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.

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.

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) .

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.

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.

'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'.

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.