
Author: Javier Martinez Garcia [aut] , Juan Jose Montano Moreno [ctb] , Albert Sese [cre, ctb]
Maintainer: Albert Sese <albert.sese@uib.es>

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Package Infusion updated to version 2.3.12 with previous version 2.3.0 dated 2025-07-22

Title: Inference Using Simulation
Description: Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated, as described in Rousset et al. (2026) <doi:10.24072/pcjournal.721>.
Author: Francois Rousset [aut, cre, cph]
Maintainer: Francois Rousset <francois.rousset@umontpellier.fr>

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Package HCUPtools updated to version 1.0.1 with previous version 1.0.0 dated 2025-12-10

Title: Access and Work with HCUP Resources and Datasets
Description: A comprehensive R package for accessing and working with publicly available and free resources from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). The package provides streamlined access to HCUP's Clinical Classifications Software Refined (CCSR) mapping files and Summary Trend Tables, enabling researchers and analysts to efficiently map ICD-10-CM diagnosis codes and ICD-10-PCS procedure codes to CCSR categories and access HCUP statistical reports. Key features include: direct download from HCUP website, multiple output formats (long/wide/default), cross-classification support, version management, citation generation, and intelligent caching. The package does not redistribute HCUP data files but facilitates direct download from the official HCUP website, ensuring users always have access to the latest versions and maintain compliance with HCUP data use policies. This package only accesses free public tools and reports; it does NOT a [...truncated...]
Author: Vikrant Dev Rathore [aut, cre]
Maintainer: Vikrant Dev Rathore <rathore.vikrant@gmail.com>

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Package persistence updated to version 1.0.0 with previous version 0.2.0 dated 2025-10-06

Title: Optimal Graph Partition using the Persistence
Description: Calculate the optimal vertex partition of a graph using the persistence as objective function. These subroutines have been used in Avellone et al. <doi:10.1007/s10288-023-00559-z>.
Author: Alessandro Avellone [aut, cre], Paolo Bartesaghi [aut], Stefano Benati [aut], Rosanna Grassi [aut]
Maintainer: Alessandro Avellone <alessandro.avellone@unimib.it>

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Package baymedr updated to version 0.2 with previous version 0.1.1 dated 2021-03-27

Title: Computation of Bayes Factors for Common Biomedical Designs
Description: BAYesian inference for MEDical designs in R. Functions for the computation of Bayes factors for common biomedical research designs. Implemented are functions to test the equivalence (equiv_bf), non-inferiority (infer_bf), and superiority (super_bf) of an experimental group compared to a control group on a continuous outcome measure, as well as functions for simulating survival data and calculating a Bayes factor for Cox proportional hazards models. Bayes factors for these tests can be computed based on raw data or summary statistics.
Author: Maximilian Linde [aut, cre] , Don van Ravenzwaaij [aut] , Jorge N. Tendeiro [aut] , Quentin F. Gronau [ctb]
Maintainer: Maximilian Linde <maximilian.linde.92@gmail.com>

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Package simmr updated to version 0.5.2 with previous version 0.5.1.217 dated 2024-10-16

Title: A Stable Isotope Mixing Model
Description: Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load() and simmr_mcmc(). The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 <doi:10.1371/journal.pone.0009672>, and Parnell et al 2013 <doi:10.1002/env.2221>.
Author: Emma Govan [aut], Andrew Parnell [cre, aut]
Maintainer: Andrew Parnell <andrew.parnell1@ucd.ie>

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Package ibr updated to version 2.4-1 with previous version 2.4-0 dated 2026-05-03

Title: Iterative Bias Reduction
Description: Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines.
Author: Pierre-Andre Cornillon [aut, cre], Nicolas Hengartner [aut], Eric Matzner-Lober [aut]
Maintainer: Pierre-Andre Cornillon <pierre-andre.cornillon@univ-rennes2.fr>

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Package ggplate updated to version 0.3.1 with previous version 0.3.0 dated 2026-05-08

Title: Create Layout Plots of Biological Culture Plates and Microplates
Description: Enables users to create simple plots of biological culture plates as well as microplates. Both continuous and discrete values can be plotted onto the plate layout.
Author: Jan-Philipp Quast [aut, cre]
Maintainer: Jan-Philipp Quast <jpquast.software@gmail.com>

