Package: abcrf 1.9

abcrf: Approximate Bayesian Computation via Random Forests

Performs Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests. Pudlo P., Marin J.-M., Estoup A., Cornuet J.-M., Gautier M. and Robert C. P. (2016) <doi:10.1093/bioinformatics/btv684>. Estoup A., Raynal L., Verdu P. and Marin J.-M. <http://journal-sfds.fr/article/view/709>. Raynal L., Marin J.-M., Pudlo P., Ribatet M., Robert C. P. and Estoup A. (2019) <doi:10.1093/bioinformatics/bty867>.

Authors:Jean-Michel Marin [aut, cre], Louis Raynal [aut], Pierre Pudlo [aut], Christian P. Robert [ctb], Arnaud Estoup [ctb]Config/pak/sysreqs: libicu-dev libx11-dev

abcrf_1.9.tar.gz
abcrf_1.9.zip(r-4.5)abcrf_1.9.zip(r-4.4)abcrf_1.9.zip(r-4.3)
abcrf_1.9.tgz(r-4.4-x86_64)abcrf_1.9.tgz(r-4.4-arm64)abcrf_1.9.tgz(r-4.3-x86_64)abcrf_1.9.tgz(r-4.3-arm64)
abcrf_1.9.tar.gz(r-4.5-noble)abcrf_1.9.tar.gz(r-4.4-noble)
abcrf_1.9.tgz(r-4.4-emscripten)abcrf_1.9.tgz(r-4.3-emscripten)
abcrf.pdf |abcrf.html✨
abcrf/json (API)

# Install 'abcrf' in R:

install.packages('abcrf', repos = c('https://jmm34.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jmm34/abcrf/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

snp - A simulated example in population genetics
snp.obs - A simulated example in population genetics

On CRAN:

4.64 score 2 stars 72 scripts 854 downloads 6 mentions 9 exports 39 dependencies

Last updated 2 years agofrom:24a28e87d3. Checks:OK: 1 WARNING: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 09 2024
R-4.5-win-x86_64	WARNING	Nov 09 2024
R-4.5-linux-x86_64	WARNING	Nov 09 2024
R-4.4-win-x86_64	WARNING	Nov 09 2024
R-4.4-mac-x86_64	WARNING	Nov 09 2024
R-4.4-mac-aarch64	WARNING	Nov 09 2024
R-4.3-win-x86_64	WARNING	Nov 09 2024
R-4.3-mac-x86_64	WARNING	Nov 09 2024
R-4.3-mac-aarch64	WARNING	Nov 09 2024

Exports:abcrf covRegAbcrf densityPlot err.abcrf err.regAbcrf predictOOB readRefTable regAbcrf variableImpPlot

Dependencies:bit bit64 cli clipr codetools cpp11 crayon doParallel fansi foreach glue hms iterators lattice lifecycle magrittr MASS Matrix matrixStats pillar pkgconfig prettyunits progress R6 ranger Rcpp RcppArmadillo RcppEigen readr rlang stringi stringr tibble tidyselect tzdb utf8 vctrs vroom withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Create an ABC-RF object: a classification random forest from a reference table towards performing an ABC model choice	abcrf abcrf.formula
Predict posterior covariance between two parameters for new data using two reg-ABC-RF objects	covRegAbcrf covRegAbcrf.regAbcrf
Plot the posterior density given a new summary statistic	densityPlot densityPlot.regAbcrf
Calculate and plot for different numbers of tree, the out-of-bag errors associated with an ABC-RF object	err.abcrf
Calculate and plot for different numbers of tree, the out-of-bag mean squared errors associated with a REG-ABC-RF object	err.regAbcrf
Plot of an ABC-RF object	plot.abcrf
Plot of a reg-ABC-RF object	plot.regAbcrf
Predict and evaluate the posterior probability of the MAP for new data using an ABC-RF object	predict.abcrf
Predict posterior expectation, median, variance and quantiles given a new dataset using a reg-ABC-RF object	predict.regAbcrf
Predict out-of-bag posterior expectation, median, variance, quantiles and error measures using a reg-ABC-RF object	predictOOB predictOOB.regAbcrf
Read a reference table simulated from DIYABC	readRefTable
Create a reg-ABC-RF object: a regression random forest from a reference table aimed out predicting posterior expectation, variance and quantiles for a parameter	regAbcrf regAbcrf.formula
A simulated example in population genetics	snp snp.obs
Variable importance plot from a random forest	variableImpPlot