Determine the number of arguments of a function
Determine the number of arguments of a primitive function
Tools for manipulating boolean functions
Breeding of GP individuals
Building block tags
Support for GP buidling blocks
A 'dist' function that supports custom metrics
Data-driven untyped standard genetic programming
Default function- and constant factory sets for Genetic Programming
A variant of do.call that ignores unused arguments
Embed columns in a data frame
Evolution restart conditions
Evolution restart strategies
Evolution stop conditions
Return the Children of an Expression or the Empty List if there are None
Return the "label" at the Root Node of an Expression Tree
Convert any expression to an expression that is plottable by plotmath
Complexity measures for R functions and expressions
Functions for decomposing and recombining R expressions
Upper bounds for expression tree search space sizes
Random crossover (recombination) of functions and expressions
Random mutation of functions and expressions
Functions for handling R symbols / names
Similarity and Distance Measures for R Functions and Expressions
Common higher-order functions for transforming R expressions
Extract a given attribute of all objects in a list and tag that list with the list of extracted attributes
Format time and data values into human-readable character vectors
Convert a function to an expression plottable by plotmath
Standard typed and untyped genetic programming
Visualization of functions and expressions as trees
Create a regular grid design matrix
Functions for analysing GP individuals
Calculate the inverse of a permutation
Check if an object is an sType
Repeatedly apply a function
Join elite lists
Create a latin hypercube design (LHD)
Functions for Lisp-like list processing
Splitting and grouping of lists
Mean absolute error (MAE)
Age Fitness Complexity Pareto GP Search Heuristic for RGP
Archive-based Pareto Tournament Search Heuristic for RGP
Create a new R closure given a function body expression and an argument list
Comma Evolution Strategy Search Heuristic for RGP
Create a fitness function from a reference function of one variable
Create a fitness function from a n-ary reference function
Create a fitness function for symbolic regression
Create a fitness function based on symbolic squared error (SE)
Tiny GP Search Heuristic for RGP
Mean squared error (MSE)
Cluster-based multi-niche genetic programming
Symbolic regression via multi-niche standard genetic programming
Create a new function argument list from a list or vector of strings
Create a new function stub
Normalized mean squared error (NMSE)
Create a nondeterministic ranking
Normalize a vector into the interval [0, 1]
Create a normalized design matrix
Rearrange points via an arbitrary Pareto-based ranking
Find the knee of a two dimensional pareto front
Rearrange points via Pareto-based rankings
Plot a 2D function as a 3D surface
Show an overlayed plot of multiple functions
Plot a GP Pareto Front
Fitness/Complexity plot for populations
Calculate the fitness value of each individual in a population
Clustering Populations for Niching
Classes for populations of individuals represented as functions
Predict method for symbolic regression models
Prints a sType and returns it invisible.
R version of Mean absolute error (MAE)
R version of Sum squared error (SSE)
R version of Scaled sum squared error (sSSE)
Choose a random element from a list or vector
Select random childs or subtrees of an expression
Creates an R expression by random growth
Creates an R expression by random growth respecting type constraints
Creates an R function with a random expression as its body
Creates a well-typed R function with a random expression as its body
Create a random terminal node
Return the range type if t is a function type, otherwise just return t
The RGP package
Utility functions for testing and benchmarking the RGP system
Root mean squared error (RMSE)
Coefficient of determination (R^2)
Type constructors for types in the Rsymbolic type system
Inference of sTypes
Some simple arithmetic and logic functions for use in GP expressions
Symbolic squared error (SE)
Symbolic squared error function (SE)
Functions for defining the search space for Genetic Programming
GP selection functions
Scaled mean squared error (SMSE)
Sort a vector or list by the result of applying a function
Tabulate a list of functions or input variables by the range part of their sTypes
Sort a vector or list via a given ranking
Tabulate a list of functions or input variables by their sTypes
Sorting algorithms for vectors and lists
Sum squared error (SSE)
Scaled sum squared error (sSSE)
Select a continuous subframe of a data frame
Summary reports of genetic programming run result objects
Symbolic regression via untyped standard genetic programming
Tabulate an n-ary function
Variable Presence Maps