Package: mmodely 0.2.5
mmodely: Modeling Multivariate Origins Determinants - Evolutionary Lineages in Ecology
Perform multivariate modeling of evolved traits, with special attention to understanding the interplay of the multi-factorial determinants of their origins in complex ecological settings (Stephens, 2007 <doi:10.1016/j.tree.2006.12.003>). This software primarily concentrates on phylogenetic regression analysis, enabling implementation of tree transformation averaging and visualization functionality. Functions additionally support information theoretic approaches (Grueber, 2011 <doi:10.1111/j.1420-9101.2010.02210.x>; Garamszegi, 2011 <doi:10.1007/s00265-010-1028-7>) such as model averaging and selection of phylogenetic models. Accessory functions are also implemented for coef standardization (Cade 2015), selection uncertainty, and variable importance (Burnham & Anderson 2000). There are other numerous functions for visualizing confounded variables, plotting phylogenetic trees, as well as reporting and exporting modeling results. Lastly, as challenges to ecology are inherently multifarious, and therefore often multi-dataset, this package features several functions to support the identification, interpolation, merging, and updating of missing data and outdated nomenclature.
Authors:
mmodely_0.2.5.tar.gz
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mmodely.pdf |mmodely.html✨
mmodely/json (API)
NEWS
# Install 'mmodely' in R: |
install.packages('mmodely', repos = c('https://dranthropoid.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:b434e85c5b. Checks:OK: 3 NOTE: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 07 2024 |
R-4.5-win | NOTE | Nov 07 2024 |
R-4.5-linux | NOTE | Nov 07 2024 |
R-4.4-win | NOTE | Nov 07 2024 |
R-4.4-mac | NOTE | Nov 07 2024 |
R-4.3-win | OK | Nov 07 2024 |
R-4.3-mac | OK | Nov 07 2024 |
Exports:average.fit.modelscalc.q2n.ratioceptcomp.datacompare.data.gs.vs.tree.tipscorrect.AICcount.mod.varsct.possible.modelsdrop.na.datafit.1ln.rprtget.mod.clmnsget.mod.outcomeget.mod.varsget.model.combosget.pgls.coefsget.phylo.statsgs.checkgs.names.mismatch.checkgs.renameinterpolatemissing.datamissing.fill.inpgls.iterpgls.iter.statspgls.printpgls.reportpgls.wrapplot.confound.gridplot.pgls.itersplot.pgls.R2AICplot.transformed.phyloplot.xy.ab.pselect.best.modelssparge.modseltrim.phylovariable.importanceweight.IC
Dependencies:apecapercarolinedigestlatticeMASSmvtnormnlmeRcpp
Information-theoretic PGLS model selection workflow: a primate vision example
Rendered fromSchruth-mmodely-vignette-Vision.Rnw
usingutils::Sweave
on Nov 07 2024.Last update: 2023-05-17
Started: 2022-11-04
Information-theoretic PGLS model selection workflow: a primate vocal communication example
Rendered fromSchruth-mmodely-vignette-Vocal.Rnw
usingutils::Sweave
on Nov 07 2024.Last update: 2023-05-17
Started: 2022-11-04