Package: EloSteepness 0.5.0

EloSteepness: Bayesian Dominance Hierarchy Steepness via Elo Rating and David's Scores

Obtain Bayesian posterior distributions of dominance hierarchy steepness (Neumann and Fischer (2023) <doi:10.1111/2041-210X.14021>). Steepness estimation is based on Bayesian implementations of either Elo-rating or David's scores.

Authors:Christof Neumann [aut, cre]

EloSteepness_0.5.0.tar.gz
EloSteepness_0.5.0.zip(r-4.7)EloSteepness_0.5.0.zip(r-4.6)EloSteepness_0.5.0.zip(r-4.5)
EloSteepness_0.5.0.tgz(r-4.6-x86_64)EloSteepness_0.5.0.tgz(r-4.6-arm64)EloSteepness_0.5.0.tgz(r-4.5-x86_64)EloSteepness_0.5.0.tgz(r-4.5-arm64)
EloSteepness_0.5.0.tar.gz(r-4.7-arm64)EloSteepness_0.5.0.tar.gz(r-4.7-x86_64)EloSteepness_0.5.0.tar.gz(r-4.6-arm64)EloSteepness_0.5.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
EloSteepness/json (API)

# Install 'EloSteepness' in R:

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

Bug tracker:https://github.com/gobbios/elosteepness/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

cpp

3.70 score 1 stars 8 scripts 187 downloads 14 exports 68 dependencies

Last updated from:66c587da26. Checks:12 OK, 1 FAIL. Indexed: yes.
A new build is currently in progress.

Target	Result	Time
linux-devel-arm64	OK	451
linux-devel-x86_64	OK	447
source / vignettes	OK	519
linux-release-arm64	OK	395
linux-release-x86_64	OK	438
macos-release-arm64	OK	392
macos-release-x86_64	OK	635
macos-oldrel-arm64	OK	455
macos-oldrel-x86_64	OK	642
windows-devel	OK	762
windows-release	OK	704
windows-oldrel	OK	722
wasm-release	FAIL	143

Exports:catch_warnings davids_steepness elo_steepness_from_matrix elo_steepness_from_sequence generate_interaction_probs plot_scores plot_steepness plot_steepness_regression remove_dyads repeatability_steepness scores simple_steep_gen steepness_precis upward_steepness

Dependencies:abind aniDom backports BH boot callr checkmate cli coda cpp11 desc distributional EloRating farver generics ggplot2 glue gridExtra gtable inline isoband labeling lattice lifecycle lme4 loo magrittr MASS Matrix matrixStats minqa network nlme nloptr numDeriv pbapply pillar pkgbuild pkgconfig posterior processx ps QuickJSR R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen RcppParallel Rdpack reformulas rlang rptR rstan rstantools S7 scales sna StanHeaders statnet.common tensorA tibble utf8 vctrs viridisLite withr zoo

EloSteepness - a brief tutorial

Rendered fromtutorial.Rmdusingknitr::rmarkdown

Last update: 2023-09-20
Started: 2022-01-21

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
The 'EloSteepness' package.	EloSteepness-package EloSteepness
catch warnings alongside results without returning warning	catch_warnings
David's scores and steepness with Bayesian flavor	davids_steepness
steepness based on Bayesian Elo-rating	elo_steepness_from_matrix
steepness based on Bayesian Elo-rating	elo_steepness_from_sequence
generate dyadic interaction probabilities for a group with fixed individual and dyadic biases	generate_interaction_probs
plot (rather than print) a matrix	plot_matrix
plot posteriors of individual scores	plot_scores
plot steepness density	plot_steepness
plot steepness regression	plot_steepness_regression
prepare data for stan call	prep_data_for_rstan
remove interactions from matrix to increase sparseness	remove_dyads
steepness via repeatability (cf aniDom package)	repeatability_steepness
catch Stan sampling issues without throwing a warning	sampler_diagnostics
numeric summaries of individual scores	scores
generate dominance interactions with specified steepness	simple_steep_gen
numeric summary of steepness	steepness_precis
summary	summary.david_steepness summary.elo_steepness
proportion of interactions against the rank order	upward_steepness