'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.

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.

Quadratic Programming Solver using the 'OSQP' Library

Provides bindings to the 'OSQP' solver. The 'OSQP' solver is a numerical optimization package or solving convex quadratic programs written in 'C' and based on the alternating direction method of multipliers. See for details.

Fast Fixed-Effects Estimations

Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) < https://github.com/lrberge/fixest/blob/master/_DOCS/FENmlm_paper.pdf>. Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.

Evolutionary Learning of Globally Optimal Trees

Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The 'evtree' package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the 'partykit' package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions.

Optimally Robust Estimation

R infrastructure for optimally robust estimation in general smoothly parameterized models using S4 classes and methods as described Kohl, M., Ruckdeschel, P., and Rieder, H. (2010), , and in Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), .

Access and Analyse Data from the 'CVD Prevent' API

Provides an R interface to the 'CVD Prevent' application programming interface (API), allowing users to retrieve and analyse cardiovascular disease prevention data from primary care records across England. The Cardiovascular Disease Prevention Audit (CVDPREVENT) automatically extracts routinely held GP health data to support national reporting and improvement initiatives. See the API documentation for details: < 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.

Tools for Modeling Bumblebee Colony Growth and Decline

Bumblebee colonies grow during worker production, then decline after switching to production of reproductive individuals (drones and gynes). This package provides tools for modeling and visualizing this pattern by identifying a switchpoint with a growth rate before and a decline rate after the switchpoint. The mathematical models fit by bumbl are described in Crone and Williams (2016) .

Infrastructure for Ordering Objects Using Seriation

Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) .