Description: Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).
Depends: R (>= 2.14.0), survival (>= 2.37-2), parallel

Depends: R (>= 2.14.0), survival (>= 2.37-2), parallel, mvtnorm

Description: Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models, mixed models for curvilinear outcomes or mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).
Depends: R (>= 3.5.0), survival (>= 2.37-2), parallel, mvtnorm,

Description: Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).

Author: Liangliang Wang <l.wang@stat.ubc.ca>, Nandini Dendukuri
Description: Bayesian estimation of latent class models with random
Packaged: 2011-02-14 05:47:23 UTC; liangliang

Title: Simple Tools to Compute Landscape Connectivity Metrics
person("Frederico", "Mestre", email = "mestre.frederico@gmail.com", role = c("aut","cre")),
person("Bruno", "Silva", email = "bmsasilva@gmail.com", role = "aut"))
Description: Provides functions to upload vectorial data and derive landscape
connectivity metrics in habitat or matrix systems. Additionally, includes an
approach to assess individual patch contribution to the overall landscape
connectivity, enabling the prioritization of habitat patches. The computation
of landscape connectivity and patch importance are very useful in Landscape
Ecology research. The metrics available are: number of components, number of
links, size of the largest component, mean size of components, class coincidence
probability, landscape coincidence probability, characteristic path length,
expected cluster size, area-weighted flux and integral index of connectivity.