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>.

A unified latent class modeling framework that encompasses both latent class analysis (LCA) and latent profile analysis (LPA), offering a one-stop solution for latent class modeling. It implements state-of-the-art parameter estimation methods, including the expectation–maximization (EM) algorithm, neural network estimation (NNE; requires users to have 'Python' and its dependent libraries installed on their computer), and integration with 'Mplus' (requires users to have 'Mplus' installed on their computer). In addition, it provides commonly used model fit indices such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), as well as classification accuracy measures such as entropy. The package also includes fully functional likelihood ratio tests (LRT) and bootstrap likelihood ratio tests (BLRT) to facilitate model comparison, along with bootstrap-based and observed information matrix-based standard error estimation. Furthermore, it supports the standard three-step approach for LCA, LPA, and latent transition analysis (LTA) with covariates, enabling detailed covariate analysis. Finally, it includes several user-friendly auxiliary functions to enhance interactive usability.