An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available.

Supports modelling real-time case data to facilitate the real-time surveillance of infectious diseases and other point phenomena. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Both Bayesian and maximum likelihood methods are provided. Log-Gaussian Cox Processes are described by Diggle et al. (2013) <doi:10.1214/13-STS441> and we provide both the low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol et al (2023) <doi:10.1007/s11222-022-10167-2> and the nearest neighbour Gaussian process described by Datta et al (2016) <doi:10.1080/01621459.2015.1044091>. cmdstanr can be downloaded at <https://mc-stan.org/cmdstanr/>.

This package provides functions for calculating the acute chronic workload ratio using three different methods: exponentially weighted moving average (EWMA), rolling average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this methods can be found in Williams et al. (2017) <doi:10.1136/bjsports-2016-096589> for EWMA and Windt & Gabbet (2018) for RAC and RAU <doi: 10.1136/bjsports-2017-098925>.

Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.

This package produces an economic evaluation of a sample of suitable variables of cost and effectiveness / utility for two or more interventions, e.g. from a Bayesian model in the form of MCMC simulations. This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis, see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.

Implementing the computational phase of the Causes of Outcome Learning approach as described in Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <doi:10.1093/ije/dyac078>. The optional ggtree package can be obtained through Bioconductor.

Collection of functions for fitting and interpreting distributed lag interaction models (DLIM). A DLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a continuous variable. Includes a dlim() function for fitting, predict() function for inference, and plotting functions for visualization. Details on methodology are described in Demateis et al. (2024) <doi:10.1002/env.2843>.

The framework provides functions to generate ODEs of reaction networks, parameter transformations, observation functions, residual functions, etc. The framework follows the paradigm that derivative information should be used for optimization whenever possible. Therefore, all major functions produce and can handle expressions for symbolic derivatives. The methods used in dMod were published in Kaschek et al, 2019, <doi:10.18637/jss.v088.i10>.

An easy-to-use implementation of the Engle-Granger two-step procedure for identifying pairs of cointegrated series. It is geared towards the analysis of pairs of securities. Summary and plot functions are provided, and the package is able to fetch closing prices of securities from Yahoo. A variety of unit root tests are supported, and an improved unit root test is included.

Computes empirical Bayes confidence estimators and confidence intervals in a normal means model. The intervals are robust in the sense that they achieve correct coverage regardless of the distribution of the means. If the means are treated as fixed, the intervals have an average coverage guarantee. The implementation is based on Armstrong, Kolesár and Plagborg-Møller (2020) <arXiv:2004.03448>.

This package provides a tool which allows users to create and evaluate ensembles of species distribution model (SDM) predictions. Functionality is offered through R functions or a GUI (R Shiny app). This tool can assist users in identifying spatial uncertainties and making informed conservation and management decisions. The package is further described in Woodman et al (2019) <doi:10.1111/2041-210X.13283>.

This package implements the statistic FAVA, an Fst-based Assessment of Variability across vectors of relative Abundances, as well as a suite of helper functions which enable the visualization and statistical analysis of relative abundance data. The FAVA R package accompanies the paper, â Quantifying compositional variability in microbial communities with FAVAâ by Morrison, Xue, and Rosenberg (2025) <doi:10.1073/pnas.2413211122>.

This package provides a statistical hypothesis test for conditional independence. Given residuals from a sufficiently powerful regression, it tests whether the covariance of the residuals is vanishing. It can be applied to both discretely-observed functional data and multivariate data. Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas Peters (2022) <doi:10.1111/rssb.12544>.

Addresses the log of zero by developing a new family of estimators called iterated Ordinary Least Squares. This family nests standard approaches such as log-linear and Poisson regressions, offers several computational advantages, and corresponds to the correct way to perform the popular log(Y + 1) transformation. For more details about how to use it, see the notebook at: <https://www.davidbenatia.com/>.

Support for the analyst in a Multicriteria Decision Aiding (MCDA) process with algorithms, preference elicitation and data visualisation functions. Sébastien Bigaret, Richard Hodgett, Patrick Meyer, Tatyana Mironova, Alexandru Olteanu (2017) Supporting the multi-criteria decision aiding process : R and the MCDA package, Euro Journal On Decision Processes, Volume 5, Issue 1 - 4, pages 169 - 194 <doi:10.1007/s40070-017-0064-1>.

This package implements the efficient algorithm by Ortmann and Brandes (2017) <doi:10.1007/s41109-017-0027-2> to compute the orbit-aware frequency distribution of induced and non-induced quads, i.e. subgraphs of size four. Given an edge matrix, data frame, or a graph object (e.g., igraph'), the orbit-aware counts are computed respective each of the edges and nodes.

This package provides tools for performing disproportionality analysis using the information component, proportional reporting rate and the reporting odds ratio. The anticipated use is passing data to the da() function, which executes the disproportionality analysis. See Norén et al (2011) <doi:10.1177/0962280211403604> and Montastruc et al (2011) <doi:10.1111/j.1365-2125.2011.04037.x> for further details.

This package provides a wrapper around the generic coordinate transformation software PROJ that transforms coordinates from one coordinate reference system ('CRS') to another. This includes cartographic projections as well as geodetic transformations. The intention is for this package to be used by user-packages such as reproj', and that the older PROJ.4 and version 5 pathways be provided by the proj4 package.

SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.

This package provides coroutines for R, a family of functions that can be suspended and resumed later on. This includes async functions (which await) and generators (which yield). Async functions are based on the concurrency framework of the promises package. Generators are based on a dependency free iteration protocol defined in coro and are compatible with iterators from the reticulate package.

This package provides a set of functions for sparse matrix algebra. Differences with other sparse matrix packages are:

1. it only supports (essentially) one sparse matrix format;

2. it is based on transparent and simple structure(s);

3. it is tailored for MCMC calculations within G(M)RF;

4. and it is fast and scalable (with the extension package spam64).

This package helps you with creation and use of R repositories via helper functions to insert packages into a repository, and to add repository information to the current R session. Two primary types of repositories are supported: gh-pages at GitHub, as well as local repositories on either the same machine or a local network. Drat is a recursive acronym: Drat R Archive Template.

This package provides a system for describing and manipulating the many models that are generated in causal inference and data analysis projects, as based on the causal theory and criteria of Austin Bradford Hill (1965) <doi:10.1177/003591576505800503>. This system includes the addition of formal attributes that modify base `R` objects, including terms and formulas, with a focus on variable roles in the "do-calculus" of modeling, as described in Pearl (2010) <doi:10.2202/1557-4679.1203>. For example, the definition of exposure, outcome, and interaction are implicit in the roles variables take in a formula. These premises allow for a more fluent modeling approach focusing on variable relationships, and assessing effect modification, as described by VanderWeele and Robins (2007) <doi:10.1097/EDE.0b013e318127181b>. The essential goal is to help contextualize formulas and models in causality-oriented workflows.

Google's Compact Language Detector 3 is a neural network model for language identification and the successor of cld2 (available from CRAN). The algorithm is still experimental and takes a novel approach to language detection with different properties and outcomes. It can be useful to combine this with the Bayesian classifier results from cld2'. See <https://github.com/google/cld3#readme> for more information.