An aggressive dimensionality reduction and network estimation technique for a high-dimensional Gaussian graphical model (GGM). Please refer to: Efficient Dimensionality Reduction for High-Dimensional Network Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of The 31st International Conference on Machine Learning, 2014, p. 1953--1961.

This package provides functions to impute missing values using Gaussian copulas for mixed data types as described by Christoffersen et al. (2021) <arXiv:2102.02642>. The method is related to Hoff (2007) <doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845> but differs by making a direct approximation of the log marginal likelihood using an extended version of the Fortran code created by Genz and Bretz (2002) <doi:10.1198/106186002394> in addition to also support multinomial variables.

Provides an interactive toolkit for educational and psychological measurement implemented using the shiny framework. The package supports content validity analysis, dimensionality assessment, and Classical Test Theory using the CTT package (Willse, 2018) <doi:10.32614/CRAN.package.CTT>.Item Response Theory (IRT) analyses are conducted via mirt (Chalmers, 2012) <doi:10.18637/jss.v048.i06>. Exploratory Factor Analysis is performed using psych (Revelle, 2025), while Confirmatory Factor Analysis and Structural Equation Modeling are based on the lavaan framework (Rosseel, 2012) <doi:10.18637/jss.v048.i02>. The application allows users to upload data, evaluate statistical models, visualize results, and export outputs through an intuitive graphical interface without requiring programming experience.

This package provides tools for creating and issuing nicely-formatted text within R diagnostic messages and those messages given during warnings and errors. The formatting of the messages can be customized using templating features. Issues with singular and plural forms can be handled through specialized syntax.

This package provides a tool for optimizing scales of effect when modeling ecological processes in space. Specifically, the scale parameter of a distance-weighted kernel distribution is identified for all environmental layers included in the model. Includes functions to assist in model selection, model evaluation, efficient transformation of raster surfaces using fast Fourier transformation, and projecting models. For more details see Peterman (2025) <doi:10.21203/rs.3.rs-7246115/v1>.

Enhances mlexperiments <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners for survival analysis. The package provides R6-based survival learners for the following algorithms: glmnet <https://CRAN.R-project.org/package=glmnet>, ranger <https://CRAN.R-project.org/package=ranger>, xgboost <https://CRAN.R-project.org/package=xgboost>, and rpart <https://CRAN.R-project.org/package=rpart>. These can be used directly with the mlexperiments R package.

This package implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.

The stepwise regression with assumptions checking and the possible Box-Cox transformation.

Selects bandwidth for the kernel density estimator with minimum distance method as proposed by Devroye and Lugosi (1996). The minimum distance method directly selects the optimal kernel density estimator from countably infinite kernel density estimators and indirectly selects the optimal bandwidth. This package selects the optimal bandwidth from finite kernel density estimators.

This package provides a variety of association tests for microbiome data analysis including Quasi-Conditional Association Tests (QCAT) described in Tang Z.-Z. et al.(2017) <doi:10.1093/bioinformatics/btw804> and Zero-Inflated Generalized Dirichlet Multinomial (ZIGDM) tests described in Tang Z.-Z. & Chen G. (2017, submitted).

This package provides probability mass, distribution, quantile, random variate generation, and method-of-moments parameter fitting for the MBBEFD family of distributions used in insurance modeling as described in Bernegger (1997) <doi:10.2143/AST.27.1.563208> without any external dependencies.

An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. Several methods were implemented to reduce the metabolomics matrix such as the principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9> , the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>. Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.

Includes support for Mapbox Navigation APIs, including directions, isochrones, and route optimization; the Search API for forward and reverse geocoding; the Maps API for interacting with Mapbox vector tilesets and visualizing Mapbox maps in R; and Mapbox Tiling Service and tippecanoe for generating map tiles. See <https://docs.mapbox.com/api/> for more information about the Mapbox APIs.

This package provides a toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.

This package implements nonparametric bootstrap tests for detecting monotonicity in regression functions from Hall, P. and Heckman, N. (2000) <doi:10.1214/aos/1016120363> Includes tools for visualizing results using Nadaraya-Watson kernel regression and supports efficient computation with C++'. Tutorials and shiny application demo are available at <https://www.laylaparast.com/monotonicitytest> and <https://parastlab.shinyapps.io/MonotonicityTest>.

Estimates multivariate subgaussian stable densities and probabilities as well as generates random variates using product distribution theory. A function for estimating the parameters from data to fit a distribution to data is also provided, using the method from Nolan (2013) <doi:10.1007/s00180-013-0396-7>.

Various tools for microeconomic analysis and microeconomic modelling, e.g. estimating quadratic, Cobb-Douglas and Translog functions, calculating partial derivatives and elasticities of these functions, and calculating Hessian matrices, checking curvature and preparing restrictions for imposing monotonicity of Translog functions.

This package provides a simple and trustworthy methodology for the analysis of misreported continuous time series. See Moriña, D, Fernández-Fontelo, A, Cabaña, A, Puig P. (2021) <arXiv:2003.09202v2>.

The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.

This package provides methods for calculating and testing the significance of pairwise monotonic association from and based on the work of Pimentel (2009) <doi:10.4135/9781412985291.n2>. Computation of association of vectors from one or multiple sets can be performed in parallel thanks to the packages foreach and doMC'.

This package provides a tidy workflow for landscape-scale analysis. multilandr offers tools to generate landscapes at multiple spatial scales and compute landscape metrics, primarily using the landscapemetrics package. It also features utility functions for plotting and analyzing multi-scale landscapes, exploring correlations between metrics, filtering landscapes based on specific conditions, generating landscape gradients for a given metric, and preparing datasets for further statistical analysis. Documentation about multilandr is provided in an introductory vignette included in this package and in the paper by Huais (2024) <doi:10.1007/s10980-024-01930-z>; see citation("multilandr") for details.

This package provides R6 objects to perform parallelized hyperparameter optimization and cross-validation. Hyperparameter optimization can be performed with Bayesian optimization (via rBayesianOptimization <https://cran.r-project.org/package=rBayesianOptimization>) and grid search. The optimized hyperparameters can be validated using k-fold cross-validation. Alternatively, hyperparameter optimization and validation can be performed with nested cross-validation. While mlexperiments focuses on core wrappers for machine learning experiments, additional learner algorithms can be supplemented by inheriting from the provided learner base class.

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.

This package provides functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by à yvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>).