Includes wrapper functions around existing functions for the analysis of categorical data and introduces functions for calculating risk differences and matched odds ratios. R currently supports a wide variety of tools for the analysis of categorical data. However, many functions are spread across a variety of packages with differing syntax and poor compatibility with each another. prop_test() combines the functions binom.test(), prop.test() and BinomCI() into one output. prop_power() allows for power and sample size calculations for both balanced and unbalanced designs. riskdiff() is used for calculating risk differences and matched_or() is used for calculating matched odds ratios. For further information on methods used that are not documented in other packages see Nathan Mantel and William Haenszel (1959) <doi:10.1093/jnci/22.4.719> and Alan Agresti (2002) <ISBN:0-471-36093-7>.

Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models.

Functions designed to simulate data that conform to basic
          unidimensional IRT models (for now 3-parameter binary response models
          and graded response models) along with Post-Hoc CAT simulations of
          those models given various item selection methods, ability estimation
          methods, and termination criteria. See
          Wainer (2000) <doi:10.4324/9781410605931>,
          van der Linden & Pashley (2010) <doi:10.1007/978-0-387-85461-8_1>,
          and Eggen (1999) <doi:10.1177/01466219922031365> for more details.

Formal psychological models of categorization and learning, independently-replicated data sets against which to test them, and simulation archives.

Although many software tools can perform meta-analyses on genetic case-control data,
    none of these apply to combined case-control and family-based (TDT) studies. This package conducts
    fixed-effects (with inverse variance weighting) and random-effects [DerSimonian and Laird (1986)
    <DOI:10.1016/0197-2456(86)90046-2>] meta-analyses on combined genetic data. Specifically, this package
    implements a fixed-effects model [Kazeem and Farrall (2005) <DOI:10.1046/j.1529-8817.2005.00156.x>]
    and a random-effects model [Nicodemus (2008) <DOI:10.1186/1471-2105-9-130>] for combined studies.