Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and
    van der Linden (2018) <doi:10.1201/9781315117430> to construct fixed, adaptive, and parallel tests.
    Supports the following mixed-integer programming (MIP) solver packages: 'Rsymphony',
    'highs', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see <https://www.gurobi.com/downloads/>.

Offers a solution for the unavailability of raw data in most anthropological studies by facilitating the calculations of several sexual dimorphism related analyses using the published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of Relethford, J. H., & Hodges, D. C. (1985) <doi:10.1002/ajpa.1330660105>, Greene, D. L. (1989) <doi:10.1002/ajpa.1330790113> and Konigsberg, L. W. (1991) <doi:10.1002/ajpa.1330840110>.

Provides data sets for teaching statistics and data science courses. 
    It includes a sample of data from John Edmund Kerrich's famous 
    coinflip experiment. These are data that I used for statistics. The package 
    also contains an R Markdown template with the required formatting for 
    assignments in my former courses.

Tests the hypothesis  that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test.
             The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) \doi{10.1016/j.csda.2010.04.012}.

Allows users to test characteristics of common R objects.

Add tests in-line in examples. Provides standalone functions for
    facilitating easier test writing in Rd files. However, a more familiar
    interface is provided using 'roxygen2' tags. Tools are also provided for
    facilitating package configuration and use with 'testthat'.

Provides a unified workflow for choosing, running, interpreting,
  and visualizing common statistical tests, from group comparisons and
  analysis of variance to regression, survival analysis, and diagnostic and
  agreement statistics. The package combines assumption checks, test
  selection, effect sizes, formatted results, plain-language interpretation,
  'ggplot2'-based statistical visualizations, and a sample-size planning
  module covering continuous, binary, survival, ordinal, bioequivalence, and
  precision-based designs. Implemented methods follow standard references
  including Casella and Berger (2002, ISBN:9780534243128), Hollander et al.
  (2013, ISBN:9781118553299), Agresti (2013, ISBN:9780470463635), Cohen
  (1988, ISBN:9780805802832), Hosmer, Lemeshow and Sturdivant (2013,
  ISBN:9780470582473), and Julious (2010, ISBN:9781584887393).