Tools for Lazy and Non-Standard Evaluation
Functions to capture, inspect, manipulate, and create
lazy values (promises), "..." lists, and active calls.
'arg.R' 'caller.R' 'quo.R' 'dots.R' 'getpromise.R'
'conversions.R' 'format.R' 'force.R' 'shortcut.R' 'missing.R'

A Clean API for Lazy and Non-Standard Evaluation
Facilities to capture, inspect, manipulate, and create
lazy values (promises), "..." lists, and active calls.

Elitist Non-dominated Sorting Genetic Algorithm based on R
This package provide functions for box-constrained
multiobjective optimization using the elitist non-dominated
sorting genetic algorithm - NSGA-II. Fast non-dominated
sorting, crowding distance, tournament selection, simulated
binary crossover, and polynomial mutation are called in the
main program, nsga2R, to complete the search.

Elitist Non-Dominated Sorting Genetic Algorithm
Box-constrained multiobjective optimization using the
elitist non-dominated sorting genetic algorithm - NSGA-II.
Fast non-dominated sorting, crowding distance, tournament
selection, simulated binary crossover, and polynomial
mutation are called in the main program. The methods are
described in Deb et al. (2002) <doi:10.1109/4235.996017>.

An Implementation of Non-Dominated Sorting Genetic Algorithm III
for Feature Selection
An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective
feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm
that solves multiple optimization problems simultaneously by applying a non-dominated sorting
technique. It uses a reference points based selection operator to explore
solution space and preserve diversity. See the original paper by K. Deb and
H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.

A Gaussian process regression using a Gaussian kernel for both
one-sample and two-sample cases. Includes non-stationary Gaussian kernel
(exponential decay function) and several likelihood ratio tests for
differential testing along target points.