Package: abcrf 2.0

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>. 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]

abcrf_2.0.tar.gz
abcrf_2.0.zip(r-4.7-x86_64)abcrf_2.0.zip(r-4.7-arm64)abcrf_2.0.zip(r-4.6-x86_64)abcrf_2.0.zip(r-4.6-arm64)abcrf_2.0.zip(r-4.5-x86_64)
abcrf_2.0.tgz(r-4.6-x86_64)abcrf_2.0.tgz(r-4.6-arm64)abcrf_2.0.tgz(r-4.5-x86_64)abcrf_2.0.tgz(r-4.5-arm64)
abcrf_2.0.tar.gz(r-4.7-arm64)abcrf_2.0.tar.gz(r-4.7-x86_64)abcrf_2.0.tar.gz(r-4.6-arm64)abcrf_2.0.tar.gz(r-4.6-x86_64)
abcrf_2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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:

cpp

4.81 score 2 stars 108 scripts 618 downloads 6 mentions 9 exports 38 dependencies

Last updated from:3a83b7b4e6. Checks:13 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	WARNING	180
linux-devel-x86_64	WARNING	170
source / vignettes	OK	176
linux-release-arm64	WARNING	122
linux-release-x86_64	WARNING	153
macos-release-arm64	WARNING	188
macos-release-x86_64	WARNING	427
macos-oldrel-arm64	WARNING	157
macos-oldrel-x86_64	WARNING	226
windows-devel-arm64	WARNING	386
windows-devel-x86_64	WARNING	175
windows-release-arm64	WARNING	381
windows-release-x86_64	WARNING	152
windows-oldrel-x86_64	WARNING	135
wasm-release	OK	112

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

Dependencies:bit bit64 cli clipr codetools cpp11 crayon doParallel 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