Generalized Additive Latent and Mixed Models

Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) , which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling . Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) ) and 'PLmixed' (Rockwood and Jeon (2019) ).

Fast Kernel Density Estimation with Hexagonal Grid

Kernel density estimation with hexagonal grid for bivariate data. Hexagonal grid has many beneficial properties like equidistant neighbours and less edge bias, making it better for spatial analyses than the more commonly used rectangular grid. Carr, D. B. et al. (1987) . Diggle, P. J. (2010) . Hill, B. (2017) < https://blog.bruce-hill.com/meandering-triangles>. Jones, M. C. (1993) .

Bessel Functions Rcpp Interface

Exports an 'Rcpp' interface for the Bessel functions in the 'Bessel' package, which can then be called from the 'C++' code of other packages. For the original 'Fortran' implementation of these functions see Amos (1995) .

Bayesian Generalized Linear Models (IID Samples)

Provides Bayesian linear and generalized linear model fitting with independent and identically distributed (iid) posterior samples. The main functions mirror R's lm() and glm() interfaces while adding prior family specifications for Gaussian, Poisson, binomial, and Gamma models with log-concave likelihoods. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) . The package also includes tools for prior setup, posterior summaries, prediction, diagnostics, simulation, vignettes, and optional 'OpenCL' acceleration for larger models.

Data Sets from "SAS System for Mixed Models

Data sets and sample lmer analyses corresponding to the examples in Littell, Milliken, Stroup and Wolfinger (1996), "SAS System for Mixed Models", SAS Institute.

Linear Fitting for Complex Valued Data

Tools for linear fitting with complex variables. Includes ordinary least-squares (zlm()) and robust M-estimation (rzlm()), and complex methods for oft used generics. Originally adapted from the rlm() functions of 'MASS' and the lm() functions of 'stats'.

'OpenCL'-Ported R 'Mathlib' for GPU-Accelerated Packages

Ships statistical and mathematical routines from R internal 'nmath' ('Mathlib') as 'OpenCL' C sources under directory 'inst/cl/', with R wrappers that use the GPU when 'OpenCL' is available at compile time and fall back to 'stats' equivalents otherwise. Aimed at package developers building custom kernels (for example Bayesian GLMs via suggested package 'glmbayes') using 'opencltools' kernel loaders and related helpers. Contains translated shims, an illustrative GLM-related kernel subsystem, vignettes, and optional GPU acceleration. The ported routines are translated from the 'nmath' ('Mathlib') and 'Rmath' sources of R Core Team (2026) "R: A Language and Environment for Statistical Computing" . 'OpenCL' GPU execution follows the standard described in Stone, Gohara, and Shi (2010) . The likelihood subgradient simulation methodology implemented by the illustrative GLM kernel subsystem is described in Nygren and Nygren (2006) .

Generalized Regression on Orthogonal Components

Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) .

R Fortunes

A collection of fortunes from the R community.

Core C++ Sampling Engine for 'glmbayes'

Core C++ engine for 'glmbayes': envelope-based iid linear and generalized linear model samplers, prior-family routing, and optional 'OpenCL' acceleration. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) . Intended as a developer backend for the 'glmbayes' formula interface; end users should use 'glmbayes' for modelling with interfaces analogous to 'lm' and 'glm'. Mixed-model engines are planned for a future release.