Thu, 26 Mar 2026

New package WeightCraft with initial version 1.0.0
Package: WeightCraft
Title: Portfolio Choice: Estimation, Construction, and Evaluation
Version: 1.0.0
Description: Providing quantitative tools for input estimation, portfolio construction, and performance evaluation.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.0.0)
Imports: quadprog, stats
NeedsCompilation: no
Packaged: 2026-03-22 19:21:23 UTC; Pavlos
Author: Pavlos Pantatosakis [aut, cre], Georgios Skoulakis [aut]
Maintainer: Pavlos Pantatosakis <pantatosakisp@yahoo.com>
Repository: CRAN
Date/Publication: 2026-03-26 09:50:14 UTC

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New package TernTables with initial version 1.6.4
Package: TernTables
Title: Publication-Ready Summary Tables and Statistical Testing for Clinical Research
Version: 1.6.4
Description: Generates publication-ready summary tables for clinical research, supporting descriptive summaries and comparisons across two or three groups. The package streamlines the analytical workflow by detecting variable types and applying appropriate statistical tests (Welch t-test, Wilcoxon rank-sum, Welch ANOVA, Kruskal-Wallis, Chi-squared, or Fisher's exact test). Results are formatted as 'tibble' objects and can be exported to 'Word' or 'Excel' using the 'officer', 'flextable', and 'writexl' packages. Optional pairwise post-hoc testing for three-group comparisons (Games-Howell and Dunn's test) is available via the 'rstatix' package. Example data are derived from the landmark adjuvant colon cancer trial described in Moertel et al. (1990) <doi:10.1056/NEJM199002083220602>.
License: MIT + file LICENSE
URL: https://github.com/jdpreston30/TernTables, https://tern-tables.com/
BugReports: https://github.com/jdpreston30/TernTables/issues
Encoding: UTF-8
Imports: cli, dplyr (>= 1.0.0), epitools, flextable (>= 0.9.0), magrittr, officer (>= 0.4.6), rlang, stats, stringr, tibble, withr, writexl
Suggests: knitr, multcompView, rmarkdown, rstatix, survival, testthat (>= 3.0.0)
VignetteBuilder: knitr
Depends: R (>= 4.1.0)
LazyData: true
NeedsCompilation: no
Packaged: 2026-03-23 19:59:17 UTC; jdp2019
Author: Joshua D. Preston [aut, cre] , Helen Abadiotakis [aut] , Ailin Tang [aut] , Clayton J. Rust [aut] , Joshua L. Chan [aut]
Maintainer: Joshua D. Preston <joshua.preston@emory.edu>
Repository: CRAN
Date/Publication: 2026-03-26 10:00:21 UTC

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New package SuperSurv with initial version 0.1.1
Package: SuperSurv
Title: A Unified Framework for Machine Learning Ensembles in Survival Analysis
Version: 0.1.1
Description: Implements a Super Learner framework for right-censored survival data. The package fits convex combinations of parametric, semiparametric, and machine learning survival learners by minimizing cross-validated risk using inverse probability of censoring weighting (IPCW). It provides tools for automated hyperparameter grid search, high-dimensional variable screening, and evaluation of prediction performance using metrics such as the Brier score, Uno's C-index, and time-dependent area under the curve (AUC). Additional utilities support model interpretation for survival ensembles, including Shapley additive explanations (SHAP), and estimation of covariate-adjusted restricted mean survival time (RMST) contrasts. The methodology is related to treatment-specific survival curve estimation using machine learning described by Westling, Luedtke, Gilbert and Carone (2024) <doi:10.1080/01621459.2023.2205060>, and the unified ensemble framework described in Lyu et al. (2026) <doi:10.64898/20 [...truncated...]
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.0.0)
LazyData: true
Imports: survival, nnls, future.apply, stats, dplyr, magrittr
URL: https://github.com/yuelyu21/SuperSurv, https://yuelyu21.github.io/SuperSurv/
BugReports: https://github.com/yuelyu21/SuperSurv/issues
Suggests: aorsf, BART, CoxBoost, glmnet, gbm, mgcv, randomForestSRC, ranger, rpart, survivalsvm, xgboost, fastshap, survex, ggplot2, tidyr, quadprog, ggforce, patchwork, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-03-23 08:46:55 UTC; a98455
Author: Yue Lyu [aut, cre]
Maintainer: Yue Lyu <yuelyu0521@gmail.com>
Repository: CRAN
Date/Publication: 2026-03-26 10:00:27 UTC

