Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported.

Data sets for Chihara and Hesterberg (2022, ISBN: 978-1-119-87404-1) "Mathematical Statistics with Resampling in R" (3rd Ed).

Immutable extensions to objects

Memoize method return values

Instantiate one with `redis = MockRedis.new` and treat it like you would a normal Redis object. It supports all the usual Redis operations.

Data driven approach for robust regression estimation in homoscedastic and heteroscedastic context. See Wang et al. (2007), <doi:10.1198/106186007X180156> regarding homoscedastic framework.

Documentation at https://melpa.org/#/ropgadget

Documentation at https://melpa.org/#/requirejs

Resizable dockable react component

This package provides the means to compile user-supplied C++ functions with Rcpp and retrieve an XPtr that can be passed to other C++ components.

Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.

Build regular expressions piece by piece using human readable code. This package contains Unicode functionality, and is primarily intended to be used by package developers.

Flamegraph support for arbitrary ruby apps

This package provides telnet client functionality.

Detect outliers in one-dimensional data.

Build regular expressions piece by piece using human readable code. This package contains number-related functionality, and is primarily intended to be used by package developers.

This package provides an infrastructure for handling multiple R Markdown reports, including automated curation and time-stamping of outputs, parameterisation and provision of helper functions to manage dependencies.

This package provides a set of functions to see and interactively adjust a distribution of lessons by day, aiming at homogenizing individual distributions (for each class and teacher).

Aggregates multiple Receiver Operating Characteristic (ROC) curves obtained from different sources into one global ROC. Additionally, itâ s also possible to calculate the aggregated precision-recall (PR) curve.

Documentation at https://melpa.org/#/rego-mode

Documentation at https://melpa.org/#/ring-mode

Documentation at https://melpa.org/#/raku-mode

Documentation at https://melpa.org/#/r-autoyas

Documentation at https://melpa.org/#/rust-mode