Easy access to data from Brazil's population censuses. The package
             provides a simple and efficient way to download and read the data 
             sets and the documentation of all the population censuses taken in 
             and after 1960 in the country. The package is built on top of the 
             'Arrow' platform <https://arrow.apache.org/docs/r/>,  which allows 
             users to work with larger-than-memory census data using 'dplyr' 
             familiar functions. <https://arrow.apache.org/docs/r/articles/arrow.html#analyzing-arrow-data-with-dplyr>.

Implement an interval censor method 
 to break ties when using data with ties to fitting a 
 bivariate copula.

Engines for survival models from the 'parsnip' package. These
    include parametric models (e.g., Jackson (2016)
    <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al
    (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g.,
    Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).

Tools for estimating censored Almost Ideal (AI) and Quadratic Almost Ideal (QUAI) demand systems using Maximum Likelihood Estimation (MLE). It includes functions for calculating demand share equations and the truncated log-likelihood function for a system of equations, incorporating demographic variables. The package is designed to handle censored data, where some observations may be zero due to non-purchase of certain goods. Package also contains a procedure to approximate demand elasticities numerically and estimate standard errors via Delta Method. It is particularly useful for applied researchers analyzing household consumption data.

Maximum Likelihood estimation of censored regression (Tobit) models
   with cross-sectional and panel data.

It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) <doi:10.1016/j.spasta.2017.12.001>.