The package ANF(Affinity Network Fusion) provides methods for affinity matrix construction and fusion as well as spectral clustering. This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion.

This package provides functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, and several other tools.

This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment.

This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture.

An interface for creating new condition generators objects. Generators are special functions that can be saved in registries and linked to other functions. Utilities for documenting your generators, and new conditions is provided for package development.

This package contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1.

An implementation of common higher order functions with syntactic sugar for anonymous function. Provides also a link to dplyr and data.table for common transformations on data frames to work around non standard evaluation by default.

This package performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.

This package implements an n-dimensional parameter space partitioning algorithm for evaluating the global behaviour of formal computational models as described by Pitt, Kim, Navarro and Myung (2006) <doi:10.1037/0033-295X.113.1.57>.

This package provides streamlined data import and export infrastructure by making assumptions that the user is probably willing to make: import and export determine the data structure from the file extension, reasonable defaults are used for data import and export (e.g., stringsAsFactors=FALSE), web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly without explicit decompression, and fast import packages are used where appropriate. An additional convenience function, convert, provides a simple method for converting between file types.

This is a package for estimating the copula mixture model and plotting correspondence curves in "Measuring reproducibility of high-throughput experiments" (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel

This package implements methods to perform fast approximate K-nearest neighbor search on the input matrix. The algorithm is based on the N2 implementation of an approximate nearest neighbor search using hierarchical NSW graphs.

Perform various floating catchment area methods to calculate a spatial accessibility index (SPAI) for demand point data. The distance matrix used for weighting is normalized in a preprocessing step using common functions (gaussian, gravity, exponential or logistic).

Estimates the first-exposure effect (FEE) using a one-inflated positive Poisson model, or a one-inflated zero-truncated negative binomial model. In addition, estimates the marginal FEE, and standard errors for the FEE and marginal FEE.

This package provides functions to compute and plot Krippendorff's inter-coder reliability coefficient alpha and bootstrapped uncertainty estimates (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to make use of parallel threads where supported.

Kernel smoothing for Wishart random matrices described in Daayeb, Khardani and Ouimet (2025) <doi:10.48550/arXiv.2506.08816>, Gaussian and log-Gaussian models using least square or likelihood cross validation criteria for optimal bandwidth selection.

Collection of functions designed to calculate numerical standard error (NSE) of univariate time series as described in Ardia et al. (2018) <doi:10.1515/jtse-2017-0011> and Ardia and Bluteau (2017) <doi:10.21105/joss.00172>.

Utilize the Bayesian prior and posterior predictive checking approach to provide a statistical assessment of replication success and failure. The package is based on the methods proposed in Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.

Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.

Converts English phrases to singular or plural form based on the length of an associated vector. Contains helper functions to create natural language lists from vectors and to include the length of a vector in natural language.

Threshold model, panel version of Hylleberg et al. (1990) <DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root test of Chang (2002) <DOI:10.1016/S0304-4076(02)00095-7>.

The PDE (Pdf Data Extractor) allows the extraction of information and tables optionally based on search words from PDF (Portable Document Format) files and enables the visualization of the results, both by providing a convenient user-interface.

This package provides a user-friendly framework for estimating a wide variety of cross-sectional and panel stochastic frontier models. Suitable for a broad range of applications, the implementation offers extensive flexibility in specification and estimation techniques.

This package provides a collection of functions for generating frequency tables and cross-tabulations of categorical variables. The resulting tables can be exported to various formats (Excel, PDF, HTML, etc.) with extensive formatting and layout customization options.