Perform the analysis of the World Health Organization
       (WHO) Pharmacovigilance database 'VigiBase' (Extract Case Level version),
       <https://who-umc.org/>
       e.g., load data, perform data management,
       disproportionality analysis, and descriptive statistics. Intended for
       pharmacovigilance routine use or studies.
       This package is NOT supported nor reflect the opinion of the WHO, or the
       Uppsala Monitoring Centre.
       Disproportionality methods are described by Norén et 
       al (2013) <doi:10.1177/0962280211403604>.

Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).

Inference methods for state-space models, relying on the Kalman Filter or on Viking (Variational Bayesian VarIance tracKING). See J. de Vilmarest (2022) <https://theses.hal.science/tel-03716104/>.

This is a package for creating and running Agent Based Models (ABM). It provides a set of base classes with core functionality to allow bootstrapped models. For more intensive modeling, the supplied classes can be extended to fit researcher needs.

Provides methods for imputation and visualization of 
    missing values. It includes graphical tools to explore the amount, structure 
    and patterns of missing and/or imputed values, supporting exploratory 
    data analysis and helping to investigate potential missingness mechanisms
    (details in Alfons, Templ and Filzmoser, <doi:10.1007/s11634-011-0102-y>. 
    The quality of imputations can be assessed visually using a wide range of 
    univariate, bivariate and multivariate plots. 
    The package further provides several imputation methods, 
    including efficient implementations of k-nearest neighbour and hot-deck 
    imputation (Kowarik and Templ 2013, <doi:10.18637/jss.v074.i07>, 
    iterative robust model-based multiple 
    imputation (Templ 2011, <doi:10.1016/j.csda.2011.04.012>; 
    Templ 2023, <doi:10.3390/math11122729>), and machine learning–based 
    approaches such as robust GAM-based multiple imputation 
    (Templ 2024, <doi:10.1007/s11222-024-10429-1>) as well as gradient boosting 
    (XGBoost) and transformer-based methods 
    (Niederhametner et al., <doi:10.1177/18747655251339401>). 
    General background and practical guidance on imputation are provided in the 
    Springer book by 
    Templ (2023) <doi:10.1007/978-3-031-30073-8>.