'ECOS' Plugin for the 'R' Optimization Infrastructure

Enhances the 'R' Optimization Infrastructure ('ROI') package with the Embedded Conic Solver ('ECOS') for solving conic optimization problems.

An Alternative Advanced Normalization Tools ('ANTs')

Provides portable access from 'R' to biomedical image processing toolbox 'ANTs' by Avants et al. (2009) via seamless integration with the 'Python' implementation 'ANTsPy'. Allows biomedical images to be processed in 'Python' and analyzed in 'R', and vice versa via shared memory. See 'citation("rpyANTs")' for more reference information.

Random Orthonormal Matrix Generation and Optimization on the Stiefel Manifold

Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" . The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" .

Active Set and Generalized PAVA for Isotone Optimization

Contains two main functions: one for solving general isotone regression problems using the pool-adjacent-violators algorithm (PAVA); another one provides a framework for active set methods for isotone optimization problems with arbitrary order restrictions. Various types of loss functions are prespecified.

Kriging-Based Optimization for Computer Experiments

Efficient Global Optimization (EGO) algorithm as described in "Roustant et al. (2012)" and adaptations for problems with noise ("Picheny and Ginsbourger, 2012") , parallel infill, and problems with constraints.

Genetic Tools for Colony Management

Provides genetic tools for colony management and is a derivation of the work in Amanda Vinson and Michael J Raboin (2015) < https://pmc.ncbi.nlm.nih.gov/articles/PMC4671785/> "A Practical Approach for Designing Breeding Groups to Maximize Genetic Diversity in a Large Colony of Captive Rhesus Macaques ('Macaca' 'mulatto')". It provides a 'Shiny' application with an exposed API. The application supports five groups of functions: (1) Quality control of studbooks contained in text files or 'Excel' workbooks and of pedigrees within 'LabKey' Electronic Health Records (EHR); (2) Creation of pedigrees from a list of animals using the 'LabKey' EHR integration; (3) Creation and display of an age by sex pyramid plot of the living animals within the designated pedigree; (4) Generation of genetic value analysis reports; and (5) Creation of potential breeding groups with and without proscribed sex ratios and defined maximum kinships.

Functions for Optimal Non-Bipartite Matching

Perform non-bipartite matching and matched randomization. A "bipartite" matching utilizes two separate groups, e.g. smokers being matched to nonsmokers or cases being matched to controls. A "non-bipartite" matching creates mates from one big group, e.g. 100 hospitals being randomized for a two-arm cluster randomized trial or 5000 children who have been exposed to various levels of secondhand smoke and are being paired to form a greater exposure vs. lesser exposure comparison. At the core of a non-bipartite matching is a N x N distance matrix for N potential mates. The distance between two units expresses a measure of similarity or quality as mates (the lower the better). The 'gendistance()' and 'distancematrix()' functions assist in creating this. The 'nonbimatch()' function creates the matching that minimizes the total sum of distances between mates; hence, it is referred to as an "optimal" matching. The 'assign.grp()' function aids in performing a matched randomization. Note bipartite matching can be performed using the prevent option in 'gendistance()'.

'Rcpp' Integration for Numerical Computing Libraries

A collection of open source libraries for numerical computing (numerical integration, optimization, etc.) and their integration with 'Rcpp'.

Wrapper for the 'CVD Prevent' Application Programming Interface

Provides an R wrapper to the 'CVD Prevent' application programming interface (API). Users can make API requests through built-in R functions. The Cardiovascular Disease Prevention Audit (CVDPREVENT) is an England-wide primary care audit that automatically extracts routinely held GP health data. < https://bmchealthdocs.atlassian.net/wiki/spaces/CP/pages/317882369/CVDPREVENT+API+Documentation>.

Model-Based Boosting

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. Models and algorithms are described in , a hands-on tutorial is available from . The package allows user-specified loss functions and base-learners.