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Package amp.sim updated to version 0.1.1 with previous version 0.1.0 dated 2026-05-07

Title: Flexible Simulation Utilities for Pharmacometric Modeling
Description: The goal of 'amp.sim' is to transform 'NONMEM' models into R syntax so they can be used for simulations using the 'deSolve', 'nlmixr2' or 'mrgsolve' package. Additionally, functionality is included to aid simulations performed directly in 'NONMEM' and to automatically create shiny apps for simulation models.
Author: Richard Hooijmaijers [aut, cre, cph], LAPP Consultants [fnd, cph]
Maintainer: Richard Hooijmaijers <richardhooijmaijers@gmail.com>

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Package tmvmixnorm updated to version 1.2.0 with previous version 1.1.1 dated 2020-09-18

Title: Sampling from Truncated Multivariate Normal and t Distributions
Description: Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong. Algorithms proposed by Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multiv [...truncated...]
Author: Ting Fung Ma [cre, aut], Sujit K. Ghosh [aut], Yifang Li [aut]
Maintainer: Ting Fung Ma <tingfung@mailbox.sc.edu>

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Package SCE updated to version 1.1.4 with previous version 1.1.3 dated 2026-05-08

Title: Stepwise Clustered Ensemble
Description: Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster Analysis (SCA) for multivariate data analysis. The package provides comprehensive tools for feature selection, model training, prediction, and evaluation in hydrological and environmental modeling applications. Key functionalities include recursive feature elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag (OOB) validation, and ensemble prediction capabilities. The package supports both single and multivariate response variables, making it suitable for complex environmental modeling scenarios. For more details see Li et al. (2021) <doi:10.5194/hess-25-4947-2021>.
Author: Kailong Li [aut, cre]
Maintainer: Kailong Li <lkl98509509@gmail.com>

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New package rvtk with initial version 0.1.3
Package: rvtk
Title: Bindings for the Visualization Toolkit ('VTK')
Version: 0.1.3
Description: Provides pre-compiled static 'VTK' libraries and headers so that downstream R packages can link against the Visualization Toolkit without requiring users to install 'VTK' manually. On all platforms the package first honours a user-supplied 'VTK_DIR' environment variable. On macOS it then tries 'Homebrew', followed by 'pkg-config'. On Linux it tries 'pkg-config' and well-known system prefixes ('/usr', '/usr/local'). If no suitable system installation is found on macOS or Linux, pre-built static libraries are downloaded automatically from the package's GitHub releases. On Windows the package tries 'VTK_DIR', then 'Rtools45' 'pacman', then common 'MSYS2' prefixes, accepting both static ('.a') and shared ('.dll.a' import libs + DLLs) installations. When shared libraries are used, the VTK DLLs are staged in 'inst/vtk-dlls/' and an '.onLoad' hook prepends that directory to PATH via 'Sys.setenv()' when the package is loaded, and restored in '.onUnload()'. The pre-built fallback downloads stat [...truncated...]
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/astamm/rvtk
BugReports: https://github.com/astamm/rvtk/issues
NeedsCompilation: no
Suggests: tinytest
Packaged: 2026-05-03 20:44:21 UTC; stamm-a
Author: Aymeric Stamm [aut, cre]
Maintainer: Aymeric Stamm <aymeric.stamm@cnrs.fr>
Repository: CRAN
Date/Publication: 2026-05-11 07:30:02 UTC

More information about rvtk at CRAN
Permanent link

Package mLLMCelltype updated to version 2.0.5 with previous version 2.0.0 dated 2026-02-08

Title: Cell Type Annotation Using Large Language Models
Description: Automated cell type annotation for single-cell RNA sequencing data using consensus predictions from multiple large language models. Integrates with Seurat objects and provides uncertainty quantification for annotations. Supports various LLM providers including OpenAI, Anthropic, and Google. For details see Yang et al. (2025) <doi:10.1101/2025.04.10.647852>.
Author: Chen Yang [aut, cre, cph]
Maintainer: Chen Yang <cafferychen777@tamu.edu>

Diff between mLLMCelltype versions 2.0.0 dated 2026-02-08 and 2.0.5 dated 2026-05-11

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Package MFRCD updated to version 0.1.1 with previous version 0.1.0 dated 2026-05-06

