This package provides a set of simple functions that transforms longitudinal data to estimate the cosinor linear model as described in Tong (1976). Methods are given to summarize the mean, amplitude and acrophase, to predict the mean annual outcome value, and to test the coefficients.

This package provides procedures to create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using ggplot2.

This package implements a generalized version of principal components analysis (GLM-PCA) for dimension reduction of non-normally distributed data such as counts or binary matrices.

This package provides an implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. It includes multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. It also provides methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.

This package provides tools to create some specific Space-Filling Design (SFD) and to test their quality.

Circular Statistics, from "Topics in Circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.

Publish data sets, models, and other R objects, making it easy to share them across projects and with your colleagues. You can pin objects to a variety of "boards", including local folders (to share on a networked drive or with DropBox), Posit Connect, AWS S3, and more.

This package integrates sophisticated mixed modelling methods with a whole genome approach to detecting significant QTL in linkage maps.

This package implements a general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.

The feature package contains functions to display and compute kernel density estimates, significant gradient and significant curvature regions. Significant gradient and/or curvature regions often correspond to significant features (e.g. local modes).

This package provides tools for creating, viewing, and assessing qualitative palettes with many (20-30 or more) colors. See Coombes and colleagues (2019) https://doi:10.18637/jss.v090.c01.

This package provides a collection of functions for interpretation and presentation of regression analysis. These functions are used to produce the statistics lectures in http://pj.freefaculty.org/guides. The package includes regression diagnostics, regression tables, and plots of interactions and "moderator" variables. The emphasis is on "mean-centered" and "residual-centered" predictors. The vignette rockchalk offers a fairly comprehensive overview.

Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. The probably package contains tools for conducting these operations as well as calibration tools and conformal inference techniques for regression models.

This package provides an R interface to the GNU Linear Programming Kit, software for solving large-scale linear programming (LP), mixed integer linear programming (MILP) and other related problems.

This package provides tools for writing and debugging R code. Provides: %.>% dot-pipe (an S3 configurable pipe), unpack/to (R style multiple assignment/return), build_frame() / draw_frame() (data.frame example tools), qc() (quoting concatenate), := (named map builder), let() (converts non-standard evaluation interfaces to parametric standard evaluation interfaces, inspired by gtools::strmacro() and base::bquote()), and more.

This package lets you generate random or human readable and pronounceable identifiers.

UpSet plots are an improvement over Venn Diagram for set overlap visualizations. Striving to bring the best of the UpSetR and ggplot2, this package offers a way to create complex overlap visualisations, using simple and familiar tools.

This package provides an R tool for estimating and partitioning R2 in generalized linear mixed models (GLMMs) based on predictor variance.

This package implements methods to perform fast approximate K-nearest neighbor search on the input matrix. The algorithm is based on the N2 implementation of an approximate nearest neighbor search using hierarchical NSW graphs.

This package creates scatterpie plots, especially useful for plotting pies on a map.

This R package caches the results of a function so that when you call it again with the same arguments it returns the pre-computed value.

This package lets you fit generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) <doi:10.1080/10618600.1995.10474663>.

This package provides an implementation of a data cube extracted out of dplyr for backward compatibility.

This package contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX code, and recoding variables.