Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" <doi:10.1007/BF00116466>, Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" <doi:10.1093/biostatistics/4.1.11>, Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" <doi:10.1214/17-AOAS1068>, and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" <doi:10.1016/j.sste.2021.100420>.

This package performs regularization of differential item functioning (DIF) parameters in item response theory (IRT) models (Belzak & Bauer, 2020) <https://pubmed.ncbi.nlm.nih.gov/31916799/> using a penalized expectation-maximization algorithm.

This package provides an interactive wrapper for the tmpinv() function from the rtmpinv package with options extending its functionality to pre- and post-estimation processing and streamlined incorporation of prior cell information. The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net).

Mimic the style of traditional reporting macros for clinical trials. The purpose is to generate tables, listings and figures that support clinical research. This package is well suited for firms or individuals who wish to incorporate R without changing their ways of working as it follows a traditional clinical research workflow. Invoke functions (instead of macros) to summarize data and produce formatted reports. This package differs from others in that it includes tools (wrappers) for both analyzing and reporting data.

Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, a built-in path tracer, Wavefront OBJ file export, and the ability to save 3D visualizations to a 3D printable format.

Despite the predominant use of R for data manipulation and various robust statistical calculations, in recent years, more people from various disciplines are beginning to use R for other purposes. In doing this seemlessly, further tools are needed users to easily and freely write in R for all kinds of purposes. The r2dictionary introduces a means for users to directly search for definitions of terms within the R environment.

This package provides R bindings for deck.gl', a WebGL framework for rendering large interactive spatial and tabular visualizations. The package supplies htmlwidgets and shiny bindings, supports DuckDB'-backed data hydration, and bundles the JavaScript assets needed to render deck.gl specifications from R.

Access to the C-level R date and datetime code is provided for C-level API use by other packages via registration of native functions. Client packages simply include a single header RApiDatetime.h provided by this package, and also import it. The R Core group is the original author of the code made available with slight modifications by this package.

Compiling regression results into a publishable format, conducting post-hoc hypothesis testing, and plotting moderating effects (the effect of X on Y becomes stronger/weaker as Z increases).

Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.

Read the data from Origin(R) project files ('*.opj') <https://www.originlab.com/doc/User-Guide/Origin-File-Types>. No write support is planned.

Inspired by the classic RSA', we developed the improved Generalized Reporter Score-based Analysis (GRSA) method, implemented in the R package ReporterScore', along with comprehensive visualization methods and pathway databases. GRSA is a threshold-free method that works well with all types of biomedical features, such as genes, chemical compounds, and microbial species. Importantly, the GRSA supports multi-group and longitudinal experimental designs, because of the included multi-group-compatible statistical methods.

This package provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) <doi:10.1016/S0378-1127(97)00026-1> and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) <doi:10.1016/j.ecolmodel.2015.07.010>.

Screens all .R', .Rmd', and .qmd files to extract the name of packages used in a project. This package detects packages called with library(foo)', require(foo)', foo::bar() and use("foo", "bar") and adds these dependencies in the DESCRIPTION file in the sections Depends, Imports, and Suggests.

Bayes estimation of probit choice models in cross-sectional and panel settings. The package can analyze binary, multivariate, ordered, and ranked choices, as well as heterogeneity of choice behavior among deciders. The main functionality includes model fitting via Gibbs sampling, tools for convergence diagnostic, choice data simulation, in-sample and out-of-sample choice prediction, and model selection using information criteria and Bayes factors. The latent class model extension facilitates preference-based decider classification, where the number of latent classes can be inferred via the Dirichlet process or a weight-based updating heuristic. This allows for flexible modeling of choice behavior without the need to impose structural constraints. For a reference on the method, see Oelschlaeger and Bauer (2021) <https://trid.trb.org/view/1759753>.

Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.

This package provides methods to easily build requests in the non-standard JSON schema required by the National Institute of Health (NIH)'s RePORTER Project API <https://api.reporter.nih.gov/#/Search/post_v2_projects_search>. Also retrieve and process result sets as either a ragged or flattened tibble'.

Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). The robomit package computes i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). The code is based on the psacalc command in Stata'. Additionally, robomit offers a set of sensitivity analysis and visualization functions. See Oster, E. 2019. <doi:10.1080/07350015.2016.1227711>. Additionally, see Diegert, P., Masten, M. A., & Poirier, A. (2022) for a recent discussion of the topic: <doi:10.48550/arXiv.2206.02303>.

This package provides a tidy interface to the COROS Training Hub API (<https://coros.com/traininghub>), the web platform that accompanies COROS GPS sports watches. Retrieves activities, daily wellness metrics (heart rate variability, resting heart rate, VO2 max and training load), workout programmes and training calendars. All results are returned as tibbles, ready for analysis with dplyr and ggplot2'. Both the US and EU regional endpoints are supported.

An implementation of R's DBI interface using ODBC package as a back-end. This allows R to connect to any DBMS that has a ODBC driver.

This package provides a programmatic client for the eBird database (<https://ebird.org/home>), including functions for searching for bird observations by geographic location (latitude, longitude), eBird hotspots, location identifiers, by notable sightings, by region, and by taxonomic name.

R Markdown output formats based on JavaScript libraries such as Scrollama (<https://github.com/russellsamora/scrollama>) for storytelling.

Allows one to use Osmium Tool (<https://osmcode.org/osmium-tool/>) from R. Osmium is a multipurpose command line tool that enables one to manipulate and analyze OpenStreetMap files through several different commands. Currently, this package does not aim to offer functions that cover the entire Osmium API, instead making available functions that wrap only a very limited set of its features.

This package provides a simple set of wrappers to easily use RDCOMClient for generating Microsoft PowerPoint presentations. Warning:this package is soon to be archived from CRAN.