Package: CAISEr 1.0.17

CAISEr: Comparison of Algorithms with Iterative Sample Size Estimation

Functions for performing experimental comparisons of algorithms using adequate sample sizes for power and accuracy. Implements the methodology originally presented in Campelo and Takahashi (2019) <doi:10.1007/s10732-018-9396-7> for the comparison of two algorithms, and later generalised in Campelo and Wanner (Submitted, 2019) <arxiv:1908.01720>.

Authors:Felipe Campelo [aut, cre], Fernanda Takahashi [ctb], Elizabeth Wanner [ctb]

CAISEr_1.0.17.tar.gz
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CAISEr_1.0.17.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CAISEr/json (API)

# Install 'CAISEr' in R:

install.packages('CAISEr', repos = c('https://fcampelo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fcampelo/caiser/issues

Pkgdown/docs site:https://fcampelo.github.io

On CRAN:

4.56 score 3 stars 24 scripts 434 downloads 13 exports 20 dependencies

Last updated from:5d4210b6b9. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	139
source / vignettes	OK	179
linux-release	OK	130
macos-release	OK	119
macos-oldrel	OK	108
windows-devel	OK	133
windows-release	OK	110
windows-oldrel	OK	66
wasm-release	OK	105

Exports:boot_sdm calc_instances calc_nreps calc_se consolidate_partial_results dummyalgo dummyinstance example_SANN get_observations run_experiment se_boot se_param TSP.dist

Dependencies:assertthat cli cpp11 farver ggplot2 glue gridExtra gtable isoband labeling lifecycle pbmcapply R6 RColorBrewer rlang S7 scales vctrs viridisLite withr

Adapting Algorithms for CAISEr

Introduction | Assembling an instance list | Adaptation of an existing algorithm implementation | Running an experiment using CAISEr

Rendered fromAdapting_Algorithm_for_CAISEr.Rmdusingknitr::rmarkdown

Last update: 2022-11-16
Started: 2017-10-16

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Bootstrap the sampling distribution of the mean	boot_sdm
Calculates number of instances for the comparison of multiple algorithms	calc_instances
Determine sample sizes for a set of algorithms on a single problem instance	calc_nreps
Calculates the standard error for simple and percent differences	calc_se
Consolidate results from partial files	consolidate_partial_results
Dummy algorithm routine to test the sampling procedures	dummyalgo
Dummy instance routine to test the sampling procedures	dummyinstance
Simulated annealing (for testing/examples)	example_SANN
Run an algorithm on a problem.	get_observations
plot.CAISEr	plot.CAISEr
plot.nreps	plot.nreps
print.CAISEr	print.CAISEr
Run a full experiment for comparing multiple algorithms using multiple instances	run_experiment
Bootstrap standard errors	se_boot
Parametric standard errors	se_param
summary.CAISEr	summary.CAISEr
summary.nreps	summary.nreps
TSP instance generator (for testing/examples)	TSP.dist