Helper functions designed to make dynamically generating R Markdown documents easier by providing a simple and tidy way to create report pieces, shape them to your data, and combine them for exporting into a single R Markdown document.

This package provides datasets and functions for the class "Modelling and Data Analysis for Pharmaceutical Sciences". The datasets can be used to present various methods of data analysis and statistical modeling. Functions for data visualization are also implemented.

Runtime for serving containers that can execute R code on the AWS Lambda serverless compute service <https://aws.amazon.com/lambda/>. Provides the necessary functionality for handling the various endpoints required for accepting new input and sending responses.

Automation tool to run R scripts if needed, based on last modified time. It comes with no package dependencies, organizational overhead, or structural requirements. In short: run an R script if underlying files have changed, otherwise do nothing.

Perform permutation-based hypothesis testing for randomized experiments as suggested in Ludbrook & Dudley (1998) <doi:10.2307/2685470> and Ernst (2004) <doi:10.1214/088342304000000396>, introduced in Pham et al. (2022) <doi:10.1016/j.chemosphere.2022.136736>.

Access the Public Transport Victoria Timetable API <https://www.ptv.vic.gov.au/footer/data-and-reporting/datasets/ptv-timetable-api/>, with results returned as familiar R data structures. Retrieve information on stops, routes, disruptions, departures, and more.

Allows users to derive multi-objective weights from pairwise comparisons, which research shows is more repeatable, transparent, and intuitive other techniques. These weights can be rank existing alternatives or to define a multi-objective utility function for optimization.

This package contains tools for supervised analyses of incomplete, overlapping multiomics datasets. Applies partial least squares in multiple steps to find models that predict survival outcomes. See Yamaguchi et al. (2023) <doi:10.1101/2023.03.10.532096>.

An interface to the Python package stanza <https://stanfordnlp.github.io/stanza/index.html>. stanza is a Python NLP library for many human languages. It contains support for running various accurate natural language processing tools on 60+ languages.

This package provides a fast and adaptable tool to convert photos and images into usable colour schemes for data visualisation. Contains functionality to extract colour palettes from images, as well for the conversion of images between colour spaces.

Identifies the name of the current script in a variety of contexts, e.g. interactively or when sourced. Attempts to support RStudio environment. Based on <https://stackoverflow.com/a/32016824/2292993> and <https://stackoverflow.com/a/35842176/2292993>.

Operationalizes the identification problem of which subset of items should be kept in the shortened version of a said psychometric instrument to best represent the set of items comprised in the original version of the said psychometric instrument.

This package provides functions to search and retrieve the consolidated text of Swedish statutes (Svensk författningssamling, SFS) via the Riksdag's open data API. Part of the lexverse ecosystem of packages for accessing legal and regulatory data.

This package provides tools for computing various vector summaries of persistence diagrams studied in Topological Data Analysis. For improved computational efficiency, all code for the vector summaries is written in C++ using the Rcpp and RcppArmadillo packages.

Tool-set of modules for creating web-based applications that use plot based strategies to visualize and analyze multi-omics data. This package utilizes the shiny and plotly frameworks to provide a user friendly dashboard for interactive plotting.

RapidRAW is a high-performance, non-destructive RAW image editor built with Rust and Tauri. It features GPU-accelerated processing via WGPU, support for various camera RAW formats through the rawler library, and optional AI-powered features using ONNX Runtime.

RepViz enables the view of a genomic region in a simple and efficient way. RepViz allows simultaneous viewing of both intra- and intergroup variation in sequencing counts of the studied conditions, as well as their comparison to the output features (e.g. identified peaks) from user selected data analysis methods.The RepViz tool is primarily designed for chromatin data such as ChIP-seq and ATAC-seq, but can also be used with other sequencing data such as RNA-seq, or combinations of different types of genomic data.

This package infers V-D-J (Variable-Diversity-Joining) haplotypes and gene deletions from AIRR-seq (Adaptive Immune Receptor Repertoire sequencing) data for Ig (Immunoglobulin) and TR (T cell Receptor) chains, based on J (Joining), D (Diversity), or V (Variable) genes as anchor, by adapting a Bayesian framework. It also calculates a Bayes factor, a number that indicates the certainty level of the inference, for each haplotyped gene. Citation: Gidoni, et al (2019) <doi:10.1038/s41467-019-08489-3>. Peres and Gidoni, et al (2019) <doi:10.1093/bioinformatics/btz481>.

redumper is a low-level byte perfect CD disc dumper. It supports incremental dumps, advanced SCSI/C2 repair, intelligent audio CD offset detection, among other features. redumper is also a general purpose DVD/HD-DVD/Blu-ray disc dumper.

Lefser is an implementation in R of the popular "LDA Effect Size" (LEfSe) method for microbiome biomarker discovery. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers of groups and sub-groups.

Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at https://github.com/kharchenkolab/hahmmr/.

This package provides procedures to answer the following questions: How much ram do you need to store a 100,000 by 100,000 matrix? How much ram is your current R session using? How much ram do you even have?

This package provides a functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

Bivariate data interpolation on regular and irregular grids, either linear or using splines are the main part of this package. It is intended to provide replacement functions for the ACM licensed akima::interp and tripack::tri.mesh functions.