This package provides a collection of functions for computing fairness metrics for machine learning and statistical models, including confidence intervals for each metric. The package supports the evaluation of group-level fairness criterion commonly used in fairness research, particularly in healthcare for binary protected attributes. It is based on the overview of fairness in machine learning written by Gao et al (2025) <doi:10.1002/sim.70234>.

Quantifies the provenance of sediments by applying a mixing model algorithm to end sediment mixtures based on a comprehensive characterization of the sediment sources. The fingerPro model builds upon the foundational concept of using mass balance linear equations for sediment source quantification by incorporating several distinct technical advancements. It employs an optimization approach to normalize discrepancies in tracer ranges and minimize the objective function. Latin hypercube sampling is used to explore all possible combinations of source contributions (0-100%), mitigating the risk of local minima. Uncertainty in source estimates is quantified through a Monte Carlo routine, and the model includes additional metrics, such as the normalized error of the virtual mixture, to detect mathematical inconsistencies, non-physical solutions, and biases. A new linear variability propagation (LVP) method is also included to address and quantify potential bias in model outcomes, particularly when dealing with dominant or non-contributing sources and high source variability, offering a significant advancement for field studies where direct comparison with theoretical apportionments is not feasible. In addition to the unmixing model, a complete framework for tracer selection is included. Several methods are implemented to evaluate tracer behaviour by considering both source and mixture information. These include the Consistent Tracer Selection (CTS) method to explore all tracer combinations and select the optimal ones improving the robustness and interpretability of the model results. A Conservative Balance (CB) method is also incorporated to enable the use of isotopic tracers. The package also provides several graphical tools to support data exploration and interpretation, including box plots, correlation plots, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA).

This package implements the Fixed Effect Jackknife Instrumental Variables ('FEJIV') estimator of Chao, Swanson, and Woutersen (2023) <doi:10.1016/j.jeconom.2022.12.011>, allowing consistent IV estimation with many (possibly weak) instruments, cluster fixed effects, heteroskedastic errors, and many exogenous covariates. The estimator is recommended by SÅ oczyÅ ski (2024) <doi:10.48550/arXiv.2011.06695> as an alternative to two-stage least squares when estimating the interacted specification of Angrist and Imbens (1995) <doi:10.1080/01621459.1995.10476535>.

Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance. A three-rectangle approximation method of a p-value histogram is proposed to derive a formula to compute the statistical power for analyses that involve the FDR. The methodology paper of this package is under review.

Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the ForLion algorithm and EW ForLion algorithm. The algorithms searches for locally optimal designs and EW optimal designs under the D-criterion. See Huang, Y., Li, K., Mandal, A., & Yang, J., (2024) <doi:10.1007/s11222-024-10465-x> and Lin, S., Huang, Y., & Yang, J. (2025) <doi:10.48550/arXiv.2505.00629>.

This package provides implementation of statistical methods for random objects lying in various metric spaces, which are not necessarily linear spaces. The core of this package is Fréchet regression for random objects with Euclidean predictors, which allows one to perform regression analysis for non-Euclidean responses under some mild conditions. Examples include distributions in 2-Wasserstein space, covariance matrices endowed with power metric (with Frobenius metric as a special case), Cholesky and log-Cholesky metrics, spherical data. References: Petersen, A., & Müller, H.-G. (2019) <doi:10.1214/17-AOS1624>.

To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to easily import, filter, and modify ForestGEO data. To learn more about ForestGEO visit <https://forestgeo.si.edu/>.

Flow of funds are financial accounts that are provided by Federal Reserve quarterly. The package contains all datasets <https://www.federalreserve.gov/datadownload/Choose.aspx?rel=z1>, tables <https://www.federalreserve.gov/apps/fof/FOFTables.aspx> and descriptions <https://www.federalreserve.gov/apps/fof/Guide/z1_tables_description.pdf> with functions to understand series <https://www.federalreserve.gov/apps/fof/SeriesStructure.aspx> and explore them.

An implementation of multiple regression models for count data. These include various forms of the negative binomial (NB-1, NB-2, NB-P, generalized negative binomial, etc.), Poisson-Lognormal, other compound Poisson distributions, the Generalized Waring model, etc. Information on the different forms of the negative binomial are described by Greene (2008) <doi:10.1016/j.econlet.2007.10.015>. For treatises on count models, see Cameron and Trivedi (2013) <doi:10.1017/CBO9781139013567> and Hilbe (2012) <doi:10.1017/CBO9780511973420>. For the implementation of under-reporting in count models, see Wood et al. (2016) <doi:10.1016/j.aap.2016.06.013>. For prediction methods in random parameter models, see Wood and Gayah (2025) <doi:10.1016/j.aap.2025.108147>. For estimating random parameters using maximum simulated likelihood, see Greene and Hill (2010) <doi:10.1108/S0731-9053(2010)26>; Gourieroux and Monfort (1996) <doi:10.1093/0198774753.001.0001>; or Hensher et al. (2015) <doi:10.1017/CBO9781316136232>.

