Functions for calculating metrics for the measurement biodiversity
and its changes across scales, treatments, and gradients. The methods
implemented in this package are described in:
Chase, J.M., et al. (2018) <doi:10.1111/ele.13151>,
McGlinn, D.J., et al. (2019) <doi:10.1111/2041-210X.13102>, and
McGlinn, D.J., et al. (2021) <doi:10.1002/ecy.3233>.

Spatial Simulation and Scale-Dependent Analysis of Biodiversity
Changes
person("Felix", "May", email = "felix.may@posteo.de", role = c("aut","cre"))
Tools for the simulation, analysis and sampling of spatial
biodiversity data (May et al. 2017) <doi:10.1101/209502>.
In the simulation tools user define the numbers of
species and individuals, the species abundance distribution and species
aggregation. Functions for analysis include species rarefaction
and accumulation curves, species-area relationships and the distance
decay of similarity.
Rcpp, vegan, sads (>= 0.4.1), grDevices, utils, graphics,
stats

rmarkdown, spatstat.geom, spatstat.core, spatstat (>= 2.0-0),
testthat, knitr

Fits a variety of mixtures models for multivariate observations
with user-difined distributions and curves.

Fits a variety of mixtures models for multivariate observations
with user-defined distributions and profiles.
GPL (version 2 or later, see the included file GPL)

Fits a variety of mixtures models for multivariate
observations with user-defined distributions and profiles.

General Nonlinear Multivariate Finite Mixtures
person("Bernard", "Boulerice",
email = "bernard.boulerice.bb@gmail.com",
role = c("aut","cre"))
Fits and vizualize user defined finite mixture models for multivariate observations
using maximum likelihood. (McLachlan, G., Peel, D. (2000) Finite Mixture Models. Wiley-Interscience.)

Multi-Objective Clustering Algorithm Guided by a-Priori
Biological Knowledge
c(person("Jorge Parraga-Alava", email = "jorge.parraga@usach.cl", comment = c(ORCID = "0000-0001-8558-9122"), role = c("aut", "cre")), person("Marcio Dorn", email = "mdorn@inf.ufrgs.br", role=c("aut")), person("Mario Inostroza-Ponta", email = "mario.inostroza@usach.cl", role = c("aut")))
Jorge Parraga-Alava [aut, cre]
(<https://orcid.org/0000-0001-8558-9122>),
Marcio Dorn [aut],
Mario Inostroza-Ponta [aut]
Implements the Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) which was proposed by Parraga-Alava, J. et. al. (2018) <doi:10.1186/s13040-018-0178-4>.
stats, amap, mco, nsga2R, reshape2, miscTools, matrixStats,
fields, Rmisc, plyr, foreach, parallel, doParallel, utils,
doSNOW, doMPI