Maintainer: Kevin M. Middleton <kmm@csusb.edu>
Description: The abd package contains data sets and sample code for The
Analysis of Biological Data by Michael Whitlock and Dolph
Schluter (2009; Roberts & Company Publishers).

Suggests: boot, car, ggplot2, plyr, HH, vcd, Hmisc

Depends: R (>= 2.10), nlme, lattice, grid, mosaic
Suggests: boot, car, ggplot2, plyr, HH, ICC, vcd, Hmisc, manipulate
Collate: 'abdData.R' 'histochart.R' 'datasets.R' 'themes.R'

Author: Kevin M. Middleton <middletonk@missouri.edu>, Randall Pruim
Maintainer: Kevin M. Middleton <middletonk@missouri.edu>

Depends: R (>= 3.0), nlme, lattice, grid, mosaic
Analysis of Biological Data by Michael Whitlock and Dolph Schluter (2009;
Roberts & Company Publishers). Some examples utilize the manipulate package,
which is available within RStudio (http://www.rstudio.com).

Suggests: boot, car, ggplot2, plyr, HH, ICC, vcd, Hmisc

Title: Alpha and Beta Diversity Measures
Authors@R: person("Kyle", "Bittinger", email = "kylebittinger@gmail.com",
Description: A collection of measures for measuring ecological diversity.
Ecological diversity comes in two flavors: alpha diversity measures the
diversity within a single site or sample, and beta diversity measures the
diversity across two sites or samples. This package overlaps considerably
with other R packages such as 'vegan', 'gUniFrac', 'betapart', and 'fossil'.
We also include a wide range of functions that are implemented in software
outside the R ecosystem, such as 'scipy', 'Mothur', and 'scikit-bio'.  The
implementations here are designed to be basic and clear to the reader.
Packaged: 2020-01-15 02:25:11 UTC; bittingerk

Authors@R: c(person(c("Rok","Blagus"),role=c("aut","cre"),email="rok.blagus@mf.uni-lj.si"),person("Sladana", "Babic", role = "ctb",
Author: Rok Blagus [aut, cre],  Sladana Babic [ctb]
Description: Performs augmented backward elimination and checks the stability of the obtained model. Augmented backward elimination combines significance or information based criteria with the change in estimate to either select the optimal model for prediction purposes or to serve as a tool to obtain a practically sound, highly interpretable model. More details can be found in Dunkler et al. (2014) <doi:10.1371/journal.pone.0113677>.