Implementations of threshold regression approaches for linear
	     regression models with a covariate subject to random censoring,
	     including deletion threshold regression and completion threshold regression.
	     Reverse survival regression, which flip the role of response variable and the
	     covariate, is also considered.

Provee un acceso conveniente a mas de 17 millones de registros
    de la base de datos del Censo 2017. Los datos fueron importados desde
    el DVD oficial del INE usando el Convertidor REDATAM creado por Pablo De
    Grande. Esta paquete esta documentado intencionalmente en castellano
    asciificado para que funcione sin problema en diferentes plataformas.
    (Provides convenient access to more than 17 million records from the
    Chilean Census 2017 database. The datasets were imported from the official
    DVD provided by the Chilean National Bureau of Statistics by using the
    REDATAM converter created by Pablo De Grande and in addition it includes the
    maps accompanying these datasets.)

Download data from Brazil's population census. 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>).

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>.