Author: Raydonal Ospina <raydonal@de.ufpe.br>  and Leila D. Amorim <leiladen@ufba.br>.
Description: Estimation of prevalence ratios using logistic models and confidence intervals with delta and bootstrap methods.

person("Anne", "Le Maitre", email = "anne.le.maitre@univie.ac.at", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2690-7367")),
person("Silvester", "Bartsch", email = "silvester.bartsch@univie.ac.at", role = "aut"),
person("Nicole", "Grunstra", email = "nicolegrunstra@googlemail.com", role = "aut"),
person("Philipp", "Mitteroecker", email = "philipp.mitteroecker@univie.ac.at", role = "aut"))
Description: Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>. Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al. (2020) <doi:10.5061/dryad.zkh189373>.
Author: Anne Le Maitre [aut, cre] (<https://orcid.org/0000-0003-2690-7367>),
Maintainer: Anne Le Maitre <anne.le.maitre@univie.ac.at>

Package: prabclus
Title: Test for clustering of presence-absence data
Version: 1.0-1
Date: 2003-06-30
Author: Christian Hennig <hennig@math.uni-hamburg.de>
Depends: R (>= 1.5.0), mclust
Description: Distance based parametric bootstrap tests for clustering,
mainly thought for presence-absence data (clustering of species
distribution maps). Jaccard and Kulczynski distance measures, clustering
of MDS scores, and nearest neighbor based noise detection (R port of Byers
and Raftery's (1998) "NNclean"). Main functions are prabtest (for
testing), prabclust (for clustering), prabinit (for preparing the data)
and NNclean (for noise detection). The help-pages for prabtest and
prabclust contain simple standard executions.
Maintainer: Christian Hennig <hennig@math.uni-hamburg.de>
License: GPL
URL: http://www.math.uni-hamburg.de/home/hennig/