Title: Optimal Row-Column Designs for Asymmetrical Factorial Experiments
Description: Constructs and analyzes optimal row-column designs for mixed-level factorial experiments under square and rectangular field layouts. For square field layouts, the package implements direct common-factor constructions by first forming two component treatment arrays, one for each factor or super-factor, and then combining them through a symbolic cell-wise product following Gopinath, Parsad and Mandal (2018) <doi:10.1080/03610926.2017.1376091>. For rectangular field layouts, the package constructs designs by extracting a balanced principal block from a mixed-level block design, treating it as the principal column, taking the complete treatment set as the principal row, and generating the full row-column design by cyclic modular development. The package also includes repair utilities for improving disconnected or partially connected row-column designs through bounded treatment-swap searches while preserving the row-column layout structure. The package provides diagnostic tools for co [...truncated...]
Author: Archana A [aut], Sukanta Dash [aut, cre]
Maintainer: Sukanta Dash <sukanta.iasri@gmail.com>

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Package wevid updated to version 0.7.0 with previous version 0.6.2 dated 2019-09-12

Title: Weight of Evidence for Quantifying Performance of a Binary Classifier
Description: The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test. This package can be used with any test dataset on which you have computed prior probabilities of case status, posterior probabilities of case status, and you have the observed case-control status. In comparison with the C-statistic (area under ROC curve), the expected weight of evidence (expected information for discrimination) has several advantages as a summary measure of predictive performance. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.
Author: Paul McKeigue [aut, cre], Marco Colombo [ctb]
Maintainer: Paul McKeigue <paul.mckeigue@ed.ac.uk>

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Permanent link

Package vDiveR readmission to version 2.2.0 with previous version 2.1.0 dated 2025-09-19

Title: Visualization of Viral Protein Sequence Diversity Dynamics
Description: To ease the visualization of outputs from Diversity Motif Analyser ('DiMA'; <https://github.com/BVU-BILSAB/DiMA>). 'vDiveR' allows visualization of the diversity motifs (index and its variants – major, minor and unique) for elucidation of the underlying inherent dynamics. Please refer <https://vdiver-manual.readthedocs.io/en/latest/> for more information.
Author: Pendy Tok [aut, cre], Li Chuin Chong [aut], Evgenia Chikina [aut], Yin Cheng Chen [aut], Mohammad Asif Khan [aut]
Maintainer: Pendy Tok <pendytok0518@gmail.com>

This is a re-admission after prior archival of version 2.1.0 dated 2025-09-19

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Package S4DM updated to version 0.0.2 with previous version 0.0.1 dated 2025-01-10

Title: Small Sample Size Species Distribution Modeling
Description: Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Maitner et al. (2026) <doi:10.1002/ecog.08112>, Drake and Richards (2018) <doi:10.1002/ecs2.2373>, Drake (2015) <doi:10.1098/rsif.2015.0086>, and Drake (2014) <doi:10.1890/ES13-00202.1>.
Author: Brian S. Maitner [aut, cre] , Robert L. Richards [aut], Ben S. Carlson [aut], John M. Drake [aut], Cory Merow [aut]
Maintainer: Brian S. Maitner <bmaitner@usf.edu>

Diff between S4DM versions 0.0.1 dated 2025-01-10 and 0.0.2 dated 2026-05-11

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Package IBclust updated to version 1.3 with previous version 1.2.1 dated 2025-09-19

Title: Information Bottleneck Methods for Clustering Mixed-Type Data
Description: Implements multiple variants of the Information Bottleneck ('IB') method for clustering datasets containing continuous, categorical (nominal/ordinal) and mixed-type variables. The package provides deterministic, agglomerative, generalized, and standard 'IB' clustering algorithms that preserve relevant information while forming interpretable clusters. The Deterministic Information Bottleneck is described in Costa et al. (2026) <doi:10.1016/j.patcog.2026.113580>. The standard 'IB' method originates from Tishby et al. (2000) <doi:10.48550/arXiv.physics/0004057>, the agglomerative variant from Slonim and Tishby (1999) <https://papers.nips.cc/paper/1651-agglomerative-information-bottleneck>, and the generalized 'IB' from Strouse and Schwab (2017) <doi:10.1162/NECO_a_00961>.
Author: Angelos Markos [aut, cre], Efthymios Costa [aut], Ioanna Papatsouma [aut]
Maintainer: Angelos Markos <amarkos@gmail.com>

