This package provides the header files for a stripped-down version of the plog header-only C++ logging library, and a method to log to R's standard error stream.

This package provides two high quality and fast PPRNGs that may be used in an OpenMP parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence.

This package provides a basic set of R functions for querying the Cancer Genomics Data Server (CGDS), hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).

Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms.

This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool.

This package contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.

Wraps the AT Protocol (Authenticated Transfer Protocol) behind Bluesky <https://bsky.social>. Functions can be used for, among others, retrieving posts and followers from the network or posting content.

This package implements the adaptive smoothing spline estimator for the function-on-function linear regression model described in Centofanti et al. (2023) <doi:10.1007/s00180-022-01223-6>.

Fits the Bayesian partial least squares regression model introduced in Urbas et al. (2024) <doi:10.1214/24-AOAS1947>. Suitable for univariate and multivariate regression with high-dimensional data.

Implementations of canonical associative learning models, with tools to run experiment simulations, estimate model parameters, and compare model representations. Experiments and results are represented using S4 classes and methods.

An efficient and convenient set of functions to perform differential network estimation through the use of alternating direction method of multipliers optimization with a variety of different loss functions.

Perform a Bayesian estimation of the exploratory deterministic input, noisy and gate (EDINA) cognitive diagnostic model described by Chen et al. (2018) <doi:10.1007/s11336-017-9579-4>.

Add a scroll back to top Font Awesome icon <https://fontawesome.com/> in rmarkdown documents and shiny apps thanks to jQuery GoTop <https://scottdorman.blog/jquery-gotop/>.

This package provides tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.

Mixture modelling of one-dimensional data using combinations of left-truncated Gamma, Weibull, and Lognormal Distributions. Blostein, Martin & Miljkovic, Tatjana. (2019) <doi:10.1016/j.insmatheco.2018.12.001>.

This package provides the method for computing the local partial autocorrelation function for locally stationary wavelet time series from Killick, Knight, Nason, Eckley (2020) <doi:10.1214/20-EJS1748>.

This package provides a suite of functions for reading in a rate file in XML format, stratify a cohort, and calculate SMRs from the stratified cohort and rate file.

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 a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.

Large-scale gene expression studies allow gene network construction to uncover associations among genes. This package is developed for estimating and testing partial correlation graphs with prior information incorporated.

This package provides access to material from the book "Processing and Analyzing Financial Data with R" by Marcelo Perlin (2017) available at <https://sites.google.com/view/pafdr/home>.

Bayesian variable selection for regression models of under-reported count data as well as for (overdispersed) Poisson, negative binomal and binomial logit regression models using spike and slab priors.

Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.

Use phenotype risk scores based on linked clinical and genetic data to study Mendelian disease and rare genetic variants. See Bastarache et al. 2018 <doi:10.1126/science.aal4043>.