Mon, 06 Apr 2026

Package TPmsm updated to version 1.2.15 with previous version 1.2.14 dated 2025-09-15

Title: Estimation of Transition Probabilities in Multistate Models
Description: Estimation of transition probabilities for the illness-death model and or the three-state progressive model.
Author: Artur Araujo [aut, cre] , Luis Meira-Machado [aut] , Javier Roca-Pardinas [aut]
Maintainer: Artur Araujo <artur.stat@gmail.com>

Diff between TPmsm versions 1.2.14 dated 2025-09-15 and 1.2.15 dated 2026-04-06

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More information about TPmsm at CRAN
Permanent link

Package spaMM updated to version 4.6.65 with previous version 4.6.1 dated 2025-06-27

Title: Mixed-Effect Models, with or without Spatial Random Effects
Description: Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.
Author: Francois Rousset [aut, cre, cph] , Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol [aut]
Maintainer: Francois Rousset <francois.rousset@umontpellier.fr>

Diff between spaMM versions 4.6.1 dated 2025-06-27 and 4.6.65 dated 2026-04-06

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More information about spaMM at CRAN
Permanent link

Package netrics updated to version 0.2.1 with previous version 0.2.0 dated 2026-03-26

Title: Many Ways to Measure and Classify Membership for Networks, Nodes, and Ties
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.
Author: James Hollway [cre, aut, ctb]
Maintainer: James Hollway <james.hollway@graduateinstitute.ch>

Diff between netrics versions 0.2.0 dated 2026-03-26 and 0.2.1 dated 2026-04-06

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Permanent link

Package n1qn1 updated to version 6.0.1-14 with previous version 6.0.1-13 dated 2026-04-02

Title: Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGS Optimization
Description: Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the 'Scilab' optimization documentation located at <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from 'f2c' to make this a C only binary.
Author: Matthew Fidler [aut, cre], Wenping Wang [aut], Claude Lemarechal [aut, ctb], Joseph Bonnans [ctb], Bill Denney [ctb] , Jean-Charles Gilbert [ctb], Claudia Sagastizabal [ctb], Stephen L. Campbell, [ctb], Jean-Philippe Chancelier [ctb], Ramine Nikoukha [...truncated...]
Maintainer: Matthew Fidler <matthew.fidler@gmail.com>

Diff between n1qn1 versions 6.0.1-13 dated 2026-04-02 and 6.0.1-14 dated 2026-04-06

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More information about n1qn1 at CRAN
Permanent link

Package ggstatsplot updated to version 0.13.6 with previous version 0.13.5 dated 2026-02-06

Title: 'ggplot2' Based Plots with Statistical Details
Description: Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.
Author: Indrajeet Patil [cre, aut, cph]
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

Diff between ggstatsplot versions 0.13.5 dated 2026-02-06 and 0.13.6 dated 2026-04-06

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Package ggmlR updated to version 0.7.0 with previous version 0.6.7 dated 2026-03-29

Title: 'GGML' Tensor Operations for Machine Learning
Description: Provides 'R' bindings to the 'GGML' tensor library for machine learning, designed primarily for 'Vulkan' GPU acceleration with full CPU fallback. Requires 'Vulkan' 1.2+ with legacy pipeline barriers (avoids 'Synchronization2' due to 'RADV' performance issues); supports 'Push Descriptors' ('VK_KHR_push_descriptor') to eliminate descriptor pool overhead when available. 'Vulkan' support is auto-detected at build time on Linux (when 'libvulkan-dev' and 'glslc' are installed) and on Windows (when 'Vulkan' 'SDK' is installed and 'VULKAN_SDK' environment variable is set); all operations fall back to CPU transparently when no GPU is available. Supports tensors up to 5D natively (GGML_MAX_DIMS=5). Implements tensor operations, neural network layers, 'quantization', and a 'Keras'-like sequential model API for building and training networks. Includes 'AdamW' (Adam with Weight decay) and 'SGD' (Stochastic Gradient Descent) optimizers with 'MSE' (Mean Squared Error) and cross-entropy losses. Also p [...truncated...]
Author: Yuri Baramykov [aut, cre], Georgi Gerganov [ctb, cph] , Jeffrey Quesnelle [ctb, cph] , Bowen Peng [ctb, cph] , Mozilla Foundation [ctb, cph]
Maintainer: Yuri Baramykov <lbsbmsu@mail.ru>

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More information about ggmlR at CRAN
Permanent link

Package CLME updated to version 2.0-13 with previous version 2.0-12 dated 2020-06-07

Title: Constrained Inference for Linear Mixed Effects Models
Description: Estimation and inference for linear models where some or all of the fixed-effects coefficients are subject to order restrictions. This package uses the robust residual bootstrap methodology for inference, and can handle some structure in the residual variance matrix.
Author: Casey M. Jelsema [aut, cre], Shyamal D. Peddada [aut]
Maintainer: Casey M. Jelsema <jelsema.casey@gmail.com>

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More information about CLME at CRAN
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