This package provides a verity of summary tables of the Covid19 cases in San Francisco. Data source: San Francisco, Department of Public Health - Population Health Division <https://datasf.org/opendata/>.

The COSSO regularization method automatically estimates and selects important function components by a soft-thresholding penalty in the context of smoothing spline ANOVA models. Implemented models include mean regression, quantile regression, logistic regression and the Cox regression models.

Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models, with and without interaction terms. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) <DOI:10.1186/s12859-017-1863-x> and the package vignette.

Estimates tree crown scorch from terrestrial lidar scans collected with a RIEGL vz400i. The methods follow those described in Cannon et al. (2025, Fire Ecology 21:71, <doi:10.1186/s42408-025-00420-0>).

Visualizes results of item analysis such as item difficulty, item discrimination, and coefficient alpha for ease of result communication.

The cyclotomic numbers are complex numbers that can be thought of as the rational numbers extended with the roots of unity. They are represented exactly, enabling exact computations. They contain the Gaussian rationals (complex numbers with rational real and imaginary parts) as well as the square roots of all rational numbers. They also contain the sine and cosine of all rational multiples of pi. The algorithms implemented in this package are taken from the Haskell package cyclotomic', whose algorithms are adapted from code by Martin Schoenert and Thomas Breuer in the GAP project (<https://www.gap-system.org/>). Cyclotomic numbers have applications in number theory, algebraic geometry, algebraic number theory, coding theory, and in the theory of graphs and combinatorics. They have connections to the theory of modular functions and modular curves.

This package provides functions to perform statistical inference of data organized in contingency tables. This package is a companion to the "Statistical Analysis of Contingency Tables" book by Fagerland et al. <ISBN 9781466588172>.

Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. Available are statistical methods in Huang, Wang and Emura (2020, JJSD accepted).

This package performs forward and backward stepwise regression for the proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events. This version includes improved handling of factors, interactions, and polynomial terms.

This package provides equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function.

Management and analysis of camera trap wildlife data through an integrated workflow. Provides functions for image/video organization and metadata extraction, species/individual identification. Creates detection histories for occupancy and spatial capture-recapture analyses, with support for multi-season studies. Includes tools for fitting community occupancy models in JAGS and NIMBLE, and an interactive dashboard for survey data visualization and analysis. Features visualization of species distributions and activity patterns, plus export capabilities for GIS and reports. Emphasizes automation and reproducibility while maintaining flexibility for different study designs.

Uses optimal transport distances to find probabilistic matching estimators for causal inference. These methods are described in Dunipace, Eric (2021) <arXiv:2109.01991>. The package will build the weights, estimate treatment effects, and calculate confidence intervals via the methods described in the paper. The package also supports several other methods as described in the help files.

Execute Nonlinear Mixed Effects (NLME) models for pharmacometrics using a shiny interface. Specify engine parameters and select from different run options, including simple estimation, stepwise covariate search, bootstrapping, simulation, visual predictive check, and more. Models are executed using the Certara.RsNLME package.

Modeling under- and over-dispersed count data using extended Poisson process models as in the article Faddy and Smith (2011) <doi:10.18637/jss.v069.i06> .

Statistical tests for the comparison between two correlations based on either independent or dependent groups. Dependent correlations can either be overlapping or nonoverlapping. A web interface is available on the website <http://comparingcorrelations.org>. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from <https://rkward.kde.org> to use this feature. The respective R package rkward cannot be installed directly from a repository, as it is a part of RKWard.

Integrative context-dependent clustering for heterogeneous biomedical datasets. Identifies local clustering structures in related datasets, and a global clusters that exist across the datasets.

This package provides a clustered random forest algorithm for fitting random forests for data of independent clusters, that exhibit within cluster dependence. Details of the method can be found in Young and Buehlmann (2025) <doi:10.48550/arXiv.2503.12634>.

This package implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a normal heterogeneity distribution. The algorithm uses a hybrid Gibbs Sampler with a random walk metropolis step for the MNL coefficients for each unit. Dependent variable may be discrete or continuous. Independent variables may be discrete or continuous with optional order constraints. Means of the distribution of heterogeneity can optionally be modeled as a linear function of unit characteristics variables.

Useful libraries for building a Java based GUI under R are provided.

Computerized tomography (CT) can be used to assess certain wood properties when wood disks or logs are scanned. Wood density profiles (i.e. variations of wood density from pith to bark) can yield important information used for studies in forest resource assessment, wood quality and dendrochronology studies. The first step consists in transforming grey values from the scan images to density values. The packages then proposes a unique method to automatically locate the pith by combining an adapted Hough Transform method and a one-dimensional edge detector. Tree ring profiles (average ring density, earlywood and latewood density, ring width and percent latewood for each ring) are then obtained.

It helps in development of a principal component analysis based composite index by assigning weights to variables and combining the weighted variables. For method details see Sendhil, R., Jha, A., Kumar, A. and Singh, S. (2018). <doi:10.1016/j.ecolind.2018.02.053>, and Wu, T. (2021). <doi:10.1016/j.ecolind.2021.108006>.

Load Current Population Survey (CPS) microdata into R using the Census Bureau Data API (<https://www.census.gov/data/developers/data-sets.html>), including basic monthly CPS and CPS ASEC microdata.

Enables creation of visualizations using the CanvasXpress framework in R. CanvasXpress is a standalone JavaScript library for reproducible research with complete tracking of data and end-user modifications stored in a single PNG image that can be played back. See <https://www.canvasxpress.org> for more information.

This comprehensive framework for periodic time series modeling is designated as "CLIC" (The LIC for Distributed Cosine Regression Analysis) analysis. It is predicated on the assumption that the underlying data exhibits complex periodic structures beyond simple harmonic components. The philosophy of the method is articulated in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.