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CAISEr package


CAISEr: Comparing Algorithms with Iterative Sample-size Estimation in R

Author/maintainer:

Contributors:

[1] Department of Computer Science, Aston University, Birmingham UK
[2] Operations Research and Complex Systems Laboratory, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
[3] Department of Computer Science, CEFET-MG, Brazil


Implementation of R package CAISEr, with routines for automatically determining the sample size needed for performing comparative experiments with multiple algorithms on multiple problem instances.

To install the most up-to-date version directly from Github, simply type:

library(devtools)
devtools::install_github("fcampelo/CAISEr")

The most recent CRAN release of the package is also available for installation directly from the R prompt, using:

install.packages("CAISEr")

For instructions and examples of use, please take a look at the vignette Adapting Algorithms for CAISEr, and at the package documentation, particularly functions run_experiment(), run_nreps() and calc_instances().


Please send any bug reports, questions or suggestions directly to the package authors listed at the top of this document: https://github.com/fcampelo/CAISEr/issues

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Version

Install

install.packages('CAISEr')

Monthly Downloads

315

Version

1.0.17

License

GPL-2

Maintainer

Felipe Campelo

Last Published

November 16th, 2022

Functions in CAISEr (1.0.17)

TSP.dist

TSP instance generator (for testing/examples)
get_observations

Run an algorithm on a problem.
consolidate_partial_results

Consolidate results from partial files
example_SANN

Simulated annealing (for testing/examples)
dummyinstance

Dummy instance routine to test the sampling procedures
boot_sdm

Bootstrap the sampling distribution of the mean
calc_instances

Calculates number of instances for the comparison of multiple algorithms
calc_se

Calculates the standard error for simple and percent differences
dummyalgo

Dummy algorithm routine to test the sampling procedures
calc_nreps

Determine sample sizes for a set of algorithms on a single problem instance
se_param

Parametric standard errors
se_boot

Bootstrap standard errors
summary.CAISEr

summary.CAISEr
summary.nreps

summary.nreps
plot.nreps

plot.nreps
print.CAISEr

print.CAISEr
plot.CAISEr

plot.CAISEr
run_experiment

Run a full experiment for comparing multiple algorithms using multiple instances