This package provides functions for the creation, evaluation and test of decision models based in Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities for a set of indexes, compute utilities and weights for the whole decision tree defining the decision model and simulate weights employing Dirichlet distributions under addition constraints in weights. Also includes other rating analysis methods as for example the Colley, Offensive - Defensive ratings and the ranking aggregation with Borda count.

Family Planning programs and initiatives typically use nationally representative surveys to estimate key indicators of a countryâ s family planning progress. However, in recent years, routinely collected family planning services data (Service Statistics) have been used as a supplementary data source to bridge gaps in the surveys. The use of service statistics comes with the caveat that adjustments need to be made for missing private sector contributions to the contraceptive method supply chain. Evaluating the supply source of modern contraceptives often relies on Demographic Health Surveys (DHS), where many countries do not have recent data beyond 2015/16. Fortunately, in the absence of recent surveys we can rely on statistical model-based estimates and projections to fill the knowledge gap. We present a Bayesian, hierarchical, penalized-spline model with multivariate-normal spline coefficients, to account for across method correlations, to produce country-specific,annual estimates for the proportion of modern contraceptive methods coming from the public and private sectors. This package provides a quick and convenient way for users to access the DHS modern contraceptive supply share data at national and subnational administration levels, estimate, evaluate and plot annual estimates with uncertainty for a sample of low- and middle-income countries. Methods for the estimation of method supply shares at the national level are described in Comiskey, Alkema, Cahill (2022) <arXiv:2212.03844>.

Conducting linear and nonlinear dose-response meta-regression using study-level summary data. It supports both continuous and binary outcomes and allows modeling of dose-effect relationships using linear trends or nonlinear restricted cubic splines. The package is designed to facilitate transparent, flexible, and reproducible dose-response meta-analyses, with built-in visualization of fitted dose-response curves.

Shrinks a failing R script to the smallest subset of statements that still triggers the same error, using the delta debugging algorithm of Zeller and Hildebrandt (2002) <doi:10.1109/32.988498>. Each candidate reduction is evaluated in a separate R process, so dependencies between statements and their side effects are respected. The result is a one-minimal example, in which removing any remaining statement makes the error disappear; this is the form most useful for bug reports and for questions on community forums. When no statement can be removed, because the failure is nested inside a function body, reduction continues within the surviving statements. A general delta debugging routine and a helper for reducing data frames to the rows that reproduce a failure are also provided.

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.

This package provides a fast and general method for restructuring classical longitudinal observational data into augmented transition data suitable for multi-state modeling with the msm package. Works with any longitudinal data where subjects accumulate repeated observations with start and end times and an optional terminal outcome. Methods are described in Grossetti, Ieva and Paganoni (2018) <doi:10.1007/s10729-017-9400-z>.

Build multiscalar territorial analysis based on various contexts.

Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.

This package provides multigroup Kitagawa-Blinder-Oaxaca ('mKBO') decompositions, that allow for more than two groups. Each group is compared to the sample average. For more details see Thaning and Nieuwenhuis (2025) <doi:10.31235/osf.io/6twvj_v1>.

An S4 update of the mefa package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices.

Researchers often need to calculate body-size growth rates for individuals that do not have associated age data. These growth rates are based on mark-recapture data where an individual was captured and measured at time 1 then recaptured and measured at time 2. The sizes at each time and amount of time between captures can be used to calculate growth rates. MRgrowth follows the approach in Edmonds et al. (2021) <doi:10.1371/journal.pone.0259978> and provides functions to calculate growth using three formulas, the Faben's reformulation of the von Bertalanffy formula, the Gompertz formula, and a logistic formula.

This package provides a causal mediation framework for single-cell data that incorporates two key features ('MedZIsc', pronounced Magics): (1) zero-inflation using beta regression and (2) overdispersed expression counts using negative binomial regression. This approach also includes a screening step based on penalized and marginal models to handle high-dimensionality. Full methodological details are available in our recent preprint by Ahn S et al. (2025) <doi:10.48550/arXiv.2507.06113>.

This package implements Meta Fuzzy Functions (MFFs) for regression Tak and Ucan (2026) <doi:10.1016/j.asoc.2026.114592> by aggregating predictions from multiple base learners using membership weights learned in the prediction space of validation set. The package supports fuzzy and crisp meta-ensemble structures via Fuzzy C-Means (FCM) Tak (2018) <doi:10.1016/j.asoc.2018.08.009>, Possibilistic FCM (PFCM) Tak (2021) <doi:10.1016/j.ins.2021.01.024>, Gustafsonâ Kessel (GK) clustering, and k-means, and provides a workflow to (i) generate validation/test prediction matrices from common regression learners (linear and penalized regression via glmnet', random forests, gradient boosting with xgboost and lightgbm'), (ii) fit cluster-wise meta fuzzy functions and compute membership-based weights, (iii) tune clustering-related hyperparameters (number of clusters/functions, fuzziness exponent, possibilistic regularization) via grid search on validation loss, and (iv) predict on new/test prediction matrices and evaluate performance using standard regression metrics (MAE, RMSE, MAPE, SMAPE, MSE, MedAE). This enables flexible, interpretable ensemble regression where different base models contribute to different meta components according to learned memberships.

This package provides a macro language for R programs, which provides a macro facility similar to SAS®'. This package contains basic macro capabilities like defining macro variables, executing conditional logic, and defining macro functions.

Statistical inference for quadratic functional of the moderate-dimensional linear model in Guo and Cheng (2021) <DOI:10.1080/01621459.2021.1893177>.

This package provides a set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017) <doi:10.1111/1365-2656.12674>. Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movement. Tests assess, for example, whether the shift was "significant", and whether a two-shift migration was a true return migration.

Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.

Interface to the Google Maps APIs: (1) routing directions based on the Directions API, returned as sf objects, either as single feature per alternative route, or a single feature per segment per alternative route; (2) travel distance or time matrices based on the Distance Matrix API; (3) geocoded locations based on the Geocode API, returned as sf objects, either points or bounds; (4) map images using the Maps Static API, returned as stars objects.

This package provides tools for econometric production analysis with the Symmetric Normalized Quadratic (SNQ) profit function, e.g. estimation, imposing convexity in prices, and calculating elasticities and shadow prices.

Maps physical activity from the National Health and Nutrition Examination Survey ('NHANES') study into population-based quantiles.

Perform missing value imputation for biological data using the random forest algorithm, the imputation aim to keep the original mean and standard deviation consistent after imputation.

Estimate diagnostic classification models (also called cognitive diagnostic models) with Stan'. Diagnostic classification models are confirmatory latent class models, as described by Rupp et al. (2010, ISBN: 978-1-60623-527-0). Automatically generate Stan code for the general loglinear cognitive diagnostic diagnostic model proposed by Henson et al. (2009) <doi:10.1007/s11336-008-9089-5> and other subtypes that introduce additional model constraints. Using the generated Stan code, estimate the model evaluate the model's performance using model fit indices, information criteria, and reliability metrics.

Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

This package provides a multivariate zero inflated logistic normal regression model is implemented for compositional data with zero values present. The relevant paper is Li Z., Lee K., Karagas M. R., Madan J. C., Hoen A. G., O'Malley A. J. and Li H. (2018). "Conditional regression based on a multivariate zero-inflated logistic-normal model for microbiome relative abundance data", <doi:10.1007/s12561-018-9219-2>.