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New package soilFlux with initial version 0.1.5
Package: soilFlux
Title: Physics-Informed Neural Networks for Soil Water Retention Curves
Version: 0.1.5
Description: Implements a physics-informed one-dimensional convolutional neural network (CNN1D-PINN) for estimating the complete soil water retention curve (SWRC) as a continuous function of matric potential, from soil texture, organic carbon, bulk density, and depth. The network architecture ensures strict monotonic decrease of volumetric water content with increasing suction by construction, through cumulative integration of non-negative slope outputs (monotone integral architecture). Four physics-based residual constraints adapted from Norouzi et al. (2025) <doi:10.1029/2024WR038149> are embedded in the loss function: (S1) linearity at the dry end (pF in [5, 7.6]); (S2) non-negativity at pF = 6.2; (S3) non-positivity at pF = 7.6; and (S4) a near-zero derivative in the saturated plateau region (pF in [-2, -0.3]). Includes tools for data preparation, model training, dense prediction, performance metrics, texture classification, and publication-quality visualisation.
License: MIT + file LICENSE
Encoding: UTF-8
SystemRequirements: Python (>= 3.8), TensorFlow (>= 2.14), Keras (>= 3.0)
LazyData: true
LazyDataCompression: xz
Depends: R (>= 4.1.0)
Imports: dplyr (>= 1.1.0), tidyr (>= 1.3.0), ggplot2 (>= 3.4.0), purrr (>= 1.0.0), tibble (>= 3.2.0), stringr (>= 1.5.0), reticulate (>= 1.34.0), tensorflow (>= 2.14.0), rlang (>= 1.1.0)
Suggests: keras3 (>= 1.0.0), ggtern, readxl (>= 1.4.0), scales (>= 1.2.0), knitr, rmarkdown, testthat (>= 3.0.0), withr
VignetteBuilder: knitr
URL: https://github.com/HugoMachadoRodrigues/soilFlux, https://hugomachadorodrigues.github.io/soilFlux/
BugReports: https://github.com/HugoMachadoRodrigues/soilFlux/issues
NeedsCompilation: no
Packaged: 2026-03-21 21:09:59 UTC; rodrigues.h
Author: Hugo Rodrigues [aut, cre]
Maintainer: Hugo Rodrigues <rodrigues.machado.hugo@gmail.com>
Repository: CRAN
Date/Publication: 2026-03-26 09:30:02 UTC

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New package SeroTrackR with initial version 1.0.0
Package: SeroTrackR
Title: Serology-Based Data Analysis and Visualization
Version: 1.0.0
Description: Data wrangling and cleaning, quality control checks and implementation of machine learning classification algorithm.
License: CC BY 4.0
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: dplyr, drc, forcats, ggplot2, here, janitor, kableExtra, knitr, magrittr, openxlsx, parsnip, purrr, ranger, readr, readxl, rmarkdown, stats, stringr, tidyr, tidyselect, utils, workflows
URL: https://github.com/dionnecargy/SeroTrackR, https://dionnecargy.github.io/SeroTrackR/
BugReports: https://github.com/dionnecargy/SeroTrackR/issues
Suggests: glue, htmltools, httr, jsonlite, shiny.fluent, tidyverse, zoo
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-03-23 04:28:32 UTC; argyropoulos.d
Author: Dionne Argyropoulos [aut, cre]
Maintainer: Dionne Argyropoulos <argyropoulos.d@wehi.edu.au>
Repository: CRAN
Date/Publication: 2026-03-26 10:00:36 UTC

