This package provides methods for extracting results from mixed-effect model objects fit with the lme4 package. Allows construction of prediction intervals efficiently from large scale linear and generalized linear mixed-effects models. This method draws from the simulation framework used in the Gelman and Hill (2007) textbook: Data Analysis Using Regression and Multilevel/Hierarchical Models.

Multivariate version of the two-sample Gehan and logrank tests, as described in L.J Wei & J.M Lachin (1984) and Persson et al. (2019).

This package provides a set of classes and methods to set up and run multi-species, trait based and community size spectrum ecological models, focused on the marine environment.

This package provides functions to access drug regulatory data from public RESTful APIs including the FDA Open API and the Health Canada Drug Product Database API', retrieving real-time or historical information on drug approvals, adverse events, recalls, and product details. Additionally, the package includes a curated collection of open datasets focused on drugs, pharmaceuticals, treatments, and clinical studies. These datasets cover diverse topics such as treatment dosages, pharmacological studies, placebo effects, drug reactions, misuses of pain relievers, and vaccine effectiveness. The package supports reproducible research and teaching in pharmacology, medicine, and healthcare by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: FDA API <https://open.fda.gov/apis/> and Health Canada API <https://health-products.canada.ca/api/documentation/dpd-documentation-en.html>.

Automatically estimate 11 effect size measures from a well-formatted dataset. Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets. This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size.

This package provides methods and tools for deriving spatial summary functions from single-cell imaging data and performing functional data analyses. Functions can be applied to other single-cell technologies such as spatial transcriptomics. Functional regression and functional principal component analysis methods are in the refund package <https://cran.r-project.org/package=refund> while calculation of the spatial summary functions are from the spatstat package <https://spatstat.org/>.

This package provides readers for easy and consistent importing of Mouse Genome Informatics (MGI) report files: <https://www.informatics.jax.org/downloads/reports/index.html>. These data are provided by Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ, Mouse Genome Informatics Group (2024) <doi:10.1093/genetics/iyae031>.

This package creates data with identical statistics (metamers) using an iterative algorithm proposed by Matejka & Fitzmaurice (2017) <DOI:10.1145/3025453.3025912>.

With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025) <doi:10.1093/jrsssc/qlaf027>). An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.

This package provides a suite of convenience functions for generating US state and county thematic maps using datasets from the MazamaSpatialUtils package.

This package provides functions to calculate Unique Trait Combinations (UTC) and scaled Unique Trait Combinations (sUTC) as measures of multivariate richness. The package can also calculate beta-diversity for trait richness and can partition this into nestedness-related and turnover components. The code will also calculate several measures of overlap. See Keyel and Wiegand (2016) <doi:10.1111/2041-210X.12558> for more details.

Global testing for regression discontinuity designs with more than one running variable. The function cef_disc_test() is used for testing whether there exist non-zero treatment effects along the boundary of the treated region. The function density_disc_test() is used for testing whether there exist discontinuities in the joint density of the running variables along the boundary of the treated region. The methodology follows Samiahulin (2026), "Global Testing for Regression Discontinuity Designs with Multiple Running Variables" <doi:10.48550/arXiv.2602.03819>.

This package provides a minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections. See Brown (2016) <doi:10.32614/RJ-2016-005>.

It offers random-forest-based functions to impute clustered incomplete data. The package is tailored for but not limited to imputing multitissue expression data, in which a gene's expression is measured on the collected tissues of an individual but missing on the uncollected tissues.

Exploratory data analysis and manipulation functions for multi- label data sets along with an interactive Shiny application to ease their use.

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this package, we present the multivariate MArginal ePIstasis Test ('mvMAPIT') â a multi-outcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact â thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multi-trait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. Crawford et al. (2017) <doi:10.1371/journal.pgen.1006869>. Stamp et al. (2023) <doi:10.1093/g3journal/jkad118>. Stamp et al. (2025) <doi:10.1016/j.ajhg.2025.07.004>.

This package provides methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, CIE Lab, CIE Luv, or RGB. Includes ISCC-NBS color block lookup. Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".

Functionality for generating and plotting random mazes. The mazes are based on matrices, so can only consist of vertical and horizontal lines along a regular grid. But there is no need to use every possible space, so they can take on many different shapes.

Calculates and differentiates probabilities and density of (conditional) multivariate normal distribution and Gaussian copula (with various marginal distributions) using methods described in A. Genz (2004) <doi:10.1023/B:STCO.0000035304.20635.31>, A. Genz, F. Bretz (2009) <doi:10.1007/978-3-642-01689-9>, H. I. Gassmann (2003) <doi:10.1198/1061860032283> and E. Kossova, B. Potanin (2018) <https://ideas.repec.org/a/ris/apltrx/0346.html>.

Comprehensive toolkit for Environmental Phillips Curve analysis featuring multidimensional instrumental variable creation, transfer entropy causal discovery, network analysis, and state-of-the-art econometric methods. Implements geographic, technological, migration, geopolitical, financial, and natural risk instruments with robust diagnostics and visualization. Provides 24 different instrumental variable approaches with empirical validation. Methods based on Phillips (1958) <doi:10.1111/j.1468-0335.1958.tb00003.x>, transfer entropy by Schreiber (2000) <doi:10.1103/PhysRevLett.85.461>, and weak instrument tests by Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006>.

Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates (Van Lissa, 2020). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.

An interface to the Microsoft 365 (formerly known as Office 365') suite of cloud services, building on the framework supplied by the AzureGraph package. Enables access from R to data stored in Teams', SharePoint Online and OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured Outlook email client, with the ability to send emails and manage emails and mail folders.

This package implements the multivariate autoregressive distributed lag (ARDL) unit root test proposed by Sam, McNown, Goh, and Goh (2024) <doi:10.1080/03796205.2024.2439101>. The test augments the standard ADF regression with lagged levels of a covariate to improve power when cointegration exists. Bootstrap critical values ensure correct size regardless of nuisance parameters. Provides automatic lag selection via AIC/BIC, diagnostic tests, and comprehensive inference tables following the four-case framework.

This package provides methods for estimating and utilizing the multivariate generalized propensity score (mvGPS) for multiple continuous exposures described in Williams, J.R, and Crespi, C.M. (2020) <arxiv:2008.13767>. The methods allow estimation of a dose-response surface relating the joint distribution of multiple continuous exposure variables to an outcome. Weights are constructed assuming a multivariate normal density for the marginal and conditional distribution of exposures given a set of confounders. Confounders can be different for different exposure variables. The weights are designed to achieve balance across all exposure dimensions and can be used to estimate dose-response surfaces.