GLI allows you to create command-line applications in Ruby with Git-Like Interfaces: that is, they take subcommands in the style of git and gem. GLI uses a simple domain-specific language, but retains all the power of the built-in OptionParser.

Hoe is a rake/rubygems helper for project Rakefiles. It helps manage, maintain, and release projects and includes a dynamic plug-in system allowing for easy extensibility. Hoe ships with plug-ins for all the usual project tasks including rdoc generation, testing, packaging, deployment, and announcement.

This package provides a tool for multiply imputing missing data using MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; <doi:10.1017/pan.2020.49>). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with Python to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>.

Package that simulates adaptive (multi-arm, multi-stage) clinical trials using adaptive stopping, adaptive arm dropping, and/or adaptive randomisation. Developed as part of the INCEPT (Intensive Care Platform Trial) project (<https://incept.dk/>), primarily supported by a grant from Sygeforsikringen "danmark" (<https://www.sygeforsikring.dk/>).

Generates nonparametric bootstrap confidence intervals (Efron and Tibshirani, 1993: <doi:10.1201/9780429246593>) for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm().

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.

CGAL is a C++ library that aims to provide easy access to efficient and reliable algorithms in computational geometry. Since its version 4, CGAL can be used as standalone header-only library and is available under a double GPL-3|LGPL license. <https://www.cgal.org/>.

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.

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'.

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.

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.

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.

Conducts hierarchical partitioning to calculate individual contributions of each predictor towards adjusted R2 and explained deviance for generalized additive models based on output of gam()in mgcv package, applying the algorithm in this paper: Lai(2024) <doi:10.1016/j.pld.2024.06.002>.

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.

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>.

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.

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.

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'.

Designed to be compatible with the R package DBI (Database Interface) when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>. To do this the R AWS Software Development Kit ('SDK') paws <https://github.com/paws-r/paws> is used as a driver.

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>.

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.