Create Primavera-style interactive Gantt charts with Work Breakdown
    Structure (WBS) hierarchy and activities. Features include color-coded WBS
    items, indented labels, scrollable views for large projects, dynamic date
    formatting, and the ability to dim past activities. Built on top of 'plotly'
    for interactive visualizations.

As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18.
        <doi:10.18637/jss.v023.i08>], it is designed as an integrated package for genetic data
        analysis of both population and family data. Currently, it contains functions for
        sample size calculations of both population-based and family-based designs, probability
        of familial disease aggregation, kinship calculation, statistics in linkage analysis,
        and association analysis involving genetic markers including haplotype analysis with or
        without environmental covariates. Over years, the package has been developed in-between
        many projects hence also in line with the name (gap).

Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in {Martinez-de-Pison} (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.

Provides functions to estimate the disparities across categories (e.g. Black and white) that persists if a treatment variable (e.g. college) is equalized. Makes estimates by treatment modeling, outcome modeling, and doubly-robust augmented inverse probability weighting estimation, with standard errors calculated by a nonparametric bootstrap. Cross-fitting is supported. Survey weights are supported for point estimation but not for standard error estimation; those applying this package with complex survey samples should consult the data distributor to select an appropriate approach for standard error construction, which may involve calling the functions repeatedly for many sets of replicate weights provided by the data distributor. The methods in this package are described in Lundberg (2021) <doi:10.31235/osf.io/gx4y3>.