This package implements the Oaxaca-Blinder decomposition method and generalizations of it that decompose differences in distributional statistics beyond the mean. The function ob_decompose() decomposes differences in the mean outcome between two groups into one part explained by different covariates (composition effect) and into another part due to differences in the way covariates are linked to the outcome variable (structure effect). The function further divides the two effects into the contribution of each covariate and allows for weighted doubly robust decompositions. For distributional statistics beyond the mean, the function performs the recentered influence function (RIF) decomposition proposed by Firpo, Fortin, and Lemieux (2018). The function dfl_decompose() divides differences in distributional statistics into an composition effect and a structure effect using inverse probability weighting as introduced by DiNardo, Fortin, and Lemieux (1996). The function also allows to sequentially decompose the composition effect into the contribution of single covariates. References: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2018) <doi:10.3390/econometrics6020028>. "Decomposing Wage Distributions Using Recentered Influence Function Regressions." Fortin, Nicole M., Thomas Lemieux, and Sergio Firpo. (2011) <doi:10.3386/w16045>. "Decomposition Methods in Economics." DiNardo, John, Nicole M. Fortin, and Thomas Lemieux. (1996) <doi:10.2307/2171954>. "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach." Oaxaca, Ronald. (1973) <doi:10.2307/2525981>. "Male-Female Wage Differentials in Urban Labor Markets." Blinder, Alan S. (1973) <doi:10.2307/144855>. "Wage Discrimination: Reduced Form and Structural Estimates.".

This package provides a foreach parallel adapter for parabar backends. This package offers a minimal implementation of the %dopar% operator, enabling users to run foreach loops in parallel, leveraging the parallel and progress-tracking capabilities of the parabar package. Learn more about parabar and doParabar at <https://parabar.mihaiconstantin.com>.

This package provides a full definition for Weibull tails and Full-Tails Gamma and tools for fitting these distributions to empirical tails. This package build upon the paper by del Castillo, Joan & Daoudi, Jalila & Serra, Isabel. (2012) <doi:10.1017/asb.2017.9>.

This package provides a set of functions to perform Raju, van der Linden and Fleer's (1995, <doi:10.1177/014662169501900405>) Differential Functioning of Items and Tests (DFIT) analyses. It includes functions to use the Monte Carlo Item Parameter Replication approach (Oshima, Raju, & Nanda, 2006, <doi:10.1111/j.1745-3984.2006.00001.x>) for obtaining the associated statistical significance tests cut-off points. They may also be used for a priori and post-hoc power calculations (Cervantes, 2017, <doi:10.18637/jss.v076.i05>).

This package provides functions for (1) ranking, selecting, and prioritising genes, proteins, and metabolites from high dimensional biology experiments, (2) multivariate hit calling in high content screens, and (3) combining data from diverse sources.

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.

This package provides tools to compute directly age-standardised rates using the 2013 European Standard Population. Includes variance estimation and 95% confidence intervals for population health applications. Functions are flexible to handle any grouping variable and age bands, allowing reproducible and automated analyses.

Estimation and testing methods for dependently truncated data. Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>, and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>. Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>. A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>. Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.

This package provides an easy to use implementation of life expectancy decomposition formulas for age bands, derived from Ponnapalli, K. (2005). A comparison of different methods for decomposition of changes in expectation of life at birth and differentials in life expectancy at birth. Demographic Research, 12, pp.141â 172. <doi:10.4054/demres.2005.12.7> In addition, there is a decomposition function for disease cause breakdown and a couple helpful plot functions.

The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.

Gaussian mixture modeling of one- and two-dimensional data, provided in original or binned form, with an option to estimate the number of model components. The method uses Gaussian Mixture Models (GMM) with initial parameters determined by a dynamic programming algorithm, leading to stable and reproducible model fitting. For more details see Zyla, J., Szumala, K., Polanski, A., Polanska, J., & Marczyk, M. (2026) <doi:10.1016/j.jocs.2026.102811>.

This package provides functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Contains data restructuring functions and functions for generating biometrically informed data for kin pairs. See [Garrison and Rodgers, 2016 <doi:10.1016/j.intell.2016.08.008>], [Sims, Trattner, and Garrison, 2024 <doi:10.3389/fpsyg.2024.1430978>] for empirical examples, and [Garrison and colleagues for theoretical work <doi:10.1101/2025.08.25.25334395>].

Implement download buttons in HTML output from rmarkdown without the need for runtime:shiny'.

We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. This package implements a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. This can be used for a variety of statistical problems involving absolute quantification under uncertainty. See Comoglio et al. (2013) <doi:10.1371/journal.pone.0074388>.

