This package provides an R implementation of the Octave package signal, containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

This package provides useful functions to deal with the haven_labelled and haven_labelled_spss classes introduced by the haven package.

This package provides several layout algorithms to visualize networks which are not part of the igraph library. Most are based on the concept of stress majorization by Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>. Some more specific algorithms emphasize hidden group structures in networks or focus on specific nodes.

This package allows for data objects in R to be rendered as HTML tables using the JavaScript library DataTables (typically via R Markdown or Shiny). The DataTables library has been included in this R package.

This package provides a more comfortable interface to work with R data or source files in a key-value fashion.

This package estimates conditional Akaike information in mixed-effect models. These models are fitted using (g)lmer() from lme4, lme() from nlme, and gamm() from mgcv. The provided functions facilitate the computation of the conditional Akaike information for model evaluation.

This package provides advanced tryCatch and try functions for better error handling (logging, stack trace with source code references and support for post-mortem analysis via dump files).

The aim of SHAPforxgboost is to aid in visual data investigations using SHAP (Shapley additive explanation) visualization plots for XGBoost. It provides summary plot, dependence plot, interaction plot, and force plot. It relies on the XGBoost package to produce SHAP values.

This package contains all the datasets for the spatstat package.

This package provides tools to help working with text files. It can return the number of lines; print the first and last lines; convert encoding. Operations are made without reading the entire file before starting, resulting in good performances with large files.

This package provides an easy to use library to setup, apply and make inference with discrete time and discrete space hidden Markov models.

This package provides a set of utilities for client/server computing with R, controlling a remote R session (the server) from a local one (the client).

This package generates version 2 and 4 request signatures for Amazon Web Services (AWS) and provides a mechanism for retrieving credentials from environment variables, AWS credentials files, and EC2 instance metadata. For use on EC2 instances, the package 'aws.ec2metadata' is suggested.

The r-nleqslv package solves a system of nonlinear equations using a Broyden or a Newton method with a choice of global strategies such as line search and trust region. There are options for using a numerical or user supplied Jacobian, for specifying a banded numerical Jacobian and for allowing a singular or ill-conditioned Jacobian.

This package provides for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user-level customization and extension, while simplifying cross-class interoperability.

This package provides an all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).

This package provides utilities to work with indices of effect size and standardized parameters for a wide variety of models, allowing computation and conversion of indices such as Cohen's d, r, odds, etc.

This package provides an iteration of the DEoptim function. It performs global optimization by differential evolution.

This package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.

This package provides a wrapper for the Intro.js library. This package makes it easy to include step-by-step introductions, and clickable hints in a Shiny application. It supports both static introductions in the UI, and programmatic introductions from the server-side.

This package provides functions and vignettes to update data sets in Ecdat and to create, manipulate, plot, and analyze those and similar data sets.

This package implements density, distribution functions, quantile functions and random generation functions for a large number of univariate and multivariate distributions.

This package provides tools for the estimation and simulation of latent variable models.

This package provides statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, and mixed data. It uses a trans-dimensional Markov Chain Monte Carlo (MCMC) approach based on a continuous-time birth-death process.