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.

Declare and Diagnose Research Designs

Researchers can characterize and learn about the properties of research designs before implementation using `DeclareDesign`. Ex ante declaration and diagnosis of designs can help researchers clarify the strengths and limitations of their designs and to improve their properties, and can help readers evaluate a research strategy prior to implementation and without access to results. It can also make it easier for designs to be shared, replicated, and critiqued.

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) .

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.

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) . (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at < https://www.gurobi.com>.)

'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'.

Mixed Variable Optimization

Mixed variable optimization for non-linear functions. Can optimize function whose inputs are a combination of continuous, ordered, and unordered variables.

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.

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.