A bundle of analytics tools for fisheries scientists. A 'shiny' R App is included for a 'no-code' solution for retrieval, analysis, and visualization.

An interface to the Fish Tree of Life API to download taxonomies,
    phylogenies, fossil calibrations, and diversification rate information for
    ray-finned fishes.

Implementation of color palettes based on fish species.

Fuzzy inference systems are based on fuzzy rules, which have a good capability for managing progressive phenomenons. 
  This package is a basic implementation of the main functions to use a Fuzzy Inference System (FIS) provided by the open source software 'FisPro' <https://www.fispro.org>. 
  'FisPro' allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system.

Provides functionality for constructing
    statistical models of transcriptomic dynamics in field conditions.
    It further offers the function to predict expression of a gene given 
    the attributes of samples and meteorological data. Nagano, A. J., Sato, 
    Y., Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., Naganuma, Y., and 
    Izawa, T. (2012). <doi:10.1016/j.cell.2012.10.048>. Iwayama, K., Aisaka, Y., 
    Kutsuna, N., and Nagano, A. J. (2017). <doi:10.1093/bioinformatics/btx049>.

The fit.models function and its associated methods (coefficients, print,
  summary, plot, etc.) were originally provided in the robust package to compare robustly
  and classically fitted model objects. See chapters 2, 3, and 5 in Insightful (2002)
  'Robust Library User's Guide' <http://robust.r-forge.r-project.org/Robust.pdf>). The aim
  of the fit.models package is to separate this fitted model object comparison functionality
  from the robust package and to extend it to support fitting methods (e.g., classical,
  robust, Bayesian, regularized, etc.) more generally.

Many 'Fitbit' users, and R-friendly 'Fitbit' users
    especially, have found themselves curious about their 'Fitbit' data.
    'Fitbit' aggregates a large amount of personal data, much of which is
    interesting for personal research and to satisfy curiosity, and is
    even potentially useful in medical settings. The goal of 'fitbitr' is
    to make interfacing with the 'Fitbit' API as streamlined as possible,
    to make it simple for R users of all backgrounds and comfort levels to
    analyze their 'Fitbit' data and do whatever they want with it!
    Currently, 'fitbitr' includes methods for pulling data on activity,
    sleep, and heart rate, but this list is likely to grow in the future
    as the package gains more traction and more requests for new methods
    to be implemented come in.  You can find details on the 'Fitbit' API
    at <https://dev.fitbit.com/build/reference/web-api/>.