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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)
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),
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.
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)
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
Parallel Bayesian Optimization of Hyperparameters
Fast, flexible framework for implementing Bayesian optimization of model
hyperparameters according to the methods described in Snoek et al.
Highly Optimized Protocol Buffer Serializers
Pure C++ implementations for reading and writing several common data formats based on Google protocol-buffers. Currently supports 'rexp.proto' for serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, hence the entire serialization is optimized at compile time. The 'RProtoBuf' package on the other hand uses the protobuf runtime library to provide a general- purpose toolkit for reading and writing arbitrary protocol-buffer data in R.
Machine Learning in R
Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.
Tools for Matrix Algebra, Optimization and Inference
Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).
'lp_solve' Plugin for the 'R' Optimization Infrastructure
Enhances the 'R' Optimization Infrastructure ('ROI') package with the 'lp_solve' solver.