Plot idiograms of karyotypes, plasmids, circular chr. having a set of data.frames for chromosome data and optionally mark data. Two styles of chromosomes can be used: without or with visible chromatids. Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangle, rounded and inProtein; and six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein); its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) <doi:10.1111/j.1601-5223.1964.tb01953.x>, karyotype indices of Watanabe et al. (1999) <doi:10.1007/PL00013869> and Romero-Zarco (1986) <doi:10.2307/1221906> and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964).

Carry out comparative authorship analysis of disputed and undisputed texts within the Likelihood Ratio Framework for expressing evidence in forensic science. This package contains implementations of well-known algorithms for comparative authorship analysis, such as Smith and Aldridge's (2011) Cosine Delta <doi:10.1080/09296174.2011.533591> or Koppel and Winter's (2014) Impostors Method <doi:10.1002/asi.22954>, as well as functions to measure their performance and to calibrate their outputs into Log-Likelihood Ratios.

Incremental Multiple Correspondence Analysis and Principal
    Component Analysis.

Interpreting the differences between mean scale scores across various
    forms of an assessment can be challenging. This difficulty arises from different
    mappings between raw scores and scale scores, complex mathematical relationships,
    adjustments based on judgmental procedures, and diverse equating functions applied
    to different assessment forms. An alternative method involves running simulations
    to explore the effect of incrementing raw scores on mean scale scores. The
    'idmact' package provides an implementation of this approach based on the
    algorithm detailed in Schiel (1998)
    <https://www.act.org/content/dam/act/unsecured/documents/ACT_RR98-01.pdf> which
    was developed to help interpret differences between mean scale scores on the
    American College Testing (ACT) assessment. The function idmact_subj() within
    the package offers a framework for running simulations on subject-level scores.
    In contrast, the idmact_comp() function provides a framework for conducting
    simulations on composite scores.