This package implements inferential and graphic procedures for the semiparametric proportional means regression of weighted composite endpoint of recurrent event and death (Mao and Lin, 2016, <doi:10.1093/biostatistics/kxv050>).

This is a collection of some useful functions when dealing with text data. Currently it only contains a very efficient function of decoding HTML entities in character vectors by Rcpp routine.

This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting.

This package provides a package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns.

R-escape streamlines gene set enrichment analysis for single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize GSEA across individual cells.

This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. It also contains a Shiny application for interactive exploration of results.

This package is designed to ease the application and comparison of multiple hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are standardized and usable by the accompanying mutossGUI package.

This R package provides tools for training gapped-kmer SVM classifiers for DNA and protein sequences. This package supports several sequence kernels, including: gkmSVM, kmer-SVM, mismatch kernel and wildcard kernel.

Inspired by the the futile.logger R package and logging Python module, this utility provides a flexible and extensible way of formatting and delivering log messages with low overhead.

This package provides a ggplot2 extension that enables the rendering of complex formatted plot labels (titles, subtitles, facet labels, axis labels, etc.). Text boxes with automatic word wrap are also supported.

This is a package for estimation of a sparse inverse covariance matrix using a lasso (L1) penalty. Facilities are provided for estimates along a path of values for the regularization parameter.

This package contains utility functions used by the Genome Analysis Toolkit (GATK) to load tables and plot data. The GATK is a toolkit for variant discovery in high-throughput sequencing data.

This package lets you manage configuration values across multiple environments (e.g. development, test, production). It reads values using a function that determines the current environment and returns the appropriate value.

The main purpose of this package is to perform simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. Dependent variables can be single or multivariate networks, which can be directed, non-directed, or two-mode; and associated actor variables. There are also functions for testing parameters and checking goodness of fit. An overview of these models is given in Snijders (2017), <doi:10.1146/annurev-statistics-060116-054035>.

This package provides a simplified version of the Portal Project Database designed for teaching. It provides a real world example of life-history, population, and ecological data, with sufficient complexity to teach many aspects of data analysis and management, but with many complexities removed to allow students to focus on the core ideas and skills being taught. The full database (which should be used for research) is available at <https://github.com/weecology/PortalData>.

Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses.

For any two way feature-set from a pair of pre-processed omics data, 3 different true discovery proportions (TDP), namely pairwise-TDP, column-TDP and row-TDP are calculated. Due to embedded closed testing procedure, the choice of feature-sets can be changed infinite times and even after seeing the data without any change in type I error rate. For more details refer to Ebrahimpoor et al., (2024) <doi:10.48550/arXiv.2410.19523>.

Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, riskyr computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent.

This package provides a fast and intuitive batch effect removal tool for single-cell data. BBKNN is originally used in the scanpy python package, and now can be used with Seurat seamlessly.

R client for Bender Hyperparameters optimizer : <https://bender.dreem.com> The R client allows you to communicate with the Bender API and therefore submit some new trials within your R script itself.

R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.

The Bloom Detecting Algorithm enables the detection of blooms within a time series of species abundance and extracts 22 phenological variables. For details, see Karasiewicz et al. (2022) <doi:10.3390/jmse10020174>.

This package provides a way to reduce model objects to necessary parts, making them easier to work with, store, share and simulate multiple values for new responses while allowing for parameter uncertainty.

The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review).