The main purpose of this package is to generate the structure of the analysis of variance 
            (ANOVA) table of the two-phase experiments. The user only need to input the design and the 
              relationships of the random and fixed factors using the Wilkinson-Rogers' syntax, 
              this package can then quickly generate the structure of the ANOVA table with the 
              coefficients of the variance components for the expected mean squares.
              Thus, the balanced incomplete block design and provides the efficiency
              factors of the fixed effects can also be studied and compared much easily.

Download official election results for Spain at polling station, municipality and province level from the Ministry of Interior (<https://infoelectoral.interior.gob.es/es/elecciones-celebradas/area-de-descargas/>), format them and import them to the R environment.

Performs exploratory data analysis and variable screening for
    binary classification models using weight-of-evidence (WOE) and information
    value (IV). In order to make the package as efficient as possible, aggregations
    are done in data.table and creation of WOE vectors can be distributed across
    multiple cores. The package also supports exploration for uplift models (NWOE
    and NIV).

Multiple Imputation for Informative Censoring.
    This package implements two methods. Gamma Imputation
    described in <DOI:10.1002/sim.6274> and Risk Score Imputation
    described in <DOI:10.1002/sim.3480>.

After testing for biased treatment assignment in an observational study using an unaffected outcome, the sensitivity analysis is constrained to be compatible with that test.  The package uses the optimization software gurobi obtainable from <https://www.gurobi.com/>, together with its associated R package, also called gurobi; see: <https://www.gurobi.com/documentation/7.0/refman/installing_the_r_package.html>.  The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies Biometrika, 79(2), 367-374  <doi:10.1093/biomet/79.2.367>.