Provides plugins for setting up 'fiery' apps as a reverse proxy.
    This allows you to use a 'fiery' server as a front for multiple services or
    even work as a load-balancer.

A model-independent factor importance ranking and selection procedure based on total Sobol' indices. Please see Huang and Joseph (2025) <doi:10.1080/00401706.2025.2483531>. This research is supported by U.S. National Science Foundation grants DMS-2310637 and DMREF-1921873.

Proposes non-parametric estimates of the Fisher information measure and the 
              Shannon entropy power. More theoretical and implementation details can be found
              in Guignard et al. <doi:10.3389/feart.2020.00255>. A 'python' version of this
              work is available on 'github' and 'PyPi' ('FiShPy').

Provides raw and curated data on the codes,
    classification and conservation status of freshwater fishes in British
    Columbia. Marine fishes will be added in a future release.

A suite of bootstrap-based models and tools for analyzing fish 
  stocks and aquatic populations. Designed for ecologists and fisheries 
  scientists, it supports data from length-frequency distributions, 
  tag-and-recapture studies, and hard structure readings (e.g., otoliths). 
  See Schwamborn et al., 2019 <doi:10.1016/j.ecolmodel.2018.12.001> 
  for background. The package includes functions for bootstrapped fitting of 
  growth curves and plotting.

A collection of four datasets 
    based around the population dynamics of migratory fish. Datasets 
    contain both basic size information on a per fish basis, as well as
    otolith data that contains a per day record of fish growth history.
    All data in this package was collected by the author, from 
    2015-2016, in the Wellington region of New Zealand.

Quantitatively analyse depth time-series data from pop-up satellite archival tags (PSATs) through the application of continuous wavelet transformation (CWT) combined with Principal Component Analysis (PCA), and k-means clustering.
  Import, crop, and plot depth time-depth records (TDRs). Using CWT to detect important signals within the non-stationary data, we create daily wavelet statistics to summarise vertical movements on different wavelet periods and combine with daily and diel depth statistics.
  Classify depth time-series with unsupervised k-means clustering into 24-hour periods of vertical movement behaviour with distinct patterns of vertical movement. Plot example days from each behaviour cluster, and plot the TDR coloured by cluster.
  Based on principals of combining CWT with k-means first developed by Sakamoto (2009) <doi:10.1371/journal.pone.0005379> and redeveloped by Beale (2026) <doi:10.21203/rs.3.rs-6907076/v1>.