Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models.

Allows access to data from the Brazilian Public Security Information System (SINESP) by state and municipality. <https://www.gov.br/mj/pt-br/assuntos/sua-seguranca/seguranca-publica/sinesp-1>.

A compilation of functions to download and processing AWS data of INMET-Brazil, with the purpose of reference evapotranspiration (ETo) estimation. The package aims to make meteorological and agricultural data analysis more parsimonious.

Performs variable selection and ranking based on several measures for the class of copula survival model(s) in high dimensional domain.
    The package is based on the class of copula survival model(s) implemented in the 'GJRM' package.

Boldness-recalibration maximally spreads out probability predictions while maintaining a user specified level of calibration, facilitated the brcal() function. Supporting functions to assess calibration via Bayesian and Frequentist approaches, Maximum Likelihood Estimator (MLE) recalibration, Linear in Log Odds (LLO)-adjust via any specified parameters, and visualize results are also provided. Methodological details can be found in Guthrie & Franck (2024) <doi:10.1080/00031305.2024.2339266>.

Fetches zonal statistics from weather indicators that were calculated for each municipality in Brazil using data from the BR-DWGD and TerraClimate projects.
    Zonal statistics such as mean, maximum, minimum, standard deviation, and sum were computed by taking into account the data cells that intersect the boundaries of each municipality and stored in Parquet files. This procedure was carried out for all Brazilian municipalities, and for all available dates, for every indicator available in the weather products (BR-DWGD and TerraClimate projects). This package queries on-line the already calculated statistics on the Parquet files and returns easy-to-use data.frames.

This is an implementation of design methods for binomial reliability demonstration tests (BRDTs) with failure count data. 
    The acceptance decision uncertainty of BRDT has been quantified and the impacts of the uncertainty on related reliability assurance activities such as reliability growth (RG) and warranty services (WS) are evaluated.
    This package is associated with the work from the published paper "Optimal Binomial Reliability Demonstration Tests Design under Acceptance Decision Uncertainty" by Suiyao Chen et al. (2020) <doi:10.1080/08982112.2020.1757703>.