Provides a modern interface to the Brazilian federal
    government's 'ObrasGov' open data application programming interface
    (<https://api-publica.obrasgov.gestao.gov.br>). Retrieves data about
    public infrastructure projects, physical execution, contracts,
    commitments, geometries, feasibility studies, and project status
    histories. Results are returned as tidy tibbles with typed date columns,
    preserved nested relationships, pagination metadata, and optional
    multi-page collection.

An implementation for computing Optimal B-Robust Estimators of two-parameter 
    distribution. The procedure is composed of some equations
    that are evaluated alternatively until the solution is reached. Some tools
    for analyzing the estimates are included. The most relevant is
    covariance matrix computation using a closed formula.

Two-part system for first collecting then managing direct
    observation data, as described by Hibbing PR, Ellingson LD,
    Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.

Creates block designs of fixed size J with at least one treated 
    and control unit per block. Blocks larger than pairs better
    distinguish effects caused by a treatment from unmeasured confounding in
    assignment of individuals to treatment. Somewhat counterintuitively,
    blocks larger than pairs can use more units while attaining better
    covariate balance and block homogeneity. A forthcoming manuscript 
    by Brumberg and Rosenbaum details the design.

Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models, see Deldossi and Nai Ruscone (2020) <doi:10.18637/jss.v094.i02>.

Observational studies are limited in that there could be an unmeasured variable related to both the response variable and the primary predictor.  If this unmeasured variable were included in the analysis it would change the relationship (possibly changing the conclusions).  Sensitivity analysis is a way to see how much of a relationship needs to exist with the unmeasured variable before the conclusions change.  This package provides tools for doing a sensitivity analysis for regression (linear, logistic, and cox) style models.