Imports data from the SurveyXact commercial service <https://rambollxact.com>, adds variable labels, and converts value labels to factors in the same order as exported from SurveyXact'.

An RStudio addin providing a visual, point-and-click interface for data manipulation and analysis, designed for users transitioning from SPSS to R. Every visual action generates clean, reproducible R code using tidyverse conventions ('dplyr', tidyr'). Includes a context-aware AI copilot supporting multiple LLM providers ('OpenAI', Anthropic Claude, Google Gemini') with built-in proxy support and privacy controls.

This package provides access to ArcGIS geoprocessing tools by building an interface between R and the ArcPy Python side-package via the reticulate package.

We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).

Use JSON templates to create folders and files structure for data science projects. Includes customized templates and accepts your own as JSON files.

This package provides a plug in for using WinEdt as an editor for R.

MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, small n large p transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.

The RJDBC package is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.

This package provides the robust gamma rank correlation coefficient as introduced by Bodenhofer, Krone, and Klawonn (2013) <DOI:10.1016/j.ins.2012.11.026> along with a permutation-based rank correlation test. The rank correlation coefficient and the test are explicitly designed for dealing with noisy numerical data.

The renewal Hawkes (RHawkes) process (Wheatley, Filimonov, and Sornette, 2016 <doi:10.1016/j.csda.2015.08.007>) is an extension to the classical Hawkes self-exciting point process widely used in the modelling of clustered event sequence data. This package provides functions to simulate the RHawkes process with a given immigrant hazard rate function and offspring birth time density function, to compute the exact likelihood of a RHawkes process using the recursive algorithm proposed by Chen and Stindl (2018) <doi:10.1080/10618600.2017.1341324>, to compute the Rosenblatt residuals for goodness-of-fit assessment, and to predict future event times based on observed event times up to a given time. A function implementing the linear time RHawkes process likelihood approximation algorithm proposed in Stindl and Chen (2021) <doi:10.1007/s11222-021-10002-0> is also included.

This is an extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>). It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) <doi:10.1097/EDE.0000000000001643>. It also accommodates the original SAS macro and PROC CAUSALMED procedure in SAS when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.

The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.

Interface to the UNU.RAN library for Universal Non-Uniform RANdom variate generators. Thus it allows to build non-uniform random number generators from quite arbitrary distributions. In particular, it provides an algorithm for fast numerical inversion for distribution with given density function. In addition, the package contains densities, distribution functions and quantiles from a couple of distributions.

Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples. Package performance is tuned for large spatial datasets.

Random Hazard Forests (RHF) extend Random Survival Forests (RSF) by directly estimating the hazard function and by accommodating time-dependent covariates through counting-process style inputs. The package fits tree ensembles for dynamic survival prediction, returning hazard, cumulative hazard, integrated hazard, and related performance summaries for training and test data. The methods build on Random Survival Forests described by Ishwaran et al. (2008) <doi:10.1214/08-AOAS169> and on nonparametric hazard modeling with time-dependent covariates described by Lee et al. (2021) <doi:10.1214/20-AOS2028>.

This package implements the pseudo-R2D2 prior for ordinal regression from the paper "Pseudo-R2D2 prior for high-dimensional ordinal regression" by Yanchenko (2025) <doi:10.1007/s11222-025-10667-x>. In particular, it provides code to evaluate the probability distribution function for the cut-points, compute the log-likelihood, calculate the hyper-parameters for the global variance parameter, find the distribution of McFadden's coefficient-of-determination, and fit the model in rstan'. Please cite the paper if you use these codes.

Allows users to import data files containing heartbeat positions in the most broadly used formats, to remove outliers or points with unacceptable physiological values present in the time series, to plot HRV data, and to perform time domain, frequency domain and nonlinear HRV analysis. See Garcia et al. (2017) <DOI:10.1007/978-3-319-65355-6>.

This package provides functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.

Fast tools for unequal probability sampling in multi-dimensional spaces, implemented in Rust for high performance. The package offers a wide range of methods, including Sampford (Sampford, 1967, <doi:10.1093/biomet/54.3-4.499>) and correlated Poisson sampling (Bondesson and Thorburn, 2008, <doi:10.1111/j.1467-9469.2008.00596.x>), pivotal sampling (Deville and Tillé, 1998, <doi:10.1093/biomet/91.4.893>), and balanced sampling such as the cube method (Deville and Tillé, 2004, <doi:10.1093/biomet/91.4.893>) to ensure auxiliary totals are respected. Spatially balanced approaches, including the local pivotal method (Grafström et al., 2012, <doi:10.1111/j.1541-0420.2011.01699.x>), spatially correlated Poisson sampling (Grafström, 2012, <doi:10.1016/j.jspi.2011.07.003>), and locally correlated Poisson sampling (Prentius, 2024, <doi:10.1002/env.2832>), provide efficient designs when the target variable is linked to auxiliary information.

Praat <https://www.fon.hum.uva.nl/praat/> is a widely used tool for manipulating, annotating and analyzing speech and acoustic data. It stores annotation data in a format called a TextGrid'. This package provides a way to read these files into R.

This package provides a collection of tools for measuring the similarity of text messages and tracing the flow of messages over time and across media.

Rank-based, asymptotic distribution-free change-point detection for modern (high-dimensional, non-Euclidean) data, based on the graph-induced ranking scheme of Zhou and Chen (2025) <doi:10.1109/TIT.2025.3575858>. Given a rank matrix built from a pairwise similarity, the method scans for a single change-point or a changed interval using three statistics (weighted WR', max-type MR', and generalized TR') and returns analytic distribution-free p-value approximations (with an optional skewness correction) as well as optional permutation p-values.

Rasterize images using a 3D software renderer. 3D scenes are created either by importing external files, building scenes out of the included objects, or by constructing meshes manually. Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion.

This package provides functionality to read files containing observations which consist of arbitrary key/value pairs.