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A Solver for 'ompr' that Uses the R Optimization Infrastructure ('ROI')
A solver for 'ompr' based on the R Optimization Infrastructure ('ROI'). The package makes all solvers in 'ROI' available to solve 'ompr' models. Please see the 'ompr' website < https://dirkschumacher.github.io/ompr/> and package docs for more information and examples on how to use it.
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.
Rmetrics - Portfolio Selection and Optimization
A collection of functions to optimize portfolios and to analyze them from different points of view.
Determining the Best Number of Clusters in a Data Set
It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
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).
Nonparametric Preprocessing for Parametric Causal Inference
Selects matched samples of the original treated and
control groups with similar covariate distributions -- can be
used to match exactly on covariates, to match on propensity
scores, or perform a variety of other matching procedures. The
package also implements a series of recommendations offered in
Ho, Imai, King, and Stuart (2007)
'lp_solve' Plugin for the 'R' Optimization Infrastructure
Enhances the 'R' Optimization Infrastructure ('ROI') package with the 'lp_solve' solver.
Linear, Quadratic, and Rational Optimization
Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. A unified interface to different R packages is provided. Optimization problems are transformed into equivalent formulations and solved by the respective package. For example, quadratic programming problems with linear, quadratic and rational constraints can be solved by augmented Lagrangian minimization using package 'alabama', or by sequential quadratic programming using solver 'slsqp'. Alternatively, they can be reformulated as optimization problems with second order cone constraints and solved with package 'cccp'.