Spatial and spatio-temporal modelling of point patterns using the
    log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal,
    multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.

The goal of this package is to cover the most common steps in Loss Given Default (LGD) rating model development.
             The main procedures available are those that refer to bivariate and multivariate analysis. In particular two statistical methods for 
             multivariate analysis are currently implemented – OLS regression and fractional logistic regression.
             Both methods are also available within different blockwise model designs and both have customized stepwise algorithms. 
             Descriptions of these customized designs are available in Siddiqi (2016) <doi:10.1002/9781119282396.ch10> and 
             Anderson, R.A. (2021) <doi:10.1093/oso/9780192844194.001.0001>. 
             Although they are explained for PD model, the same designs are applicable for LGD model with different underlying regression methods 
             (OLS and fractional logistic regression). To cover other important steps for LGD model development, it is recommended to use 
             'LGDtoolkit' package along with 'PDtoolkit', and 'monobin' (or 'monobinShiny') packages.
             Additionally, 'LGDtoolkit' provides set of procedures handy for initial and periodical model validation.

Functions for genome-wide association studies (GWAS)/gene-environment-wide interaction studies (GEWIS) with longitudinal outcomes and exposures. He et al. (2017) "Set-Based Tests for Gene-Environment Interaction in Longitudinal Studies" and He et al. (2017) "Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA)".

For high-dimensional correlated observations, this package carries out the L_1 penalized maximum likelihood 
            estimation of the precision matrix (network) and the correlation parameters. The correlated data can be 
            longitudinal data (may be irregularly spaced) with dampening correlation or clustered data with uniform correlation.  
            For the details of the algorithms, please see the paper Jie Zhou et al. Identifying Microbial Interaction Networks Based on Irregularly Spaced 
            Longitudinal 16S rRNA sequence data <doi:10.1101/2021.11.26.470159>.