Differential Analysis of short RNA transcripts that can be modeled by either Poisson or Negative binomial distribution.  The statistical methodology implemented in this package is based on the random selection of references genes (Desaulle et al. (2021) <arXiv:2103.09872>).

The recovery of visual sensitivity in a dark environment is known
    as dark adaptation. In a clinical or research setting the recovery is typically
    measured after a dazzling flash of light and can be described by the Mahroo,
    Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take
    dark adaptation data and use nonlinear regression to find the parameters of the
    model that 'best' describe the data. They do this by firstly, generating rapid
    initial objective estimates of data adaptation parameters, then a multi-start
    algorithm is used to reduce the possibility of a local minimum. There is also a
    bootstrap method to calculate parameter confidence intervals. The functions rely
    upon a 'dark' list or object. This object is created as the first step in the
    workflow and parts of the object are updated as it is processed.

Estimation of dark diversity and site-specific species pools using species co-occurrences. 
    It includes implementations of probabilistic dark diversity based on the 
    Hypergeometric distribution, as well as estimations based on the Beals index,
    which can be transformed to binary predictions using different thresholds, 
    or transformed into a favorability index. All methods include the possibility of
    using a calibration dataset that is used to estimate the indication matrix 
    between pairs of species, or to estimate dark diversity directly on a single 
    dataset. See De Caceres and Legendre (2008) <doi:10.1007/s00442-008-1017-y>, 
    Lewis et al. (2016) <doi:10.1111/2041-210X.12443>, 
    Partel et al. (2011) <doi:10.1016/j.tree.2010.12.004>, Real et al. (2017) <doi:10.1093/sysbio/syw072>
    for further information.