Some basic functions to implement belief functions including:
    transformation between belief functions using the method introduced by
    Philippe Smets <arXiv:1304.1122>, evidence combination, evidence
    discounting, decision-making, and constructing masses. Currently, thirteen
    combination rules and six decision rules are supported. It can also be
    used to generate different types of random masses when working on belief
    combination and conflict management.

The initial basic feasible solution (IBFS) is a significant step to achieve the minimal total cost (optimal solution) of the transportation problem. However, the existing methods of IBFS do not always provide a good feasible solution which can reduce the number of iterations to find the optimal solution. This initial basic feasible solution can be obtained by using any of the following methods. 
             a) North West Corner Method. 
             b) Least Cost Method. 
             c) Row Minimum Method. 
             d) Column Minimum Method. 
             e) Vogel's Approximation Method.
             etc.
             For more technical details about the algorithms please refer below URLs.
             <https://theintactone.com/2018/05/24/ds-u2-topic-8-transportation-problems-initial-basic-feasible-solution/>.
             <https://www.brainkart.com/article/Methods-of-finding-initial-Basic-Feasible-Solutions_39037/>.
             <https://myhomeworkhelp.com/row-minima-method/>.
             <https://myhomeworkhelp.com/column-minima-method/>.

'Tidyverse'-friendly interface to the Brazilian Institute of Geography and Statistics ('IBGE') aggregate data 'API' 
    <https://servicodados.ibge.gov.br/api/docs/agregados?versao=3>. Query aggregates, variables, localities, periods, and metadata
    from surveys and censuses conducted by 'IBGE'.

Variable selection for generalized linear models and the Cox
    proportional-hazards model in ultrahigh dimensions via the iterated block
    Gibbs sampler (IBGS). The sampler is implemented in C with parallel block
    screening through 'OpenMP', and supports the gaussian, binomial and poisson
    families (fitted by least squares or iteratively reweighted least squares)
    as well as the Cox model for survival analysis (fitted by its Efron partial
    likelihood), together with the AIC, BIC, AICc and extended BIC model
    selection criteria.