Calculates a cumulative summation nonparametric extended median test based on the work of Brown & Schaffer (2020) <DOI:10.1080/03610926.2020.1738492>. It then generates a control chart to assess processes and determine if any streams are out of control.

Evaluates the Owen Q-function for an integer value of the degrees of freedom, by applying Owen's algorithm (1965) <doi:10.1093/biomet/52.3-4.437>. It is useful for the calculation of the power of equivalence tests.

Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial. More information in Dusseldorp, E., Doove, L., & Van Mechelen, I. (2016) <doi:10.3758/s13428-015-0594-z>.

This package provides a covariance estimator for multivariate normal data that is sparse and positive definite. Implements the majorize-minimize algorithm described in Bien, J., and Tibshirani, R. (2011), "Sparse Estimation of a Covariance Matrix," Biometrika. 98(4). 807--820.

This package provides a general, tidyverse'-friendly framework for simulation studies, design analysis, and power analysis. Specify data generation, define varying parameters, generate data, fit models, and tidy model results in a single pipeline, without needing loops or custom functions.

Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.

This package provides a tool to obtain tumor growth rates from clinical trial patient data. Output includes individual and summary data for tumor growth rate estimates as well as optional plots of the observed and predicted tumor quantity over time.

This package provides a type system for R. It supports setting variable types in a script or the body of a function, so variables can't be assigned illegal values. Moreover it supports setting argument and return types for functions.

This package provides various commonly-used response time trimming methods, including the recursive / moving-criterion methods reported by Van Selst and Jolicoeur (1994). By passing trimming functions raw data files, the package will return trimmed data ready for inferential testing.

Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method (<doi:10.2307/2531628>). Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options.

Compared with the similar graph embedding method such as Laplacian Eigenmaps, Vicus can exploit more local structures of graph data. For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/Vicus>.

Makes available code necessary to reproduce figures and tables in papers on the WaveD method for wavelet deconvolution of noisy signals as presented in The WaveD Transform in R, Journal of Statistical Software Volume 21, No. 3, 2007.

Root Expected Proportion Squared Difference (REPSD) is a nonparametric differential item functioning (DIF) method that (a) allows practitioners to explore for DIF related to small, fine-grained focal groups of examinees, and (b) compares the focal group directly to the composite group that will be used to develop the reported test score scale. Using your provided response matrix with a column that identifies focal group membership, this package provides the REPSD values, a simulated null distribution of possible REPSD values, and the simulated p-values identifying items possibly displaying DIF without requiring enormous sample sizes.

Integrates population dynamics and dispersal into a mechanistic virtual species simulator. The package can be used to study the effects of environmental change on population growth and range shifts. It allows for simple and straightforward definition of population dynamics (including positive density dependence), extensive possibilities for defining dispersal kernels, and the ability to generate virtual ecologist data. Learn more about the rangr at <https://docs.ropensci.org/rangr/>. This work was supported by the National Science Centre, Poland, grant no. 2018/29/B/NZ8/00066 and the PoznaÅ Supercomputing and Networking Centre (grant no. pl0090-01).

This package can be used for the analysis of gene expression studies, especially the use of linear models for analysing designed experiments and the assessment of differential expression. The analysis methods apply to different technologies, including microarrays, RNA-seq, and quantitative PCR.

Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores.

This package provides tools to help working with text files. It can return the number of lines; print the first and last lines; convert encoding. Operations are made without reading the entire file before starting, resulting in good performances with large files.

This package provides a collection of functions to visualize spatial data and models on top of static maps from various online sources (e.g Google Maps and Stamen Maps). It includes tools common to those tasks, including functions for geolocation and routing.

This package provides a wrapper around the Parsing Expression Grammar Template Library, a C++11 library for generating parsing expression grammars, that makes it accessible within Rcpp. With this, developers can implement their own grammars and easily expose them in R packages.

ripgrep (rg) is a line-oriented search tool that recursively searches your current directory for a regex pattern while respecting your gitignore rules. ripgrep is similar to other popular search tools like The Silver Searcher, ack and grep.

This package provides a package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization.

Hidden Ising models are implemented to identify enriched genomic regions in ChIP-chip data. They can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates.

Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley.

This package implements z-test, t-test, and normal moment prior Bayes factors based on summary statistics, along with functionality to perform corresponding power and sample size calculations as described in Pawel and Held (2025) <doi:10.1080/00031305.2025.2467919>.