Implementation of the CNAIM standard in R. Contains a series of
    algorithms which determine the probability of failure, consequences of
    failure and monetary risk associated with electricity distribution
    companies' assets such as transformers and cables. Results are visualized
    in an easy-to-understand risk matrix.

This is an add-on to the 'cna' package <https://CRAN.R-project.org/package=cna> comprising various functions for optimizing consistency and coverage scores of models of configurational comparative methods as Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA). The function conCovOpt() calculates con-cov optima, selectMax() selects con-cov maxima among the con-cov optima, DNFbuild() can be used to build models actually reaching those optima, and findOutcomes() identifies those factor values in analyzed data that can be modeled as outcomes. For a theoretical introduction to these functions see Baumgartner and Ambuehl (2021) <doi:10.1177/0049124121995554>.

Provided R functions for working with the Conditional Negative Binomial distribution.

A GUI with which users can construct and interact
        with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.

An interface for creating new condition generators objects.  
    Generators are special functions that can be saved in registries and linked
    to other functions.  Utilities for documenting your generators, and new
    conditions is provided for package development.

Download, cache and read municipality-level address data from the
    Cadastro Nacional de Enderecos para Fins Estatisticos (CNEFE) of the 2022
    Brazilian Census, published by the Instituto Brasileiro de Geografia e
    Estatistica (IBGE)
    <https://ftp.ibge.gov.br/Cadastro_Nacional_de_Enderecos_para_Fins_Estatisticos/>.
    Beyond data access, provides spatial aggregation of addresses, computation
    of land-use mix indices, and dasymetric interpolation of census tract
    variables using CNEFE dwelling points as ancillary data. Results can be
    produced on 'H3' hexagonal grids or user-supplied polygons, and heavy
    operations leverage a 'DuckDB' backend with extensions for fast,
    in-process execution.