This package provides a re-implementation of the gWidgets API. The API is defined in this package. A second, toolkit-specific package is required to use it.

This package provides tools to more conveniently perform tasks associated with add-on packages. pacman conveniently wraps library and package related functions and names them in an intuitive and consistent fashion. It seeks to combine functionality from lower level functions which can speed up workflow.

This package provides routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons.

httpcode provides functionality for finding and explaining the meaning of HTTP status codes. Functions are included for searching for codes by full or partial number, by message, and to get appropriate dog and cat images for many status codes.

Compute time-dependent ROC curve from censored survival data using Kaplan-Meier (KM) or Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No 2, 2000, PP 337-344)

This package provides the header files for a stripped-down version of the plog header-only C++ logging library, and a method to log to R's standard error stream.

ActiLife generates activity counts from data collected by Actigraph accelerometers. Actigraph is one of the most common research-grade accelerometers. There is considerable research validating and developing algorithms for human activity using ActiLife counts. Unfortunately, ActiLife counts are proprietary and difficult to implement if researchers use different accelerometer brands. The code creates ActiLife counts from raw acceleration data for different accelerometer brands.

This package simulates the process of installing a package and then attaching it. This is a key part of the devtools package as it allows you to rapidly iterate while developing a package.

This package can be used to solve Linear Programming / Linear Optimization problems by using the simplex algorithm.

This package provides an implementation of sparse linear discriminant analysis, which is a supervised classification method for multiple classes. Various novel optimization approaches to this problem are implemented including alternating direction method of multipliers (ADMM), proximal gradient (PG) and accelerated proximal gradient (APG). Functions for performing cross validation are also supplied along with basic prediction and plotting functions. Sparse zero variance discriminant (SZVD) analysis is also included in the package.

This package provides a port of the web-based software DAGitty for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.

This is an implementation of the Future API on top of the callr package. This allows you to process futures, as defined by the future package, in parallel out of the box, on your local machine. Contrary to backends relying on the parallel package (e.g. future::multisession) and socket connections, the callr backend provided here can run more than 125 parallel R processes.

This package provides bindings to GnuPG for working with OpenGPG (RFC4880) cryptographic methods. It includes utilities for public key encryption, creating and verifying digital signatures, and managing your local keyring. Some functionality depends on the version of GnuPG that is installed on the system.

This package provides an R API to the Open Source Geometry Engine (GEOS) library and a vector format with which to efficiently store GEOS geometries. High-performance functions to extract information from, calculate relationships between, and transform geometries are provided. Finally, facilities to import and export geometry vectors to other spatial formats are provided.

This is a companion package for the book "A Course in Statistics with R" (ISBN 978-1-119-15272-9.)

This package provides a set of functions for data manipulation with list objects, including mapping, filtering, grouping, sorting, updating, searching, and other useful functions. Most functions are designed to be pipeline friendly so that data processing with lists can be chained.

This package provides a command line parser inspired by Python's optparse library to be used with Rscript to write shebang scripts that accept short and long options.

This package offers an interactive function for the detection of breakpoints in series.

The main purpose of this package is to provide the algorithmic complexity for short strings, an approximation of the Kolmogorov Complexity of a short string using the coding theorem method. While the database containing the complexity is provided in the data only package acss.data, this package provides functions accessing the data such as prob_random returning the posterior probability that a given string was produced by a random process. In addition, two traditional (but problematic) measures of complexity are also provided: entropy and change complexity.

This package provides a collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics.

This package provides functions to export graphics drawn with package grid to SVG format. Extra functions provide access to SVG features that are not available in standard R graphics, such as hyperlinks, animation, filters, masks, clipping paths, and gradient and pattern fills.

This is a package for the analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported.

This package provides tools for the calibration of penalized criteria for model selection. The calibration methods available are based on the slope heuristics.

This package handles very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. The package includes a vignette that gives a step-by-step introduction to using S4 methods.