A programmatic interface to 'FishBase', re-written
    based on an accompanying 'RESTful' API. Access tables describing over 30,000
    species of fish, their biology, ecology, morphology, and more. This package also
    supports experimental access to 'SeaLifeBase' data, which contains
    nearly 200,000 species records for all types of aquatic life not covered by
    'FishBase.'

Helps fisheries scientists collect measurements from calcified
    structures and back-calculate estimated lengths at previous ages using
    standard procedures and models. This is intended to replace much of the
    functionality provided by the now out-dated 'fishBC' software
    (<https://fisheries.org/bookstore/all-titles/software/70317/>).

Automatic generation of fish drawings based on JavaScript library <https://github.com/LingDong-/fishdraw>, including JavaScript code for dynamic generation of fish drawings.

A programmatic interface to 'openfisheries.org'. This package is
    part of the 'rOpenSci' suite (http://ropensci.org).

Provides data processing and summarization of data from FishNet2.net
  in text and graphical outputs. Allows efficient filtering of information and
  data cleaning.

Rank-based (R) estimation and inference for linear models.  Estimation
        is for general scores and a library of commonly used score
        functions is included.

Implementation of the FlashText algorithm, by Singh (2017) <arXiv:1711.00046>. It can be used to find and replace words in a given text with only one pass over the document.

Functions for the detection of spatial clusters using the flexible
    spatial scan statistic developed by Tango and Takahashi (2005) <doi:10.1186/1476-072X-4-11>.
    This package implements a wrapper for the C routine used in the FleXScan 3.1.2 
    <https://sites.google.com/site/flexscansoftware/home> developed by Takahashi,
    Yokoyama, and Tango. For details, see Otani et al. (2021) <doi:10.18637/jss.v099.i13>.

Sets a significance level for  Random Forest MDI (Mean Decrease in Impurity, Gini or
             sum of squares) variable importance scores, using an empirical Bayes approach.
             See Dunne et al. (2022)  <doi:10.1101/2022.04.06.487300>.