Perform flexible simulation studies using one or multiple computer cores.
          The package is set up to be usable on high-performance clusters in addition
          to being run locally, see examples on <https://github.com/SachaEpskamp/parSim>.

A common interface is provided to allow users to specify a
    model without having to remember the different argument names across
    different functions or computational engines (e.g. 'R', 'Spark',
    'Stan', 'H2O', etc).

It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.

A novel tool for generating a piecewise
        constant estimation list of increasingly complex predictors
        based on an intensive and comprehensive search over the entire
        covariate space.

Provides a 'partialised' class that extends the partialising 
    function of 'purrr' by making it easier to change the arguments. This is 
    similar to the function-like object in 'Julia' 
    (<https://docs.julialang.org/en/v1/manual/methods/#Function-like-objects>).

Creates a data frame with the residuals of partial regressions of the main explanatory variable and the variable of interest. This method follows the Frisch-Waugh-Lovell theorem, as explained in Lovell (2008) <doi:10.3200/JECE.39.1.88-91>.

Tests for a comparison of two partially overlapping samples.
  A comparison of means using the partially overlapping samples t-test: 
  See Derrick, Russ, Toher and White (2017), Test 
  statistics for the comparison of means for two samples which include 
  both paired observations and independent observations, Journal of 
  Modern Applied Statistical Methods, 16(1). 
  A comparison of proportions using the partially overlapping samples z-test:
  See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics
  for comparing two proportions with partially overlapping samples.
  Journal of Applied Quantitative Methods, 10(3).