An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) <DOI:10.1093/pan/mpt024> Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.

Low level functions for implementing maximum likelihood estimating procedures for complex models using data cloning and Bayesian Markov chain Monte Carlo methods as described in Solymos 2010 <doi:10.32614/RJ-2010-011>. Sequential and parallel MCMC support for JAGS', WinBUGS', OpenBUGS', and Stan'.

This package creates a data frame containing the metadata associated with the documentation of a collection of R packages. It allows for linking topic names to their corresponding documentation online. If you maintain a universe meta-package, it helps create a comprehensive reference for its website.

Support for measurement errors in R vectors, matrices and arrays: automatic uncertainty propagation and reporting. Documentation about errors is provided in the paper by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a vignette; see citation("errors") for details.

Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for (i) plotting EEG data, (ii) filtering EEG data, (iii) smoothing EEG data; (iv) frequency domain (Fourier) analysis of EEG data, (v) Independent Component Analysis of EEG data, and (vi) simulating event-related potential EEG data.

Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly shiny application is included.

Predict hatch and emergence timing for a wide range of wild fishes using the effective value framework (Sparks et al., (2019) <DOI:10.1139/cjfas-2017-0468>). hatchR offers users access to established phenological models and the flexibility to incorporate custom parameterizations using external datasets.

Interface to Keras <https://keras.io>, a high-level neural networks API. Keras was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

An emulator designed for rapid sequential emulation (e.g., Markov chain Monte Carlo applications). Works via extension of the laGP approach by Gramacy and Apley (2015 <doi:10.1080/10618600.2014.914442>). Details are given in Rumsey et al. (2023 <doi:10.1002/sta4.576>).

An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.

This package provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the geeM package.

This package provides functions for fitting models of trait evolution on phylogenies for continuous traits. The majority of functions are described in Thomas and Freckleton (2012) <doi:10.1111/j.2041-210X.2011.00132.x> and allow tests of variation in the rates of trait evolution.

Encodes several methods for performing Mendelian randomization analyses with summarized data. Similar to the MendelianRandomization package, but with fewer bells and whistles, and less frequent updates. As described in Yavorska (2017) <doi:10.1093/ije/dyx034> and Broadbent (2020) <doi:10.12688/wellcomeopenres.16374.2>.

Producing high-quality documents suitable for publication directly from R is made possible by the R Markdown ecosystem. memoiR makes it easy. It provides templates to knit memoirs, articles and slideshows with helpers to publish the documents on GitHub Pages and activate continuous integration.

Implement methods to determine the dynamic fit index cutoffs for network models. The package allows users to evaluate model fit based on their own model statement, model type, and sample size. Methods are described in Du and Epskamp (2026) <doi:10.31234/osf.io/5wj2y_v2>.

Efficient tools for preparation, checking and post-processing of data in PK/PD (pharmacokinetics/pharmacodynamics) modeling, with focus on use of Nonmem, including consistency, traceability, and Nonmem compatibility of Data. Rigorously checks final Nonmem datasets. Implemented in data.table', but easily integrated with base and tidyverse'.

Mainly for maximum likelihood estimation of nonparametric and semiparametric mixture models, but can also be used for fitting finite mixtures. The algorithms are developed in Wang (2007) <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010) <doi:10.1007/s11222-009-9117-z>.

R bindings to odiff', a blazing-fast pixel-by-pixel image comparison tool <https://github.com/dmtrKovalenko/odiff>. Supports PNG, JPEG, WEBP, and TIFF with configurable thresholds, antialiasing detection, and region ignoring. Requires system installation of odiff'. Ideal for visual regression testing in automated workflows.

This package implements a general framework for creating dependency graphs using projection as introduced in Fan, Feng and Xia (2019)<arXiv:1501.01617>. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance options are available to generate the graph.

Extends the S3 generic function knit_print() in knitr to automatically print some objects using an appropriate format such as Markdown or LaTeX. For example, data frames are automatically printed as tables, and the help() pages can also be rendered in knitr documents.

This package contains tests for association between a set of genetic variants and multiple correlated outcomes that are interval censored. Interval-censored data arises when the exact time of the onset of an outcome of interest is unknown but known to fall between two time points.

The Twilio web service provides an API for computer programs to interact with telephony. The included functions wrap the SMS and MMS portions of Twilio's API, allowing users to send and receive text messages from R. See <https://www.twilio.com/docs/> for more information.

This package provides a set of regular time-series datasets, describing the US electricity grid. That includes the total demand and supply, and as well as the demand by energy source (coal, solar, wind, etc.). Source: US Energy Information Administration (Dec 2019) <https://www.eia.gov/>.

Geneâ based association tests to model count data with excessive zeros and rare variants using zero-inflated Poisson/zero-inflated negative Binomial regression framework. This method was originally described by Fan, Sun, and Li in Genetic Epidemiology 46(1):73-86 <doi:10.1002/gepi.22438>.