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New package rtemis with initial version 1.0.0
Package: rtemis
Version: 1.0.0
Title: Machine Learning and Visualization
Date: 2026-03-14
Description: Machine learning and visualization package with an 'S7' backend featuring comprehensive type checking and validation, paired with an efficient functional user-facing API. train(), cluster(), and decomp() provide one-call access to supervised and unsupervised learning. All configuration steps are performed using setup functions and validated. A single call to train() handles preprocessing, hyperparameter tuning, and testing with nested resampling. Supports 'data.frame', 'data.table', and 'tibble' inputs, parallel execution, and interactive visualizations. The package first appeared in E.D. Gennatas (2017) <https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.
License: GPL (>= 3)
URL: https://www.rtemis.org, https://docs.rtemis.org/r/, https://docs.rtemis.org/r-api/
BugReports: https://github.com/rtemis-org/rtemis/issues
ByteCompile: yes
Depends: R (>= 4.1.0)
Imports: grDevices, graphics, stats, methods, utils, S7, data.table, future, htmltools, cli
Suggests: arrow, bit64, car, colorspace, DBI, dbscan, dendextend (>= 0.18.0), duckdb, e1071, farff, fastICA, flexclust, future.apply, future.mirai, futurize, geosphere, ggplot2, glmnet, geojsonio, glue, grid, gsubfn, haven, heatmaply, htmlwidgets, igraph, jsonlite, leaflet, leaps, lightAUC, lightgbm, matrixStats, mgcv, mice, mirai, missRanger, networkD3, NMF, openxlsx, parallelly, partykit, plotly, pROC, progressr, psych, pvclust, ranger, reactable, readxl, reticulate, ROCR, rpart, Rtsne, seqinr, sf, shapr, survival, tabnet, threejs, testthat (>= 3.0.0), tibble, timeDate, toml, torch, uwot, vegan, vroom, withr
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2026-03-23 09:34:39 UTC; egenn
Author: E.D. Gennatas [aut, cre, cph]
Maintainer: E.D. Gennatas <gennatas@gmail.com>
Repository: CRAN
Date/Publication: 2026-03-26 10:00:02 UTC

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New package rcloner with initial version 0.0.1
Package: rcloner
Title: Interface to 'rclone' Cloud Storage Utility
Version: 0.0.1
Description: Provides an R interface to 'rclone' <https://rclone.org>, a command-line program for managing files on cloud storage. 'rclone' supports over 40 cloud storage providers including 'S3'-compatible services ('Amazon S3', 'MinIO', 'Ceph'), 'Google Cloud Storage', 'Azure Blob Storage', and many others. This package downloads and manages the 'rclone' binary automatically and wraps its commands as R functions, returning results as data frames where appropriate.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: fs, jsonlite, processx, utils
Suggests: covr, knitr, rmarkdown, spelling, testthat (>= 3.0.0), withr
Language: en-US
VignetteBuilder: knitr
URL: https://boettiger-lab.github.io/rcloner/, https://github.com/boettiger-lab/rcloner
BugReports: https://github.com/boettiger-lab/rcloner/issues
NeedsCompilation: no
Packaged: 2026-03-22 20:57:50 UTC; cboettig
Author: Carl Boettiger [aut, cre]
Maintainer: Carl Boettiger <cboettig@gmail.com>
Repository: CRAN
Date/Publication: 2026-03-26 09:50:02 UTC

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New package radEmu with initial version 2.3.1.0
Package: radEmu
Title: Using Relative Abundance Data to Estimate of Multiplicative Differences in Mean Absolute Abundance
Version: 2.3.1.0
Description: A differential abundance method for the analysis of microbiome data. 'radEmu' estimates fold-differences in the abundance of taxa across samples relative to "typical" fold-differences. Notably, it does not require pseudocounts, nor choosing a denominator taxon. For more details, see Clausen et al. (2026) <doi:10.1093/biomet/asag009>.
URL: https://github.com/statdivlab/radEmu, https://statdivlab.github.io/radEmu/
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: MASS, Matrix, methods, magrittr, rlang, R (>= 2.10)
Suggests: testthat (>= 3.0.0), numDeriv, phyloseq, TreeSummarizedExperiment, SummarizedExperiment, SingleCellExperiment, knitr, dplyr, ggplot2, stringr, parallel, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-03-20 20:42:23 UTC; sarahteichman
Author: David Clausen [aut], Amy D Willis [aut, cre] , Sarah Teichman [aut]
Maintainer: Amy D Willis <adwillis@uw.edu>
Repository: CRAN
Date/Publication: 2026-03-26 09:20:02 UTC

