This package provides infrastructure for psychometric modeling such as data classes (for item response data and paired comparisons), basic model fitting functions (for Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit, rating scale, multinomial processing tree models), extractor functions for different types of parameters (item, person, threshold, discrimination, guessing, upper asymptotes), unified inference and visualizations, and various datasets for illustration. It is intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package psychomix and trees based on psychometric models in package psychotree.

This package provides a comprehensive collection of color palettes, color maps, and tools to evaluate them.

This package provides functionality to create pretty word clouds, visualize differences and similarity between documents, and avoid over-plotting in scatter plots with text.

This package provides a replication of key functionality from dplyr and the wider tidyverse using only base.

Estimate generalized additive mixed models via a version of function gamm from the mgcv package, using the lme4 packagefor estimation.

This package provides a data.table backend for dplyr. The goal of dtplyr is to allow you to write dplyr code that is automatically translated to the equivalent, but usually much faster, data.table code.

This package provides data used as examples to demonstrate GAMLSS models.

This package provides an implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available.

This package lets you take formulas including random-effects components (formatted as in lme4, glmmTMB, etc.) and process them. It includes various helper functions.

This package provides a set of functions to analyze overdispersed counts or proportions. Most of the methods are already available elsewhere but are scattered in different packages. The proposed functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM).

This package performs optimization in R using C++. A unified wrapper interface is provided to call C functions of the five optimization algorithms (Nelder-Mead, BFGS, CG, L-BFGS-B and SANN) underlying optim().

This package provides some functions for sample classification in microarrays.

This package provides a collection of functions useful in learning and practicing Item Response Theory (IRT), which can be combined into larger programs. It provides basic CTT analysis, a simple common interface to the estimation of item parameters in IRT models for binary responses with three different programs (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord, and the complete Hebaera method), and a rich array of parametric and non-parametric (kernel) plots. It estimates and plots Haberman's interaction model when all items are dichotomously scored.

How fast can you type R functions on your keyboard? Find out by running a zty.pe game: export R functions as instructions to type to destroy opponents' vessels.

This package provides tools for data frame summaries, cross-tabulations, weight-enabled frequency tables and common univariate statistics in concise tables available in a variety of formats (plain ASCII, Markdown and HTML). A good point-of-entry for exploring data, both for experienced and new R users.

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

Function-oriented Make-like declarative pipelines for statistics and data science are supported in the targets R package. As an extension to targets, the tarchetypes package provides convenient user-side functions to make targets easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the drake R package by Will Landau (2018) <doi:10.21105/joss.00550>.

Access to selected functions found in liblwgeom, the light-weight geometry library used by PostGIS.

This package provides UI widget and layout functions for writing Shiny apps that work well on small screens.

This package provides utilities for secure password hashing via the argon2 algorithm.

This package provides functions to perform reproducible parallel foreach loops, using independent random streams as generated by L'Ecuyer's combined multiple-recursive generator. It enables to easily convert standard %dopar% loops into fully reproducible loops, independently of the number of workers, the task scheduling strategy, or the chosen parallel environment and associated foreach backend.

This package provides model-robust standard error estimators for cross-sectional, time series, clustered, panel, and longitudinal data.

r-selectr translates a CSS3 selector into an equivalent XPath expression. This allows you to use CSS selectors when working with the XML package as it can only evaluate XPath expressions. Also provided are convenience functions useful for using CSS selectors on XML nodes. This package is a port of the Python package cssselect.

Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression and state-space models. They are also known as observation-driven and parameter-driven models respectively in the time series literature. The functions used for Markov regression or observation-driven models can also be used to fit ordinary regression models with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) assumption. The package also contains miscellaneous functions to compute density, distribution, quantile, and generate random numbers from ZIP and ZINB distributions.