Package: evolvability 2.0.0

evolvability: Calculation of Evolvability Parameters

Provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.

Authors:Geir H. Bolstad [aut, cre]

evolvability_2.0.0.tar.gz
evolvability_2.0.0.zip(r-4.5)evolvability_2.0.0.zip(r-4.4)evolvability_2.0.0.zip(r-4.3)
evolvability_2.0.0.tgz(r-4.4-any)evolvability_2.0.0.tgz(r-4.3-any)
evolvability_2.0.0.tar.gz(r-4.5-noble)evolvability_2.0.0.tar.gz(r-4.4-noble)
evolvability_2.0.0.tgz(r-4.4-emscripten)evolvability_2.0.0.tgz(r-4.3-emscripten)
evolvability.pdf |evolvability.html✨
evolvability/json (API)

# Install 'evolvability' in R:
install.packages('evolvability', repos = c('https://ghbolstad.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/ghbolstad/evolvability/issues

On CRAN:

4.20 score 16 scripts 217 downloads 1 mentions 22 exports 13 dependencies

Last updated 6 months agofrom:ffdb625508. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 23 2024
R-4.5-winNOTENov 23 2024
R-4.5-linuxNOTENov 23 2024
R-4.4-winOKNov 23 2024
R-4.4-macOKNov 23 2024
R-4.3-winOKNov 23 2024
R-4.3-macOKNov 23 2024

Exports:AlmerAlmer_bootAlmer_SEAlmer_simconditionalGevolvabilityBetaevolvabilityBetaMCMCevolvabilityBetaMCMC2evolvabilityMeansevolvabilityMeansMCMCGLSmacro_predmeanStdGmeanStdGMCMCphylHrandomBetarate_glsrate_gls_bootrate_gls_simresponsediffBetaround_and_formatsimulate_rate

Dependencies:apebootcodadigestlatticelme4MASSMatrixminqanlmenloptrRcppRcppEigen

Analyzing rates of evolution

Geir H. Bolstad

Rendered fromAnalyzing_rates_of_evolution.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2021-12-08
Started: 2020-04-21

Phylogenetic mixed model

Geir H. Bolstad

Rendered fromPhylogenetic_mixed_model.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2021-01-04
Started: 2018-12-06

Readme and manuals

Help Manual

Help pageTopics
Calculation of Evolvability Parametersevolvability-package evolvability
Linear mixed model with correlated random effects structureAlmer
Parametric bootstrap on 'Almer' model fitAlmer_boot
Linear mixed model for response variables with uncertaintyAlmer_SE
Simulate responses from 'Almer' fitAlmer_sim
Computing a conditional sub-matrix of GconditionalG
Calculate evolvability parameters along a set of selection gradientsevolvabilityBeta
Calculate posterior distribution of evolvability parameters from a set of selection gradientsevolvabilityBetaMCMC
Calculate posterior distribution of evolvability parameters from a selection gradient estimated with uncertaintyevolvabilityBetaMCMC2
Calculate average evolvability parameters of a G-matrixevolvabilityMeans
Calculate posterior distribution of average evolvability parameters of a G-matrixevolvabilityMeansMCMC
Generalized least squareGLS
Macroevolutionary predictionsmacro_pred
Mean standardize a variance matrixmeanStdG
Mean standardize the posterior distribution of a G-matrixmeanStdGMCMC
Phylogenetic heritabilityphylH
Plot of rate_gls objectplot.rate_gls
Plot of simulate_rate objectplot.simulate_rate
Generating selection gradients/vectors in random directions.randomBeta
Generalized least squares rate modelrate_gls
Bootstrap of the 'rate_gls' model fitrate_gls_boot
Simulate responses from 'rate_gls' fitrate_gls_sim
Calculate response differences along a set of selection gradientsresponsediffBeta
Rounds and formats in the same functionround_and_format
Simulating evolutionary rate modelsimulate_rate
Summarizing evolvability parameters over a set of selection gradientssummary.evolvabilityBeta
Summarizing posterior distribution of evolvability parameters over a set of selection gradientssummary.evolvabilityBetaMCMC
Summarizing response differences over a set of selection gradientssummary.responsediffBeta