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New package netrics with initial version 0.2.0
Package: netrics
Title: Many Ways to Measure and Classify Membership for Networks, Nodes, and Ties
Version: 0.2.0
Date: 2026-03-21
Description: Many tools for calculating network, node, or tie marks, measures, motifs and memberships of many different types of networks. Marks identify structural positions, measures quantify network properties, memberships classify nodes into groups, and motifs tabulate substructure participation. All functions operate with all classes of network data covered in 'manynet', and on directed, undirected, multiplex, multimodal, signed, and other networks.
URL: https://stocnet.github.io/netrics/
BugReports: https://github.com/stocnet/netrics/issues
License: MIT + file LICENSE
Language: en-GB
Encoding: UTF-8
Depends: R (>= 3.6.0), manynet
Imports: dplyr, igraph (>= 2.1.0)
Suggests: autograph, sna, testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2026-03-21 19:41:35 UTC; hollway
Author: James Hollway [cre, aut, ctb]
Maintainer: James Hollway <james.hollway@graduateinstitute.ch>
Repository: CRAN
Date/Publication: 2026-03-26 09:30:09 UTC

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Package MolgenisArmadillo updated to version 3.0.0 with previous version 2.9.3 dated 2025-12-06

Title: Armadillo Client for the Armadillo Service
Description: A set of functions to manage data shared on a 'MOLGENIS Armadillo' server.
Author: Mariska Slofstra [aut, cre] , Sido Haakma [aut] , Tommy de Boer [aut] , Fleur Kelpin [aut] , Morris Swertz [aut] , Tim Cadman [aut]
Maintainer: Mariska Slofstra <m.k.slofstra@umcg.nl>

Diff between MolgenisArmadillo versions 2.9.3 dated 2025-12-06 and 3.0.0 dated 2026-03-26

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New package longitree with initial version 1.0.0
Package: longitree
Title: Decision Tree Analysis for Longitudinal Measurement Data
Version: 1.0.0
Maintainer: Ryoto Obata <ryoto.obata@gmail.com>
Description: Implements tree-based methods for longitudinal data. The package constructs decision trees that evaluate both the main effect of a covariate and its interaction with time through a weighted splitting criterion. It supports single-tree construction, bootstrap-based multiple-tree selection, and tree visualisation. For methodological details, see Obata and Sugimoto (2026) <doi:10.1007/s11634-025-00665-2>.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
Depends: R (>= 3.5.0)
Imports: stats, graphics, partykit, ggparty, ggplot2, lme4, utils
Suggests: testthat (>= 3.0.0)
Packaged: 2026-03-22 10:37:44 UTC; jovyan
Author: Ryoto Obata [aut, cre], Tomoyuki Sugimoto [aut]
Repository: CRAN
Date/Publication: 2026-03-26 09:40:02 UTC

