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.

Generate decision tables and simulate operating characteristics for phase I dose-finding designs to enable objective comparison across methods. Supported designs include the traditional 3+3, Bayesian Optimal Interval (BOIN) (Liu and Yuan (2015) <doi:10.1158/1078-0432.CCR-14-1526>), modified Toxicity Probability Interval-2 (mTPI-2) (Guo et al. (2017) <doi:10.1002/sim.7185>), interval 3+3 (i3+3) (Liu et al. (2020) <doi:10.1177/0962280220939123>), and Generalized 3+3 (G3). Provides visualization tools for comparing decision rules and operating characteristics across multiple designs simultaneously.

This package implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes. See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), <doi:10.48550/arXiv.2407.10089>; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), <doi:10.48550/arXiv.2501.01324>.

Cluster functional data using phase and amplitude components of each function. By weighting phase and amplitude variation differently, clustering results can be obtained from multiple perspectives. Routines for synchronization, functional k-means clustering, and functional k-medians clustering are provided.

Easily analyze relational data from the United States 2016 federal election cycle as reported by the Federal Election Commission. This package contains data about candidates, committees, and a variety of different financial expenditures. Data is from <https://www.fec.gov/data/browse-data/?tab=bulk-data>.

Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate GENEPOP format data files and allele/haplotype frequency format files.