This package provides tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included.

Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data.

This package provides a toolbox to read all R files inside a package and automatically generate @importFrom roxygen2 tags in the right place. Includes a shiny application to review the changes before applying them.

The Australian Statistical Geography Standard ('ASGS') is a set of shapefiles by the Australian Bureau of Statistics. This package provides an interface to those shapefiles, as well as methods for converting coordinates to shapefiles.

State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.

This package provides a tool for transforming coordinates in a color space to common color names using data from the Royal Horticultural Society and the International Union for the Protection of New Varieties of Plants.

Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.

Classical cryptography methods for words and brief phrases. Substitution, transposition and concealment (null) ciphers are available, like Caesar, Vigenère, Atbash, affine, simple substitution, Playfair, rail fence, Scytale, single column, bifid, trifid, and Polybius ciphers.

Helps to describe a data frame in hand. Has been developed during PhD work of the maintainer. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.

This package provides a Shiny web application for energy industry analytics. Take an overview of the industry, measure Key Performance Indicators, identify changes in the industry over time, and discover new relationships in the data.

Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2018) <doi:10.1111/2041-210X.12918>. The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation.

It provides a custom ggplot2 geom to add day/night patterns to plots. It visually distinguishes daytime and nighttime periods. It is useful for visualizing data that spans multiple days and for highlighting diurnal patterns.

This package provides a variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more.

Generate a stream of pseudo-random numbers generated using the MLS Junk Generator algorithm. Functions exist to generate single pseudo-random numbers as well as a vector, data frame, or matrix of pseudo-random numbers.

Enables the creation of Moodle quiz questions using literate programming with R Markdown. This makes it easy to quickly create a quiz that can be randomly replicated with new datasets, questions, and options for answers.

Given a vector of multivariate normal data, a matrix of covariates and the data covariance matrix, generate new multivariate normal samples that have the same covariance matrix based on permutations of the transformed data residuals.

Implementation of models for the controlled introduction of errors in classification datasets. This package contains the noise models described in Saez (2022) <doi:10.3390/math10203736> that allow corrupting class labels, attributes and both simultaneously.

Bindings for Poisson regression models for use with the parsnip package. Models include simple generalized linear models, Bayesian models, and zero-inflated Poisson models (Zeileis, Kleiber, and Jackman (2008) <doi:10.18637/jss.v027.i08>).

This package provides useful UI components and input widgets for Shiny applications. The offered components allow to apply non-standard operations and view to your Shiny application, but also help to overcome common performance issues.

Estimates sparse regression models (i.e., with few non-zero coefficients) in high-dimensional multi-task learning and transfer learning settings, as proposed by Rauschenberger et al. (2025) <https://orbilu.uni.lu/handle/10993/63425>.

This package performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI).

This package provides a unified estimation procedure for the analysis of right censored data using linear transformation models. An introduction can be found in Jie Zhou et al. (2022) <doi:10.18637/jss.v101.i09>.

This package provides wrapper functions to the multiple marginal model function mmm() of package multcomp to implement the trend test of Tukey, Ciminera and Heyse (1985) <DOI:10.2307/2530666> for general parametric models.

This package provides functions for metrics and plots for model evaluation. Based on vectors of observed and predicted values. Method: Kristin Piikki, Johanna Wetterlind, Mats Soderstrom and Bo Stenberg (2021). <doi:10.1111/SUM.12694>.