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New package L0ggm with initial version 0.0.1
Package: L0ggm
Title: Smooth L0 Penalty Approximations for Gaussian Graphical Models
Version: 0.0.1
Date: 2026-03-21
Description: Provides smooth approximations to the L0 norm penalty for estimating sparse Gaussian graphical models (GGMs). Network estimation is performed using the Local Linear Approximation (LLA) framework (Fan & Li, 2001 <doi:10.1198/016214501753382273>; Zou & Li, 2008 <doi:10.1214/009053607000000802>) with five penalty functions: arctangent (Wang & Zhu, 2016 <doi:10.1155/2016/6495417>), EXP (Wang, Fan, & Zhu, 2018 <doi:10.1007/s10463-016-0588-3>), Gumbel, Log (Candes, Wakin, & Boyd, 2008 <doi:10.1007/s00041-008-9045-x>), and Weibull. Adaptive penalty parameters for EXP, Gumbel, and Weibull are estimated via maximum likelihood, and model selection uses information criteria including AIC, BIC, and EBIC (Extended BIC). Simulation functions generate multivariate normal data from GGMs with stochastic block model or small-world (Watts-Strogatz) network structures.
Depends: R (>= 3.5.0)
License: AGPL (>= 3.0)
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
Imports: igraph, glasso, glassoFast, Matrix, methods, psych, stats
Copyright: See inst/COPYRIGHTS for details
BugReports: https://github.com/AlexChristensen/L0ggm/issues
Packaged: 2026-03-21 16:01:20 UTC; alextops
Author: Alexander Christensen [aut, cre] , Jeongwon Choi [ctb] , John Fox [cph, ctb] , Yves Rosseel [cph, ctb] , Alexander Robitzsch [cph, ctb] , David Blackman [ctb] , Sebastiano Vigna [ctb] , John Burkardt [cph, ctb]
Maintainer: Alexander Christensen <alexpaulchristensen@gmail.com>
Repository: CRAN
Date/Publication: 2026-03-26 09:30:27 UTC

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New package ixsurface with initial version 0.1.0
Package: ixsurface
Title: Interactive 3D Surface Plots for Multi-Factor Interaction Visualization
Version: 0.1.0
Description: Visualize interactions between multiple experimental factors using interactive 3D surface plots powered by 'plotly'. Instead of examining combinatorial pairwise interaction plots, map factor combinations to response surfaces and use surface crossings as geometric indicators of interaction effects. Supports continuous, categorical, and mixed factor designs with automatic binning for continuous conditioning variables.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/cjbrant/ixsurface
BugReports: https://github.com/cjbrant/ixsurface/issues
Imports: plotly (>= 4.10.0), stats, utils, grDevices
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-03-21 19:06:11 UTC; christopherbrantner
Author: Chris Brantner [aut, cre]
Maintainer: Chris Brantner <cjbrantn@gmail.com>
Repository: CRAN
Date/Publication: 2026-03-26 09:30:22 UTC

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New package hhdynamics with initial version 1.3.2
Package: hhdynamics
Title: Fitting Household Transmission Model to Estimate Household Transmission Dynamics of Influenza
Version: 1.3.2
Maintainer: Tim Tsang <timkltsang@gmail.com>
Description: A Bayesian household transmission model to estimate household transmission dynamics, with accounting for infection from community and tertiary cases.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: TRUE
URL: https://github.com/timktsang/hhdynamics
BugReports: https://github.com/timktsang/hhdynamics/issues
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 1.0.9), RcppParallel
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
SystemRequirements: GNU make
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-03-23 22:45:29 UTC; timtsang
Author: Tim Tsang [aut, cre]
Repository: CRAN
Date/Publication: 2026-03-26 10:00:15 UTC

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New package growthDecomp with initial version 0.1.0
Package: growthDecomp
Title: Decomposition of Growth Trends
Version: 0.1.0
Description: Decomposes observed growth in agricultural and livestock systems into interpretable component effects. Depending on the application, the total change in output can be attributed to components such as area effect, yield effect, herd or slaughter effect, productivity effect, and interaction effect. Details can be found in Rakshit and Bardhan (2026) <doi:10.1007/s11250-026-04988-w>.
Author: Debopam Rakshit [aut, cre], Dwaipayan Bardhan [aut]
Maintainer: Debopam Rakshit <rakshitdebopam@yahoo.com>
Imports: fracdiff
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-03-17 18:21:47 UTC; Debopam
Repository: CRAN
Date/Publication: 2026-03-26 09:50:08 UTC

