Toolkit for Data Processing, Quality, and Statistical Models

Offers tools for data formatting, anomaly detection, and classification of tree-ring data using spatial comparisons and cross-correlation. Supports flexible detrending and climate–growth modeling via generalized additive mixed models (Wood 2017, ISBN:978-1498728331) and the 'mgcv' package (< https://CRAN.R-project.org/package=mgcv>), enabling robust analysis of non-linear trends and autocorrelated data. Provides standardized visual reporting, including summaries, diagnostics, and model performance. Compatible with '.rwl' files and tailored for the Canadian Forest Service Tree-Ring Data (CFS-TRenD) repository (Girardin et al. (2021) ), offering a comprehensive and adaptable framework for dendrochronologists working with large and complex datasets.

Easy Visualization of Conditional Effects from Regression Models

Offers a flexible and user-friendly interface for visualizing conditional effects from a broad range of regression models, including mixed-effects and generalized additive (mixed) models. Compatible model types include lm(), rlm(), glm(), glm.nb(), betareg(), and gam() (from 'mgcv'); nonlinear models via nls(); generalized least squares via gls(); and survival models via coxph() (from 'survival'). Mixed-effects models with random intercepts and/or slopes can be fitted using lmer(), glmer(), glmer.nb(), glmmTMB(), or gam() (from 'mgcv', via smooth terms). Plots are rendered using base R graphics with extensive customization options. Approximate confidence intervals for nls() and betareg() models are computed using the delta method. Robust standard errors for rlm() are computed using the sandwich estimator (Zeileis 2004) . For beta regression using 'betareg', see Cribari-Neto and Zeileis (2010) . For mixed-effects models with 'lme4', see Bates et al. (2015) . For models using 'glmmTMB', see Brooks et al. (2017) . Methods for generalized additive models using 'mgcv' follow Wood (2017) .

Automate the Creation of Generalized Additive Models (GAMs)

This wrapper package for 'mgcv' makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, 'AutoGAM' tries to automate the procedure of configuring a highly accurate GAM which performs at reasonably high speed, even for large datasets.

Parallelize Common Functions via One Magic Function

The futurize() function turns sequential map-reduce functions such as base::lapply(), purrr::map(), 'foreach::foreach() %do% { ... }' into concurrent alternatives, providing you with a simple, straightforward path to scalable parallel computing via the 'future' ecosystem . By combining this transpiler function with R's native pipe operator, you have a convenient way for speeding up iterative computations with minimal refactoring, e.g. 'lapply(xs, fcn) |> futurize()', 'purrr::map(xs, fcn) |> futurize()', and 'foreach::foreach(x = xs) %do% { fcn(x) } |> futurize()'. Other map-reduce packages that can be "futurized" are 'BiocParallel', 'plyr', 'crossmap', 'pbapply' packages. There is also support for a growing set of domain-specific packages on CRAN (e.g. 'boot', 'caret', 'DiceKriging', 'ez', 'fgsea', 'fwb', 'gamlss', 'glmmTMB', 'glmnet', 'kernelshap', 'lme4', 'metafor', 'mgcv', 'modelsummary', 'parameters', 'partykit', 'pls', 'pvclust', 'riskRegression', 'rugarch', 'sandwich', 'seriation', 'shapr', 'Sim.DiffProc', 'SimDesign', 'stars', 'strucchange', 'SuperLearner', 'tm', 'TSP', and 'vegan') and on Bioconductor (e.g. 'DESeq2', 'GenomicAlignments', 'GSVA', 'Rsamtools', 'scater', 'scuttle', 'SingleCellExperiment', and 'sva').

Visualization of Spline Effects in GAM and GLM Models

Creates 'ggplot2'-based visualizations of smooth effects from GAM (Generalized Additive Models) fitted with 'mgcv' and spline effects from GLM (Generalized Linear Models). Supports survey-weighted models ('svyglm', 'svycoxph') from the 'survey' package, interaction terms, and provides hazard ratio plots with histograms for survival analysis. Wood (2017, ISBN:9781498728331) provides comprehensive methodology for generalized additive models.

Visualising Statistical Models using Dynamic Nomograms

Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the < https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, and mgcv::gam model objects.

Generalized Additive Models with Hyper Column

An interactive HTML widget of the perspective plot for generalized additive models. An alternative solution of the function mgcv::vis.gam(). This R package author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications. Backward compatibility with user-code published in and is not maintained in versions >= 0.3.0 of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries.

Estimate Diet Proportions Using Multivariate Tweedie Model

Defines predict function that transforms output from a Tweedie Generalized Linear Mixed Model (using 'glmmTMB'), Generalized Additive Model (using 'mgcv'), or spatio-temporal Generalized Linear Mixed Model (using package 'tinyVAST'), and returns predicted proportions (and standard errors) across a grouping variable from an equivalent multivariate-logit Tweedie model. These predicted proportions can then be used for standard plotting and diagnostics. See Thorson et al. 2022 .

Multivariate Spatio-Temporal Models using Structural Equations

Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the 'mgcv' package to specify splines via the formula interface. See Thorson et al. (2025) for more details.

Generalized Kernel Regularized Least Squares

Kernel regularized least squares, also known as kernel ridge regression, is a flexible machine learning method. This package implements this method by providing a smooth term for use with 'mgcv' and uses random sketching to facilitate scalable estimation on large datasets. It provides additional functions for calculating marginal effects after estimation and for use with ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'), and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2024) provide further details.