Bayesian population size estimation using non parametric latent-class models.

Estimation of various extensions of the mixed models including latent class mixed models, joint 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>).

The goal of 'LCMSQA' is to make it easy to check the quality of
             liquid chromatograph/mass spectrometry (LC/MS) experiments using a
             'shiny' application. This package provides interactive data
             visualizations for quality control (QC) samples, including total
             ion current chromatogram (TIC), base peak chromatogram (BPC), mass
             spectrum, extracted ion chromatogram (XIC), and feature detection
             results from internal standards or known metabolites.

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.
    Pascual-Hortal, L., and Saura, S. (2006) <doi:10.1007/s10980-006-0013-z>
    Urban, D., and Keitt, T. (2001) <doi:10.2307/2679983>
    Laita, A., Kotiaho, J., Monkkonen, M. (2011) <doi:10.1007/s10980-011-9620-4>.

Collections of functions allowing random number generations and
    estimation of 'Liouville' copulas, as described in Belzile and Neslehova (2017) <doi:10.1016/j.jmva.2017.05.008>.