An R interface to the Free Dictionary API <https://dictionaryapi.dev/>, <https://github.com/meetDeveloper/freeDictionaryAPI>. Retrieve dictionary definitions for English words, as well as additional information including phonetics, part of speech, origins, audio pronunciation, example usage, synonyms and antonyms, returned in tidy format for ease of use.

Implementation of the double/debiased machine learning framework of Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear regression models, partially linear instrumental variable regression models, interactive regression models and interactive instrumental variable regression models. DoubleML allows estimation of the nuisance parts in these models by machine learning methods and computation of the Neyman orthogonal score functions. DoubleML is built on top of mlr3 and the mlr3 ecosystem. The object-oriented implementation of DoubleML based on the R6 package is very flexible. More information available in the publication in the Journal of Statistical Software: <doi:10.18637/jss.v108.i03>.

Duplicated data can exist in different rows and columns and user may need to treat observations (rows) connected by duplicated data as one observation, e.g. companies can belong to one family (and thus: be one company) by sharing some telephone numbers. This package allows to find connected rows based on data on chosen columns and collapse it into one row.

Detection of differential item functioning (DIF) among dichotomously scored items and differential distractor functioning (DDF) among unscored items with non-linear regression procedures based on generalized logistic regression models (Hladka & Martinkova, 2020, <doi:10.32614/RJ-2020-014>).

This package provides methods by Steinhauser et al. (2016) <DOI:10.1186/s12874-016-0196-1> for meta-analysis of diagnostic accuracy studies with several cutpoints.

This package provides a flexible container to transport and manipulate complex sets of data. These data may consist of multiple data files and associated meta data and ancillary files. Individual data objects have associated system level meta data, and data files are linked together using the OAI-ORE standard resource map which describes the relationships between the files. The OAI- ORE standard is described at <https://www.openarchives.org/ore/>. Data packages can be serialized and transported as structured files that have been created following the BagIt specification. The BagIt specification is described at <https://datatracker.ietf.org/doc/html/draft-kunze-bagit-08>.

This package implements distributional cost-effectiveness analysis (DCEA) as described in Cookson et al. (2020, ISBN:9780198838197) and the methods endorsed by NICE (2025) for health technology evaluation. Provides functions for both aggregate and full-form DCEA, inequality measurement (Atkinson index, Gini coefficient, slope index of inequality, relative index of inequality), social welfare function evaluation, equity-efficiency impact plane visualisation, and sensitivity analysis over inequality aversion parameters. Includes baseline health distributions for England (by IMD quintile), Canada (income quintile), and global WHO regions. Suitable for academic research, health technology assessment submissions, and public health policy analysis.

This package provides functions for computing the density, distribution, and random generation of the Decision Diffusion model (DDM), a widely used cognitive model for analysing choice and response time data. The package allows model specification, including the ability to fix, constrain, or vary parameters across experimental conditions. While it does not include a built-in optimiser, it supports likelihood evaluation and can be integrated with external tools for parameter estimation. Functions for simulating synthetic datasets are also provided. This package is intended for researchers modelling speeded decision-making in behavioural and cognitive experiments. For more information, see Voss, Rothermund, and Voss (2004) <doi:10.3758/BF03196893>, Voss and Voss (2007) <doi:10.3758/BF03192967>, and Ratcliff and McKoon (2008) <doi:10.1162/neco.2008.12-06-420>.

This package provides a comprehensive visualization toolkit built with coders of all skill levels and color-vision impaired audiences in mind. It allows creation of finely-tuned, publication-quality figures from single function calls. Visualizations include scatter plots, compositional bar plots, violin, box, and ridge plots, and more. Customization ranges from size and title adjustments to discrete-group circling and labeling, hidden data overlay upon cursor hovering via ggplotly() conversion, and many more, all with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors().

This package provides tools for constructing and auditing longitudinal decision paths from panel data. Implements a decision infrastructure framework for representing institutional AI systems as generators of time-ordered binary decision sequences. Provides functions to build path objects from panel data, summarise per-unit descriptors (dosage, switching rate, onset, duration, longest run), compute the Decision Reliability Index (DRI) following Cronbach (1951) <doi:10.1007/BF02310555>, estimate Shannon decision-path entropy following Shannon (1948) <doi:10.1002/j.1538-7305.1948.tb01338.x>, classify systems by infrastructure type (static, periodic, continuous, human-in-the-loop), and evaluate subgroup disparities in decision exposure and stability. Applications include education, policy, health, and organisational research.