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.

Provides functions for detecting 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>.

Data for the vignette and examples in 'RFlocalfdr'. Contains a dataset of 1103547 importance values,
       and the table of variables used in the random forest splits. The data is Chromosome 22 taken from Auton et al.
       (2015) <doi:10.1038/nature15393>. It also contains a 51 samples by 22283 genes data set taken from
       Spira et al. (2004) <doi:10.1165/rcmb.2004-0273OC>.

Provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).

Interface to the 'flsgen' neutral landscape generator <https://github.com/dimitri-justeau/flsgen>. It allows to
  - Generate fractal terrain;
  - Generate landscape structures satisfying user targets over landscape indices;
  - Generate landscape raster from landscape structures.

Tools for RFM (recency, frequency and monetary value) analysis. 
    Generate RFM score from both transaction and customer level data. Visualize the
    relationship between recency, frequency and monetary value using heatmap, 
    histograms, bar charts and scatter plots. Includes a 'shiny' app for 
    interactive segmentation. References:
    i. Blattberg R.C., Kim BD., Neslin S.A (2008) <doi:10.1007/978-0-387-72579-6_12>.