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New package animejs with initial version 0.1.0
Package: animejs
Title: R Bindings to the 'Anime.js' Animation Library
Version: 0.1.0
Description: Provides low-level R bindings to the 'Anime.js' library (<https://animejs.com>), enabling the creation of browser-native SVG and HTML animations via the 'htmlwidgets' framework.
License: MIT + file LICENSE
URL: https://github.com/long39ng/animejs, https://long39ng.github.io/animejs/
BugReports: https://github.com/long39ng/animejs/issues
Depends: R (>= 4.1.0)
Imports: htmlwidgets, rlang
Suggests: htmltools, jsonlite, knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-03-22 10:12:54 UTC; long
Author: Long Nguyen [aut, cre]
Maintainer: Long Nguyen <nguyen@dezim-institut.de>
Repository: CRAN
Date/Publication: 2026-03-26 09:40:09 UTC

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Package TFORGE updated to version 0.1.17 with previous version 0.1.16 dated 2025-12-12

Title: Tests for Geophysical Eigenvalues
Description: The eigenvalues of observed symmetric matrices are often of intense scientific interest. This package offers single sample tests for the eigenvalues of the population mean or the eigenvalue-multiplicity of the population mean. For k-samples, this package offers tests for equal eigenvalues between samples. Included is support for matrices with constraints common to geophysical tensors (constant trace, sum of squared eigenvalues, or both) and eigenvectors are usually considered nuisance parameters. Pivotal bootstrap methods enable these tests to have good performance for small samples (n=15 for 3x3 matrices). These methods were developed and studied by Hingee, Scealy and Wood (2026, <doi:10.1080/01621459.2025.2606381>). Also available is a 2-sample test using a Gaussian orthogonal ensemble approximation and an eigenvalue-multiplicity test that assumes orthogonally-invariant covariance.
Author: Kassel Liam Hingee [aut, cre] , Art B. Owen [cph] , Board of Trustees Leland Stanford Junior University [cph]
Maintainer: Kassel Liam Hingee <kassel.hingee@gmail.com>

Diff between TFORGE versions 0.1.16 dated 2025-12-12 and 0.1.17 dated 2026-03-26

 DESCRIPTION              |   12 ++++++------
 MD5                      |   18 +++++++++---------
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Package SAPP updated to version 1.0.9-4 with previous version 1.0.9-1 dated 2023-06-01

Title: Statistical Analysis of Point Processes
Description: Functions for statistical analysis of point processes.
Author: Yoshinori Kawasaki [aut, cre], Junji Nakano [aut], Masami Saga [aut], The Institute of Statistical Mathematics [cph, fnd]
Maintainer: Yoshinori Kawasaki <kawasaki@ism.ac.jp>

Diff between SAPP versions 1.0.9-1 dated 2023-06-01 and 1.0.9-4 dated 2026-03-26

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Package PwePred updated to version 1.1.2 with previous version 1.1.1 dated 2026-03-24

Title: Event/Timeline Prediction Model Based on Piecewise Exponential
Description: Efficient algorithm for estimating piecewise exponential hazard models for right-censored data, and is useful for reliable power calculation, study design, and event/timeline prediction for study monitoring.
Author: Tianchen Xu [aut, cre]
Maintainer: Tianchen Xu <zjph602xutianchen@gmail.com>

Diff between PwePred versions 1.1.1 dated 2026-03-24 and 1.1.2 dated 2026-03-26

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Package NScluster updated to version 1.3.6-5 with previous version 1.3.6-4 dated 2025-04-30

Title: Simulation and Estimation of the Neyman-Scott Type Spatial Cluster Models
Description: Simulation and estimation for Neyman-Scott spatial cluster point process models and their extensions, based on the methodology in Tanaka, Ogata, and Stoyan (2008) <doi:10.1002/bimj.200610339>. To estimate parameters by the simplex method, parallel computation using 'OpenMP' application programming interface is available. For more details see Tanaka, Saga and Nakano <doi:10.18637/jss.v098.i06>.
Author: Ushio Tanaka [aut, cre], Junji Nakano [aut], Masami Saga [aut]
Maintainer: Ushio Tanaka <utanaka@omu.ac.jp>

