Implements modern resampling and permutation methods for robust 
    statistical inference without restrictive parametric assumptions. Provides 
    bias-corrected and accelerated (BCa) bootstrap (Efron and Tibshirani (1993) 
    <doi:10.1201/9780429246593>), wild bootstrap for heteroscedastic regression 
    (Liu (1988) <doi:10.1214/aos/1176351062>, Davidson and Flachaire (2008) 
    <doi:10.1016/j.jeconom.2008.08.003>), block bootstrap for time series 
    (Politis and Romano (1994) <doi:10.1080/01621459.1994.10476870>), and 
    permutation-based multiple testing correction (Westfall and Young (1993) 
    <ISBN:0-471-55761-7>). Methods handle non-normal data, 
    heteroscedasticity, time series correlation, and multiple comparisons.

Provides a single unified interface for computing a wide variety of
    classical and modern correlation and association measures. Continuous methods
    include classical correlations (Pearson, Spearman, Kendall), modern dependence
    measures (distance correlation, maximal information coefficient, Hilbert-Schmidt
    independence criterion, Chatterjee's xi, Hoeffding's D, mutual information),
    robust correlations (biweight midcorrelation, percentage bend, Winsorized),
    ordinal correlations (polychoric, tetrachoric), partial and semi-partial
    correlations, and nonparametric measures (ball correlation, Bergsma-Dassios
    tau*). Categorical association measures (Cramer's V, phi coefficient,
    Goodman-Kruskal gamma, Somers' D, contingency coefficient, Tschuprow's T)
    are available via moderncor_cat().

Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.

Provides functions that fit two modern education-based value-added models.  
                 One of these models is the quantile value-added model.  This model 
                 permits estimating a school's value-added based on specific quantiles of 
                 the post-test distribution.  Estimating value-added based on quantiles 
                 of the post-test distribution provides a more complete picture of an 
                 education institution's contribution to learning for students of all 
                 abilities. See Page, G.L.; San Martín, E.; Orellana, J.; Gonzalez, J. (2017)
                 <doi:10.1111/rssa.12195> for more details.  The second model is a temporally 
                 dependent value-added model. This model takes into account the temporal 
                 dependence that may exist in school performance  between two cohorts in one 
                 of two ways.  The first is by modeling school  random effects with a 
                 non-stationary AR(1) process.  The second is by  modeling school effects 
                 based on previous cohort's post-test performance. In addition to more 
                 efficiently estimating value-added, this model permits making statements 
                 about the persistence of a schools effectiveness. The standard  value-added 
                 model is also an option.