This package lets you easily use Bootstrap icons inside Shiny apps and R Markdown documents. More generally, icons can be inserted in any htmltools document through inline SVG.

This package provides functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).

This package provides a set of predicates and assertions for checking the properties of UK-specific complex data types. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package contains functions to estimate L-moments and trimmed L-moments from the data. It also contains functions to estimate the parameters of the normal polynomial quantile mixture and the Cauchy polynomial quantile mixture from L-moments and trimmed L-moments.

The labeling package provides a range of axis labeling algorithms.

This package provides p-values in type I, II or III anova and summary tables for lmer model fits via Satterthwaite's degrees of freedom method. A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available.

This package provides alternative statistical methods for meta-analysis, including:

1. bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences

2. heterogeneity tests and measures that are robust to outliers;

3. measures, tests, and visualization tools for publication bias or small-study effects;

4. meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc.;

5. meta-analysis methods for synthesizing proportions;

6. models for multivariate meta-analysis.

This package provides a collection of Lua filters that extend the functionality of R Markdown templates (e.g., count words or post-process citations).

This package provides a collection of tools to streamline the process of fitting elastic net models with glmnet. In addition to providing a formula interface, it also features a function cva.glmnet to do crossvalidation for both α and λ, as well as some utility functions.

HTML widgets is a framework for creating HTML widgets that render in various contexts including the R console, R Markdown documents, and Shiny web applications.

This package performs approximate bayesian computation (ABC) model choice and parameter inference via random forests. This machine learning tool named random forests (RF) can conduct selection among the highly complex models covered by ABC algorithms.

This package provides a ggplot2 extension for easy plotting of half-half geom combinations. Think half boxplot and half jitterplot, or half violinplot and half dotplot.

This package provides classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.

This package contains data structures and algorithms for sparse arrays and matrices, based on index arrays and simple triplet representations, respectively.

This package provides unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using the ggplot2 package.

This package provides simulation methods for the evolution of antibody repertoires. The heavy and light chain variable region of both human and C57BL/6 mice can be simulated in a time-dependent fashion. Both single lineages using one set of V-, D-, and J-genes or full repertoires can be simulated. The algorithm begins with an initial V-D-J recombination event, starting the first phylogenetic tree. Upon completion, the main loop of the algorithm begins, with each iteration representing one simulated time step. Various mutation events are possible at each time step, contributing to a diverse final repertoire.

Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The package provides functions for model estimation, dispersion testing, and diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016) <doi:10.1016/j.csda.2016.01.007> is also supported.

This package provides non-parametric (and semi-parametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types.

This package provides an implementation of efficient approximate leave-one-out (LOO) cross-validation for Bayesian models fit using Markov chain Monte Carlo, as described in doi:10.1007/s11222-016-9696-4. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.

Predictors can be converted to one or more numeric representations using a variety of methods. Effect encodings using simple generalized linear models <doi:10.48550/arXiv.1611.09477> or nonlinear models <doi:10.48550/arXiv.1604.06737> can be used. There are also functions for dimension reduction and other approaches.

This package provides utilities for processing Rd objects and files. Extract argument descriptions and other parts of the help pages of functions.

This is a package for mixture and flexible discriminant analysis, multivariate adaptive regression splines (MARS), BRUTO, and so on.

This package provides efficient tools to compute the proximity between rows or columns of large matrices. Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries. Among several built-in similarity/distance measures, computation of correlation, cosine similarity and Euclidean distance is particularly fast.

This package defines the generic method extract and provides openMP support as needed in several packages like aws, adimpro, fmri, and dwi.