Count transformation models featuring 
        parameters interpretable as discrete hazard ratios, odds ratios,
        reverse-time discrete hazard ratios, or transformed expectations. 
        An appropriate data transformation for a count outcome and
        regression coefficients are simultaneously estimated by maximising
        the exact discrete log-likelihood using the computational framework
        provided in package 'mlt', technical details are given in
        Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>. 
        The package also contains an experimental implementation of 
        multivariate count transformation models with an application
        to multi-species distribution models <DOI:10.48550/arXiv.2201.13095>.

Implements cointegration/co-trending rank selection
        algorithm in Guo and Shintani (2013) "Consistent
        co-trending rank selection when both stochastic and nonlinear
        deterministic trends are present". 
        The Econometrics Journal 16: 473-483 
        <doi:10.1111/j.1368-423X.2012.00392.x>.
        Numbered examples correspond to Feb 2011 preprint 
        <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>.

Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).

Counts colors within color range(s) in images, and
    provides a masked version of the image with targeted pixels
    changed to a different color. Output includes the locations
    of the pixels in the images, and the proportion of the image
    within the target color range with optional background masking.
    Users can specify multiple color ranges for masking.

The beta-binomial test is used for significance analysis of independent samples by Pham et al. (2010) <doi:10.1093/bioinformatics/btp677>. The inverted beta-binomial test is used for paired sample testing, e.g. pre-treatment and post-treatment data, by Pham and Jimenez (2012) <doi:10.1093/bioinformatics/bts394>.