'MTrackJ' is an 'ImageJ' plugin for motion tracking and analysis (see 
    <https://imagescience.org/meijering/software/mtrackj/>). This package reads 
    and writes 'MTrackJ Data Files' ('.mdf', see 
    <https://imagescience.org/meijering/software/mtrackj/format/>). It supports
    2D data and read/writes cluster, point, and channel information. If desired, 
    generates track identifiers that are unique over the clusters.
    See the project page for more information and examples.

Provides functions to impute missing values using Gaussian 
    copulas for mixed data types as described by Christoffersen et al. 
    (2021) <arXiv:2102.02642>. The method is related to Hoff (2007) 
    <doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845> 
    but differs by making a direct approximation of the log marginal likelihood 
    using an extended version of the Fortran code created by Genz and Bretz 
    (2002) <doi:10.1198/106186002394> in addition to also support multinomial 
    variables.

The routine gof_test() in this package runs the
    goodness-of-fit test using various test statistic for multivariate data. 
    Models under the null hypothesis can either be simple or allow for parameter estimation.
    p values are found via the parametric bootstrap (simulation). 
    The routine gof_test_adjusted_pvalues() runs several tests and then finds a 
    p value adjusted for simultaneous inference.
    The routine gof_power() allows the estimation of the power of the tests. 
    hybrid_test() and hybrid_power() do the same by first generating a Monte Carlo data set under
    the null hypothesis and then running a number of two-sample methods.
    The routine run.studies() allows a user to quickly study the power of a new method and 
    how it compares to those included in the package via a large number of case studies.
    For details of the methods and references see the included vignettes.

Normalize data to minimize the difference between sample plates 
    (batch effects). For given data in a matrix and grouping variable (or
	plate), the function 'normn_MA' normalizes the data on MA coordinates. 
	More details are in the citation. The primary method is 'Multi-MA'. Other 
	fitting functions on MA coordinates can also be employed e.g. loess.