An implementation of a formal grammar and parser for R Markdown documents
    using the Boost Spirit X3 library. It also includes a collection of high level
    functions for working with the resulting abstract syntax tree.

Functions to load Research Patient Data Registry ('RPDR') text queries from Partners Healthcare institutions into R.
             The package also provides helper functions to manipulate data and execute common procedures
             such as finding the closest radiological exams considering a given timepoint, or creating a DICOM header database
             from the downloaded images. All functionalities are parallelized for fast and efficient analyses.

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.