Multivariate functional principal component analysis via fast covariance estimation for multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) <doi: 10.1002/sta4.245>.

Factorize binary matrices into rank-k components using the logistic function in the updating process. See e.g. Tomé et al (2015) <doi:10.1007/s11045-013-0240-9> .

This package contains the following 5 nonparametric hypothesis tests: The Sign Test, The 2 Sample Median Test, Miller's Jackknife Procedure, Cochran's Q Test, & The Stuart-Maxwell Test.

An implementation of the Naive Bayes Classifier (NBC) algorithm used for Verbal Autopsy (VA) built on code from Miasnikof et al (2015) <DOI:10.1186/s12916-015-0521-2>.

Helper functions for coding object-oriented programming with a focus on R6. Includes functions for assertions and testing, looping, and re-usable design patterns including Abstract and Decorator classes.

This package provides tools for the test for the comparison of survival curves, the evaluation of the goodness-of-fit and the predictive capacity of the proportional hazards model.

Structured fusion Lasso penalized estimation of multi-state models with the penalty applied to absolute effects and absolute effect differences (i.e., effects on transition-type specific hazard rates).

This package provides pseudo-likelihood methods for empirically analyzing common signaling games in international relations as described in Crisman-Cox and Gibilisco (2019) <doi:10.1017/psrm.2019.58>.

Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.

This package provides functionality to fit and simulate from stationary vine copula models for time series, see Nagler et al. (2022) <doi:10.1016/j.jeconom.2021.11.015>.

Data and functions to support Bayesian and frequentist inference and decision making for the Coursera Specialization "Statistics with R". See <https://github.com/StatsWithR/statsr> for more information.

Spatial coverage sampling and random sampling from compact geographical strata created by k-means. See Walvoort et al. (2010) <doi:10.1016/j.cageo.2010.04.005> for details.

This package implements methods for anticipating the emergence and eradication of infectious diseases from surveillance time series. Also provides support for computational experiments testing the performance of such methods.

To provide a high dimensional grouped variable selection approach for detection of whole-genome SNP effects and SNP-SNP interactions, as described in Fang et al. (2017, under review).

This package provides a tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.

Generate continuous maps of genetic diversity using moving windows with options for rarefaction, interpolation, and masking as described in Bishop et al. (2023) <doi:10.1111/2041-210X.14090>.

The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR.

This package provides Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.

This package provides an all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).

This package lets you plot model surfaces for a wide variety of models using partial dependence plots and other techniques. Also plot model residuals and other information on the model.

This package provides methods and algorithms for discrete optimization, e.g. knapsack and subset sum procedures, derivative-free Nelder-Mead and Hooke-Jeeves minimization, and some (evolutionary) global optimization functions.

This package provides tools for exploratory data analysis and data visualization of biological sequence (DNA and protein) data. It also includes utilities for sequence data management under the ACNUC system.

Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) <doi:10.1007/s11306-017-1178-y>.

Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. The RavenR package is also archived at Chlumsky et al. (2020) <doi:10.5281/zenodo.4248183>. The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) <doi:10.1016/j.envsoft.2020.104728>.