Diff between IBclust versions 1.2.1 dated 2025-09-19 and 1.3 dated 2026-05-11

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Package GPCMlasso updated to version 0.1-9 with previous version 0.1-8 dated 2025-07-23

Title: Differential Item Functioning in Generalized Partial Credit Models
Description: Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) <doi:10.3758/s13428-019-01224-2>. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
Author: Gunther Schauberger [aut, cre]
Maintainer: Gunther Schauberger <gunther.schauberger@tum.de>

Diff between GPCMlasso versions 0.1-8 dated 2025-07-23 and 0.1-9 dated 2026-05-11

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Package coursekata (with last version 0.19.2) was removed from CRAN

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

2026-03-10 0.19.2
2025-07-16 0.19.0
2024-12-12 0.18.1
2024-08-16 0.18.0
2024-05-26 0.17.0

Permanent link
Package flightplot (with last version 0.1.0) was removed from CRAN

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

2020-06-29 0.1.0

Permanent link
Package polite updated to version 0.1.4 with previous version 0.1.3 dated 2023-06-30

Title: Be Nice on the Web
Description: Be responsible when scraping data from websites by following polite principles: introduce yourself, ask for permission, take slowly and never ask twice.
Author: Dmytro Perepolkin [aut, cre]
Maintainer: Dmytro Perepolkin <dperepolkin@gmail.com>

Diff between polite versions 0.1.3 dated 2023-06-30 and 0.1.4 dated 2026-05-11

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Package highlightr updated to version 2.0.1 with previous version 2.0.0 dated 2026-04-10

Title: Highlight Conserved Edits Across Versions of a Document
Description: Input multiple versions of a source document, and receive HTML code for a highlighted version of the source document indicating the frequency of occurrence of phrases in the different versions. This method is described in Chapter 3 of Rogers (2024) <https://digitalcommons.unl.edu/dissertations/AAI31240449/>.
Author: Center for Statistics and Applications in Forensic Evidence [aut, cph, fnd], Rachel Rogers [aut, cre] , Susan VanderPlas [aut]
Maintainer: Rachel Rogers <rrogers.rpackages@gmail.com>

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Package algaeClassify updated to version 2.0.6 with previous version 2.0.5 dated 2025-12-01

Title: Tools to Query the 'Algaebase' Online Database, Standardize Phytoplankton Taxonomic Data, and Perform Functional Group Classifications
Description: Functions that facilitate the use of accepted taxonomic nomenclature, collection of functional trait data, and assignment of functional group classifications to phytoplankton species. Possible classifications include Morpho-functional group (MFG; Salmaso et al. 2015 <doi:10.1111/fwb.12520>) and CSR (Reynolds 1988; Functional morphology and the adaptive strategies of phytoplankton. In C.D. Sandgren (ed). Growth and reproductive strategies of freshwater phytoplankton, 388-433. Cambridge University Press, New York). Versions 2.0.0 and later includes new functions for querying the 'algaebase' online taxonomic database (www.algaebase.org), however these functions require a valid API key that must be acquired from the 'algaebase' administrators. Note that none of the 'algaeClassify' authors are affiliated with 'algaebase' in any way. Taxonomic names can also be checked against a variety of taxonomic databases using the 'Global Names Resolver' service via its API (<https://resolver.g [...truncated...]
Author: Vijay Patil [aut, cre], Torsten Seltmann [aut], Nico Salmaso [aut], Orlane Anneville [aut], Marc Lajeunesse [aut], Dietmar Straile [aut]
Maintainer: Vijay Patil <vij.patil@gmail.com>

Diff between algaeClassify versions 2.0.5 dated 2025-12-01 and 2.0.6 dated 2026-05-11

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Package agricolaeplotr updated to version 1.0.0 with previous version 0.6.1 dated 2025-01-30

Title: Visualization of Design of Experiments from the 'agricolae' Package
Description: Visualization of Design of Experiments from the 'agricolae' package with 'ggplot2' framework The user provides an experiment design from the 'agricolae' package, calls the corresponding function and will receive a visualization with 'ggplot2' based functions that are specific for each design. As there are many different designs, each design is tested on its type. The output can be modified with standard 'ggplot2' commands or with other packages with 'ggplot2' function extensions.
Author: Jens Harbers [aut, cre]
Maintainer: Jens Harbers <jensharbers@gmail.com>

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