Linear cross-section factor model fitting with least-squares and robust fitting the lmrobdetMM() function from RobStatTM'; related volatility, Value at Risk and Expected Shortfall risk and performance attribution (factor-contributed vs idiosyncratic returns); tabular displays of risk and performance reports; factor model Monte Carlo. The package authors would like to thank Chicago Research on Security Prices,LLC for the cross-section of about 300 CRSP stocks data (in the data.table object stocksCRSP', and S&P GLOBAL MARKET INTELLIGENCE for contributing 14 factor scores (a.k.a "alpha factors".and "factor exposures") fundamental data on the 300 companies in the data.table object factorSPGMI'. The stocksCRSP and factorsSPGMI data are not covered by the GPL-2 license, are not provided as open source of any kind, and they are not to be redistributed in any form.

Implementation of the fast univariate inference approach (Cui et al. (2022) <doi:10.1080/10618600.2021.1950006>, Loewinger et al. (2024) <doi:10.7554/eLife.95802.2>, Xin et al. (2025) <doi:10.7554/eLife.109428.1>) for fitting functional mixed models. User guides and Python package information can be found at <https://github.com/gloewing/photometry_FLMM>.

Allows the user to implement easily canvas elements within a shiny app or an RMarkdown document. The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes. The package relies on the fabricjs JavaScript library. See <http://fabricjs.com/>.

This package provides methods for performing fMRI quality assurance (QA) measurements of test objects. Heavily based on the fBIRN procedures detailed by Friedman and Glover (2006) <doi:10.1002/jmri.20583>.

Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from <http://sourceforge.net/projects/formscanner/>. The spreadsheet file created by FormScanner is imported in a convenient format to perform the analyses provided by the package. These analyses include the conversion of multiple responses to binary (correct/incorrect) data, the computation of the number of corrected responses for each subject or item, scoring using weights,the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of a few subjects.

Probability distributions that are sometimes useful in hydrology.

Easily use Font Awesome icons as shiny favicons (the icons that appear on browser tabs). Font Awesome (<https://fontawesome.com/>) is a popular set of icons that can be used in web pages. favawesome provides a simple way to use these icons as favicons in shiny applications and other HTML pages.

Interval estimation of the population allele frequency from qPCR analysis based on the restriction enzyme digestion (RED)-DeltaDeltaCq method (Osakabe et al. 2017, <doi:10.1016/j.pestbp.2017.04.003>), as well as general DeltaDeltaCq analysis. Compatible with the Cq measurement of DNA extracted from multiple individuals at once, so called "group-testing", this model assumes that the quantity of DNA extracted from an individual organism follows a gamma distribution. Therefore, the point estimate is robust regarding the uncertainty of the DNA yield.

Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace ImageJ (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) <doi:10.1529/biophysj.103.026765>; Day, Charles A. (2012) <doi:10.1002/0471142956.cy0219s62>.

Uses three different correlation coefficients to calculate measurement-level adequate correlations in a feature matrix: Pearson product-moment correlation coefficient, Intraclass correlation and Cramer's V.

Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.

Fuzzy set ordination is a multivariate analysis used in ecology to relate the composition of samples to possible explanatory variables. While differing in theory and method, in practice, the use is similar to constrained ordination. The package contains plotting and summary functions as well as the analyses.

These functions were developed to support statistical analysis on functional covariance operators. The package contains functions to: - compute 2-Wasserstein distances between Gaussian Processes as in Masarotto, Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>; - compute the Wasserstein barycenter (Frechet mean) as in Masarotto, Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>; - perform analysis of variance testing procedures for functional covariances and tangent space principal component analysis of covariance operators as in Masarotto, Panaretos & Zemel (2022) <arXiv:2212.04797>. - perform a soft-clustering based on the Wasserstein distance where functional data are classified based on their covariance structure as in Masarotto & Masarotto (2023) <doi:10.1111/sjos.12692>.

This package provides optimized, Tidyverse-compatible functions for calculating the Fragility Index and Reverse Fragility Index for 2x2 contingency tables from clinical trials. Uses customized hypergeometric and algebraic calculations along with binary search algorithms to achieve substantial speedups over standard implementations, with seamless integration into dplyr pipelines.

Streamlines the process of updating changelogs (NEWS.md) and versioning R packages developed in git repositories.