GPU Functions for R Objects

Provides GPU enabled functions for 'R' objects in a simple and approachable manner. New 'gpu*' and 'vcl*' classes have been provided to wrap typical 'R' objects (e.g. vector, matrix), in both host and device spaces, to mirror typical 'R' syntax without the need to know 'OpenCL'.

Maximal Biclique Enumeration in Bipartite Graphs

A tool for enumerating maximal complete bipartite graphs. The input should be a edge list file or a binary matrix file. The output are maximal complete bipartite graphs. Algorithms used can be found in this paper Y. Lu et al. BMC Res Notes 13, 88 (2020) .

Perform Forward and Backwards Chasing in Evidence Syntheses

In searching for research articles, we often want to obtain lists of references from across studies, and also obtain lists of articles that cite a particular study. In systematic reviews, this supplementary search technique is known as 'citation chasing': forward citation chasing looks for all records citing one or more articles of known relevance; backward citation chasing looks for all records referenced in one or more articles. Traditionally, this process would be done manually, and the resulting records would need to be checked one-by-one against included studies in a review to identify potentially relevant records that should be included in a review. This package contains functions to automate this process by making use of the Lens.org API. An input article list can be used to return a list of all referenced records, and/or all citing records in the Lens.org database (consisting of PubMed, PubMed Central, CrossRef, Microsoft Academic Graph and CORE; < https://www.lens.org>).

Segmentation and Classification of Accelerometer Data

Segmentation and classification procedures for data from the 'Activinsights GENEActiv' < https://activinsights.com/technology/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing. Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models.

Probability-Scale Residuals and Residual Correlations

Computes probability-scale residuals and residual correlations for continuous, ordinal, binary, count, and time-to-event data Qi Liu, Bryan Shepherd, Chun Li (2020) .

Advanced 'tryCatch()' and 'try()' Functions

Advanced tryCatch() and try() functions for better error handling (logging, stack trace with source code references and support for post-mortem analysis via dump files).

Meta-Population Compartmental Model for Respiratory Virus Diseases

Simulates respiratory virus epidemics using meta-population compartmental models following Fadikar et. al. (2025) . 'MetaRVM' implements a stochastic SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) framework with demographic stratification by age, race, and geographic zones. It supports complex epidemiological scenarios including asymptomatic and presymptomatic transmission, hospitalization dynamics, vaccination schedules, and time-varying contact patterns via mixing matrices.

CEU Mass Mediator RESTful API

CEU (CEU San Pablo University) Mass Mediator is an on-line tool for aiding researchers in performing metabolite annotation. 'cmmr' (CEU Mass Mediator RESTful API) allows for programmatic access in R: batch search, batch advanced search, MS/MS (tandem mass spectrometry) search, etc. For more information about the API Endpoint please go to < https://github.com/YaoxiangLi/cmmr>.

Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGS Optimization

Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the 'Scilab' optimization documentation located at < https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from 'f2c' to make this a C only binary.

Power Analysis Across a Grid of Assumptions

Evaluate a function across a grid of parameters. The function may be evaluated once, or many times for simulation. Parallel computing is facilitated. Utilities aim at performing analyses of power and sample size, allowing for easy search of minimum n (or min/max of any other parameter) to achieve a desired minimal level of power (or maximum of any other objective). Plotting functions are included that present the dependency of n and power in relation to further assumptions.