Calculates population attributable fraction causal effects. The causalPAF package contains a suite of functions for causal analysis calculations of population attributable fractions (PAF) given a causal diagram which apply both: Pathway-specific population attributable fractions (PS-PAFs) Oâ Connell and Ferguson (2022) <doi:10.1093/ije/dyac079> and Sequential population attributable fractions Ferguson, Oâ Connell, and Oâ Donnell (2020) <doi:10.1186/s13690-020-00442-x>. Results are presentable in both table and plot format.

This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.

This package provides a new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. <doi:10.1007/s11222-023-10328-x>.

Integrated, convenient, and uniform access to Canadian Census data and geography retrieved using the CensusMapper API. This package produces analysis-ready tidy data frames and spatial data in multiple formats, as well as convenience functions for working with Census variables, variable hierarchies, and region selection. API keys are freely available with free registration at <https://censusmapper.ca/api>. Census data and boundary geometries are reproduced and distributed on an "as is" basis with the permission of Statistics Canada (Statistics Canada 2001; 2006; 2011; 2016; 2021).

Dataset for the R package cancerclass.

This package provides a package containing an environment representing the Canine_2.cdf file.

Affymetrix Affymetrix Canine_2 Array annotation data (chip canine2) assembled using data from public repositories.

Terrestrial maps with simplified topologies for Census Divisions, Agricultural Regions, Economic Regions, Federal Electoral Divisions and Provinces.

Base annotation databases for canine, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.

Identification of cardinal dates (begin, time of maximum, end of mass developments) in ecological time series using fitted Weibull functions.

The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.

This package implements methods for querying data from CalPASS using its API. CalPASS Plus. MMAP API V1. <https://mmap.calpassplus.org/docs/index.html>.

Fast and efficient reading and writing of mass spectrometry imaging data files. Supports imzML and Analyze 7.5 formats. Provides ontologies for mass spectrometry imaging.

Several causal effects are measured using least squares regressions and basis function approximations. Backward and forward selection methods based on different criteria are used to select the basis functions.

This package provides six variants of two-way correspondence analysis (ca): simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca, singly ordered non symmetrical ca, and doubly ordered non symmetrical ca.

Procedures for making continuous cartogram. Procedures available are: flow based cartogram (Gastner & Newman (2004) <doi:10.1073/pnas.0400280101>), fast flow based cartogram (Gastner, Seguy & More (2018) <doi:10.1073/pnas.1712674115>), rubber band based cartogram (Dougenik et al. (1985) <doi:10.1111/j.0033-0124.1985.00075.x>).

This package implements the framework introduced in Di Francesco and Mellace (2025) <doi:10.48550/arXiv.2502.11691>, shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences designs.

Computes a range of scatterplot diagnostics (scagnostics) on pairs of numerical variables in a data set. A range of scagnostics, including graph and association-based scagnostics described by Leland Wilkinson and Graham Wills (2008) <doi:10.1198/106186008X320465> and association-based scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be computed. Summary and plotting functions are provided.

Predicts anticancer peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The CancerGram model is too large for CRAN and it has to be downloaded separately from the repository: <https://github.com/BioGenies/CancerGramModel>. For more information see: Burdukiewicz et al. (2020) <doi:10.3390/pharmaceutics12111045>.

This package performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) <doi:10.1111/rssc.12599>). Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points.

Example data sets to run the example problems from causal inference textbooks. Currently, contains data sets for Huntington-Klein, Nick (2021 and 2025) "The Effect" <https://theeffectbook.net>, first and second edition, Cunningham, Scott (2021 and 2025, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.

This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. CatsCradle allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes.

CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization.

This is a one-function package that will pass only unique values to a computationally-expensive function that returns an output of the same length as the input. In importing and working with tidy data, it is common to have index columns, often including time stamps that are far from unique. Some functions to work with these such as text conversion to other variable types (e.g. as.POSIXct()), various grep()-based functions, and often the cut() function are relatively slow when working with tens of millions of rows or more.