Data input/output functions for data that conform to the Digital Imaging and Communications in Medicine (DICOM) standard, part of the Rigorous Analytics bundle.

This package provides a method for the quantitative prediction using omics data. This package provides functions to construct the quantitative prediction model using omics data.

Safely extracts and coerces values from a Power BI parameter table (one row, multiple columns) without string concatenation or injection of raw values into scripts.

Estimate the internal consistency of your tasks with a permutation based split-half reliability approach. Unofficial release name: "I eat stickers all the time, dude!".

High-performance parsing of Tableau workbook files into tidy data frames and dependency graphs for other visualization tools like R Shiny or Power BI replication.

This package provides a method for combining single-cell cytometry datasets, which increases the analytical flexibility and the statistical power of the analyses while minimizing technical noise.

This package provides a common interface to specifying clustering models, in the same style as parsnip. It creates a unified interface across different functions and computational engines.

This package provides various tools for creating iterators, many patterned after functions in the Python itertools module, and others patterned after functions in the snow package.

Cl-reexport makes a package reexport symbols which are external symbols in other Common Lisp packages. This functionality is intended to be used with (virtual) hierarchical packages.

wf-recorder is a utility program for screen recording of wlroots-based compositors. More specifically, those that support wlr-screencopy-v1 and xdg-output.

CNVfilteR identifies those CNVs that can be discarded by using the single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines.

This package provides tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided.

The cov.nnve() function implements robust covariance estimation by the nearest neighbor variance estimation (NNVE) method of Wang and Raftery (2002) <DOI:10.1198/016214502388618780>.

Extends the functionality of base R lists and provides specialized data structures deque', set', dict', and dict.table', the latter to extend the data.table package.

Flexible framework for trait-based simulation of community assembly, where components could be replaced by user-defined function and that allows variation of traits within species.

Compares two dataframes with a common key and returns the delta records. The package will return three dataframes that contain the added, changed, and deleted records.

Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.

Generate SPSS'/'SAS styled frequency tables. Frequency tables are generated with variable and value label attributes where applicable with optional html output to quickly examine datasets.

R interface to access the Vocabularies REST API of the ICES (International Council for the Exploration of the Sea) Vocabularies database <https://vocab.ices.dk/services/>.

This package provides a flexible approach, inspired by cosinor regression, for differential analysis of rhythmic transcriptome data. See Singer and Hughey (2018) <doi:10.1177/0748730418813785>.

Crabs in the English channel, deer skulls, English monarchs, half-caste Manga characters, Jamaican cities, Shakespeare's The Tempest, drugged up cyclists and sexually transmitted diseases.

Data sets exemplifying statistical methods, and some facilitatory utility functions used in ``Analyzing Linguistic Data: A practical introduction to statistics using R'', Cambridge University Press, 2008.

Run Queries against the API of Piwik Pro <https://developers.piwik.pro/en/latest/custom_reports/http_api/http_api.html>. The result is a tibble.

Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. Both regression and classification is supported.