Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.

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>.

Implements Cumulative Sum (CUSUM) control charts specifically
 designed for monitoring processes following a Gamma distribution. Provides
 functions to estimate distribution parameters, simulate control limits, and
 apply cautious learning schemes for adaptive thresholding. It supports
 upward and downward monitoring with guaranteed performance evaluated via
 Monte Carlo simulations. It is useful for quality control applications in
 industries where data follows a Gamma distribution. Methods are based on
 Madrid-Alvarez et al. (2024) <doi:10.1002/qre.3464> and Madrid-Alvarez et
 al. (2024) <doi:10.1080/08982112.2024.2440368>.

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.