Package: airt 0.2.3

Sevvandi Kandanaarachchi

airt: Evaluation of Algorithm Collections Using Item Response Theory

An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.

Authors:Sevvandi Kandanaarachchi [aut, cre]

airt_0.2.3.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
airt/json (API)

# Install 'airt' in R:

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

Bug tracker:https://github.com/sevvandi/airt/issues

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

Datasets:

classification_cts - A dataset containing classification algorithm performance data in a continuous format.
classification_poly - A dataset containing classification algorithm performance data in a polytomous format.

On CRAN:

4.18 score 1 packages 9 scripts 394 downloads 15 exports 118 dependencies

Last updated from:ff32b1c20b. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	188
source / vignettes	OK	237
linux-release-x86_64	OK	192
macos-release-arm64	OK	135
macos-oldrel-arm64	OK	127
windows-devel	OK	155
windows-release	OK	154
windows-oldrel	OK	160
wasm-release	OK	135

Exports:algo_effectiveness_crm algo_effectiveness_poly autoplot cirtmodel effectiveness_crm effectiveness_poly heatmaps_crm latent_trait_analysis make_polyIRT_data model_goodness_crm model_goodness_for_algo_crm model_goodness_for_algo_poly model_goodness_poly pirtmodel tracelines_poly

Dependencies:audio backports base64enc beepr brio bslib cachem callr checkmate class cli clipr cluster codetools colorspace cpp11 crayon data.table dcurver Deriv desc diffobj digest dplyr e1071 EstCRM evaluate farver fastmap fontawesome foreign Formula fs future future.apply generics ggplot2 globals glue GPArotation gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets isoband jquerylib jsonlite knitr labeling lattice lifecycle listenv magrittr MASS Matrix memoise mgcv mime mirai mirt nanonext nlme nnet otel parallelly pbapply permute pillar pkgbuild pkgconfig pkgload pracma praise processx progressr proxy ps purrr qs2 R.methodsS3 R.oo R.utils R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppParallel rlang rmarkdown rpart rprojroot rstudioapi S7 sass scales sessioninfo SimDesign splines2 stringfish stringi stringr testthat tibble tidyr tidyselect tinytex utf8 vctrs vegan viridisLite waldo withr xfun yaml

Introduction to airt

Rendered fromairt.Rmdusingknitr::rmarkdown

Last update: 2024-03-21
Started: 2020-03-22

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Computes the actual and predicted effectiveness of a given algorithm.	algo_effectiveness_crm
Computes the actual and predicted effectiveness of a given algorithm.	algo_effectiveness_poly
Fits a continuous IRT model.	cirtmodel
A dataset containing classification algorithm performance data in a continuous format.	classification_cts
A dataset containing classification algorithm performance data in a polytomous format.	classification_poly
Computes the actual and predicted effectiveness of the collection of algorithms.	autoplot.effectivenesscrm effectiveness_crm
Computes the actual and predicted effectiveness of the collection of algorithms.	autoplot.effectivenesspoly effectiveness_poly
Function to produce heatmaps from a continuous IRTmodel	autoplot.heatmapcrm heatmaps_crm
Performs the latent trait analysis	autoplot.latenttrait latent_trait_analysis
Converts continuous performance data to polytomous data with 5 categories.	make_polyIRT_data
Computes the goodness of IRT model for all algorithms.	autoplot.modelgoodnesscrm model_goodness_crm
Computes the goodness of IRT model for a given algorithm.	model_goodness_for_algo_crm
Computes the goodness of the IRT model fit for a given algorithm.	model_goodness_for_algo_poly
Computes the goodness of IRT model for all algorithms.	autoplot.modelgoodnesspoly model_goodness_poly
Fits a polytomous IRT model.	pirtmodel
Function to plot tracelines from a polytomous IRTmodel	autoplot.tracelinespoly tracelines_poly