Diff between NScluster versions 1.3.6-4 dated 2025-04-30 and 1.3.6-5 dated 2026-03-26

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Package catdap updated to version 1.3.7-4 with previous version 1.3.7 dated 2023-05-31

Title: Categorical Data Analysis Program Package
Description: Categorical data analysis by AIC. The methodology is described in Sakamoto (1992) <ISBN 978-0-7923-1429-5>.
Author: Yoshinori Kawasaki [aut, cre], Junji Nakano [aut], Masami Saga [aut], The Institute of Statistical Mathematics [cph, fnd]
Maintainer: Yoshinori Kawasaki <kawasaki@ism.ac.jp>

Diff between catdap versions 1.3.7 dated 2023-05-31 and 1.3.7-4 dated 2026-03-26

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Package htetree updated to version 0.1.23 with previous version 0.1.20 dated 2025-01-13

Title: Causal Inference with Tree-Based Machine Learning Algorithms
Description: Estimating heterogeneous treatment effects with tree-based machine learning algorithms and visualizing estimated results in flexible and presentation-ready ways. For more information, see Brand, Xu, Koch, and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package first started as a fork of the 'causalTree' package on 'GitHub' and we greatly appreciate the authors for their extremely useful and free package.
Author: Jiahui Xu [cre, aut], Tanvi Shinkre [aut], Jennie Brand [aut]
Maintainer: Jiahui Xu <jiahuixu@ucla.edu>

Diff between htetree versions 0.1.20 dated 2025-01-13 and 0.1.23 dated 2026-03-26

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Package party updated to version 1.3-20 with previous version 1.3-19 dated 2026-03-10

Title: A Laboratory for Recursive Partytioning
Description: A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) <doi:10.1198/106186006X133933>, Zeileis et al. (2008) <d [...truncated...]
Author: Torsten Hothorn [aut, cre] , Kurt Hornik [aut] , Carolin Strobl [aut] , Achim Zeileis [aut]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between party versions 1.3-19 dated 2026-03-10 and 1.3-20 dated 2026-03-26

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Package duckdb updated to version 1.5.1 with previous version 1.5.0 dated 2026-03-14

Title: DBI Package for the DuckDB Database Management System
Description: The DuckDB project is an embedded analytical data management system with support for the Structured Query Language (SQL). This package includes all of DuckDB and an R Database Interface (DBI) connector.
Author: Hannes Muehleisen [aut] , Mark Raasveldt [aut] , Kirill Mueller [cre] , Stichting DuckDB Foundation [cph], Apache Software Foundation [cph], PostgreSQL Global Development Group [cph], The Regents of the University of California [cph], Cameron Desrocher [...truncated...]
Maintainer: Kirill Mueller <kirill@cynkra.com>

Diff between duckdb versions 1.5.0 dated 2026-03-14 and 1.5.1 dated 2026-03-26

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Package degradr updated to version 1.0.2 with previous version 1.0.1 dated 2025-10-01

Title: Estimating Remaining Useful Life with Linear Mixed Effects Models
Description: Provides tools for estimating the Remaining Useful Life (RUL) of degrading systems using linear mixed-effects models and creating a health index. It supports both univariate and multivariate degradation signals. For multivariate inputs, the signals are merged into a univariate health index prior to modeling. Linear and exponential degradation trajectories are supported (the latter using a log transformation). Remaining Useful Life (RUL) distributions are estimated using Bayesian updating for new units, enabling on-site predictive maintenance. Based on the methodology of Liu and Huang (2016) <doi:10.1109/TASE.2014.2349733>.
Author: Pedro Abraham Montoya Calzada [aut, cre, cph] , Rogelio Salinas Gutierrez [aut, cph] , Silvia Rodriguez-Narciso [aut, cph] , Netzahualcoyotl Castaneda-Leyva [aut, cph]
Maintainer: Pedro Abraham Montoya Calzada <pedroabraham.montoya@gmail.com>

Diff between degradr versions 1.0.1 dated 2025-10-01 and 1.0.2 dated 2026-03-26

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