Classifies the type of cancer using routinely collected data commonly found in cancer registries from pathology reports. The package implements the International Classification of Diseases for Oncology, 3rd Edition site (topography), histology (morphology), and behaviour codes of neoplasms to classify cancer type <https://www.who.int/standards/classifications/other-classifications/international-classification-of-diseases-for-oncology>. Classification in children utilize the International Classification of Childhood Cancer by Steliarova-Foucher et al. (2005) <doi:10.1002/cncr.20910>. Adolescent and young adult cancer classification is based on Barr et al. (2020) <doi:10.1002/cncr.33041>.

CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.

Automated method for doublet detection in flow or mass cytometry data, based on simulating doublets and finding events whose protein expression patterns are similar to the simulated doublets.

This package provides the tools to produce catseye plots, principally by catseyesplot() function which calls R's standard plot() function internally, or alternatively by the catseyes() function to overlay the catseye plot onto an existing R plot window. Catseye plots illustrate the normal distribution of the mean (picture a normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence interval; they are an intuitive way of illustrating and comparing normally distributed estimates, and are arguably a superior alternative to standard confidence intervals, since they show the full distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require pre-calculated means and standard errors (or standard deviations), provided as numeric vectors; this allows the flexibility of obtaining this information from a variety of sources, such as direct calculation or prediction from a model. Catseye plots, as illustrations of the normal distribution of the means, are described in Cumming (2013 & 2014). Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629.

Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.

Canonical correlation analysis (CCA) via reduced-rank regression with support for regularization and cross-validation. Several methods for estimating CCA in high-dimensional settings are implemented. The first set of methods, cca_rrr() (and variants: cca_group_rrr() and cca_graph_rrr()), assumes that one dataset is high-dimensional and the other is low-dimensional, while the second, ecca() (for Efficient CCA) assumes that both datasets are high-dimensional. For both methods, standard l1 regularization as well as group-lasso regularization are available. cca_graph_rrr further supports total variation regularization when there is a known graph structure among the variables of the high-dimensional dataset. In this case, the loadings of the canonical directions of the high-dimensional dataset are assumed to be smooth on the graph. For more details see Donnat and Tuzhilina (2024) <doi:10.48550/arXiv.2405.19539> and Wu, Tuzhilina and Donnat (2025) <doi:10.48550/arXiv.2507.11160>.

This package provides a toolkit for querying Team Cymru <http://team-cymru.org> IP address, Autonomous System Number ('ASN'), Border Gateway Protocol ('BGP'), Bogon and Malware Hash Data Services.

Computes maximum response from Cardiac Magnetic Resonance Images using spatial and voxel wise spline based Bayesian model. This is an implementation of the methods described in Schmid (2011) <doi:10.1109/TMI.2011.2109733> "Voxel-Based Adaptive Spatio-Temporal Modelling of Perfusion Cardiovascular MRI". IEEE TMI 30(7) p. 1305 - 1313.

Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.

This model fitting tool incorporates cyclic coordinate descent and majorization-minimization approaches to fit a variety of regression models found in large-scale observational healthcare data. Implementations focus on computational optimization and fine-scale parallelization to yield efficient inference in massive datasets. Please see: Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.

This package provides a uniform statistical inferential tool in making individualized treatment decisions, which implements the methods of Ma et al. (2017)<DOI:10.1177/0962280214541724> and Guo et al. (2021)<DOI:10.1080/01621459.2020.1865167>. It uses a flexible semiparametric modeling strategy for heterogeneous treatment effect estimation in high-dimensional settings and can gave valid confidence bands. Based on it, one can find the subgroups of patients that benefit from each treatment, thereby making individualized treatment selection.

Interactive shiny application for running classical test theory (item analysis).

An implementation of the Chrome DevTools Protocol', for controlling a headless Chrome web browser.

Speeds up exploratory data analysis (EDA) by providing a succinct workflow and interactive visualization tools for understanding which features have relationships to target (response). Uses binary correlation analysis to determine relationship. Default correlation method is the Pearson method. Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.

Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. Most of the data relate to the set of 271 Intermediate Zones (IZ) that make up the 2001 definition of the Greater Glasgow and Clyde health board.

Generation of different Christmas cards, most of them being animated. Most of the cards can be generated in three languages (English, Catalan and Spanish). The collection started in 2009.

This package contains the basic functions to apply the unified framework for partitioning the drivers of stability of ecological communities. Segrestin et al. (2024) <doi:10.1111/geb.13828>.

Generates synthetic data distributions to enable testing various modelling techniques in ways that real data does not allow. Noise can be added in a controlled manner such that the data seems real. This methodology is generic and therefore benefits both the academic and industrial research.

Extension of cmprsk to Stratified and Clustered data. A goodness of fit test for Fine-Gray model is also provided. Methods are detailed in the following articles: Zhou et al. (2011) <doi:10.1111/j.1541-0420.2010.01493.x>, Zhou et al. (2012) <doi:10.1093/biostatistics/kxr032>, Zhou et al. (2013) <doi: 10.1002/sim.5815>.

Calculating silhouette information for clusters on circular or linear data using fast algorithms. These algorithms run in linear time on sorted data, in contrast to quadratic time by the definition of silhouette. When used together with the fast and optimal circular clustering method FOCC (Debnath & Song 2021) <doi:10.1109/TCBB.2021.3077573> implemented in R package OptCirClust', circular silhouette can be maximized to find the optimal number of circular clusters; it can also be used to estimate the period of noisy periodical data.

The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution <doi:10.1111/stan.12355>, of which functions are provided.

Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix to another data matrix. More specifically, CoCA maximises the weighted covariance between the weighted averaged species scores of one community and the weighted averaged species scores of another community. CoCA attempts to find patterns that are common to both communities.

Quantifies and assesses the significance of convergent evolution using multiple methods and measures as described in Stayton (2015) <DOI: 10.1111/evo.12729> and Grossnickle et al. 2023. Also displays results in various ways.

Automates the process of containerizing R projects. The core function of containr is generate_dockerfile()', which analyzes an R project's environment and dependencies via an renv lock file and generates a ready-to-use Dockerfile that encapsulates the computational setup. The package helps researchers build portable and consistent workflows so that analyses can be reliably shared, archived, and rerun across systems. See R Core Team (2025) <https://www.R-project.org/>, Ushey et al. (2025) <https://CRAN.R-project.org/package=renv>, and Docker Inc. (2025) <https://www.docker.com/>.