Fast functions for effective sample size, weighted multivariate mean, variance, and quantile computation, and weight diagnostic plot for generic importance sampling type or other probability weighted samples.

Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). DiSTATIS and CovSTATIS are used to analyze multiple distance/covariance matrices collected on the same set of observations. These methods are based on Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012) <doi:10.1002/wics.198>.

Evaluate the presence of disposition effect and others irrational investor's behaviors based solely on investor's transactions and financial market data. Experimental data can also be used to perform the analysis. Four different methodologies are implemented to account for the different nature of human behaviors on financial markets. Novel analyses such as portfolio driven and time series disposition effect are also allowed.

Easily create descriptive and comparative tables. It makes use and integrates directly with the tidyverse family of packages, and pipes. Tables are produced as (nested) dataframes for easy manipulation.

This package performs detection of Differential Item Functioning using the method DIFboost as proposed by Schauberger and Tutz (2016) <doi:10.1111/bmsp.12060>.

This package provides tools to help the design and analysis of resilient non-inferiority trials. These include functions for sample size calculations and analyses of trials, with either a risk difference, risk ratio or arc-sine difference margin, and a function to run simulations to design a trial with the methods described in Quartagno et al. (2019) <arXiv:1905.00241>.

This package contains functions to validate administrative register as CPF (Cadastro de Pessoa Fisica), CNPJ (Cadastro de Pessoa Juridica), PIS (Programa de Integracao Social), CNES (Cadastro Nacional de Saude). Builds from and improves on previous package from IPEA validaRA <https://github.com/ipea/validaRA>. It can check individual registers or help creating a table summarizing validity of a set.

Fits Bayesian copula vector autoregressive models for bivariate time series with dynamic, regime-switching, and constant dependence structures. The package includes simulation, data preparation, estimation with Stan through rstan or cmdstanr', posterior summaries, diagnostics, trajectory extraction, fitted and predictive summaries, and approximate leave-one-out cross-validation model comparison for supported fits. For Bayesian computation and model comparison, see Carpenter et al. (2017) <doi:10.18637/jss.v076.i01> and Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>.

Simplifies and automates the process of exploring and merging data from relational databases. This package allows users to discover table relationships, create a map of all possible joins, and generate executable plans to merge data based on a structured metadata framework.

Distributed Online Mean Tests is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform mean tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, Domean ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for mean testing. The philosophy of Domean is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.

Fits constrained and unrestricted meta-analytic location-scale models in which residual between-study heterogeneity is modeled as an exponential function of a prespecified design-robustness score. The package supports maximum-likelihood (ML) and restricted maximum-likelihood (REML) estimation, location moderators, the conventional random-effects model as a nested special case, exact estimation at the nonnegative scale-gradient boundary, design-indexed heterogeneity summaries, scale-attenuation measures, prediction of fitted heterogeneity, leave-one-out influence diagnostics, and parametric-bootstrap inference for the scale gradient. Because the scale-gradient null lies on the boundary of the constrained parameter space, standard chi-square likelihood-ratio references do not apply (Self and Liang, 1987, <doi:10.1080/01621459.1987.10478472>). The general location-scale parent model is described in Viechtbauer and Lopez-Lopez (2022, <doi:10.1002/jrsm.1562>). A scale model reweights studies and does not adjust the mean for design-linked bias.

Create a details HTML tag around R objects to place in a Markdown, Rmarkdown and roxygen2 documentation.

Quality control and formatting tools developed for the Copernicus Data Rescue Service. The package includes functions to handle the Station Exchange Format (SEF), various statistical tests for climate data at daily and sub-daily resolution, as well as functions to plot the data. For more information and documentation see <https://datarescue.climate.copernicus.eu/st_data-quality-control>.

Attempt to repair inconsistencies and missing values in data records by using information from valid values and validation rules restricting the data.

Implementation of the Dirichlet Random Forest algorithm for compositional response data. Trees are grown using a Dirichlet log-likelihood splitting criterion, with maximum likelihood ('MLE') and method-of-moments ('MOM') parameter estimation. Provides averaging-based predictions (average of responses within terminal nodes), parameter-based predictions (expected value derived from the estimated Dirichlet parameters within terminal nodes), and distributional predictions represented as a weighted distribution over the training responses. Out-of-bag estimation and impurity- and permutation-based variable importance are also supported. For more details see Masoumifard, van der Westhuizen, and Gardner-Lubbe (2026, ISBN:9781032903910).

Deep compositional spatial models are standard spatial covariance models coupled with an injective warping function of the spatial domain. The warping function is constructed through a composition of multiple elemental injective functions in a deep-learning framework. The package implements two cases for the univariate setting; first, when these warping functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. In the multivariate setting only the former case is available. Estimation and inference is done using `tensorflow`, which makes use of graphics processing units. For more details see Zammit-Mangion et al. (2022) <doi:10.1080/01621459.2021.1887741>, Vu et al. (2022) <doi:10.5705/ss.202020.0156>, Vu et al. (2023) <doi:10.1016/j.spasta.2023.100742>, and Shao et al. (2025) <doi:10.48550/arXiv.2505.12548>.

Decorrelates a set of summary statistics (i.e., Z-scores or P-values per SNP) via Decorrelation by Orthogonal Transformation (DOT) approach and performs gene-set analyses by combining transformed statistic values; operations are performed with algorithms that rely only on the association summary results and the linkage disequilibrium (LD). For more details on DOT and its power, see Olga (2020) <doi:10.1371/journal.pcbi.1007819>.

Parse, format, and validate international phone numbers using Google's libphonenumber java library, <https://github.com/google/libphonenumber>.

This package provides the ability to display something analogous to Python's docstrings within R. By allowing the user to document their functions as comments at the beginning of their function without requiring putting the function into a package we allow more users to easily provide documentation for their functions. The documentation can be viewed just like any other help files for functions provided by packages as well.

Based on random forest principle, DynForest is able to include multiple longitudinal predictors to provide individual predictions. Longitudinal predictors are modeled through the random forest. The methodology is fully described for a survival outcome in: Devaux, Helmer, Genuer & Proust-Lima (2023) <doi: 10.1177/09622802231206477>.

Distributed estimation method is based on a Laplace factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.

Estimation of Difference-in-Differences (DiD) estimators from de Chaisemartin et al. (2025) <doi:10.48550/arXiv.2405.04465> in Heterogeneous Adoption Designs with Quasi Untreated Groups.

Secant acceleration applied to derivative-free Spectral Residual Methods for solving large-scale nonlinear systems of equations. The main reference follows: E. G. Birgin and J. M. Martinez (2022) <doi:10.1137/20M1388024>.

Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model.