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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.
'ROI' Plug-in 'GLPK'
Enhances the 'R' Optimization Infrastructure ('ROI') package by registering the free 'GLPK' solver. It allows for solving mixed integer linear programming ('MILP') problems as well as all variants/combinations of 'LP', 'IP'.
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)
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'.
Trust Region Optimization for Nonlinear Functions with Sparse Hessians
Trust region algorithm for nonlinear optimization. Efficient when
the Hessian of the objective function is sparse (i.e., relatively few nonzero
cross-partial derivatives). See Braun, M. (2014)
Plots for Visualizing the Data Produced by the 'irace' Package
Graphical visualization tools for analyzing the data produced by 'irace'. The 'iraceplot' package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of 'irace' and their performance. The functions just require the log file generated by 'irace' and, in some cases, they can be used with user-provided data.
L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model
Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.
Sequential Parameter Optimization Toolbox
A set of tools for model-based optimization and tuning of algorithms (hyperparameter tuning respectively hyperparameter optimization). It includes surrogate models, optimizers, and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources.
Unconstrained Numerical Optimization Algorithms
Optimization algorithms implemented in R, including conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS) methods. Most internal parameters can be set through the call interface. The solvers hold up quite well for higher-dimensional problems.