methods. Detailed vignettes are available on the fishR website <http://derekogle.com/fishR/>.

dunn.test, lmtest, plotrix, sciplot, withr
nlstools, pkgdown, plyr, psych, Rcapture, rmarkdown, testthat,

Imports: graphics, grDevices, stats, tools, utils, car, dunn.test,
Suggests: DescTools, dplyr, fishmethods, FSAdata, knitr, marked, nlme,

Title: Data to Support Fish Stock Assessment (FSA) Package
Description: The datasets to support the ``Fish Stock Assessment'' (FSA) package.

Description: The datasets to support the Fish Stock Assessment (FSA) package.

Title: Data to Support Fish Stock Assessment ('FSA') Package
Description: The datasets to support the Fish Stock Assessment ('FSA') package.

Author: Derek Ogle [aut, cre] (<https://orcid.org/0000-0002-0370-9299>)

Title: Forward Stepwise Deep Autoencoder-Based Monotone NLDR
Authors@R: c(person("Youyi", "Fong", role = "cre", email = "youyifong@gmail.com"), person("Jun", "Xu", role = "aut", email = "junxu8@gmail.com"))
Description: FS-DAM performs feature extraction through latent variables identification. Implementation is based on autoencoders with monotonicity and orthogonality constraints.

Maintainer: Rajen D. Shah <r.shah@statslab.cam.ac.uk>
Description: Performs fast detection of interactions in large-scale data using the
method of random intersection trees introduced in
``Shah, R. D. and Meinshausen, N. (2014) Random Intersection Trees''.  The algorithm
finds potentially high-order interactions in high-dimensional binary
two-class classification data, without requiring lower order interactions
to be informative.  The search is particularly fast when the matrices of
predictors are sparse.  It can also be used to perform market basket analysis
when supplied with a single binary data matrix.  Here it will find collections
of columns which for many rows contain all 1's.

Shah, R. D. and Meinshausen, N. (2014) <http://www.jmlr.org/papers/v15/shah14a.html>.
The algorithm finds potentially high-order interactions in high-dimensional binary

Title: An Interface Between the 'FSK-ML' Standard and 'R'
Authors@R: c(person("Alberto", "Garre", email = "garre.alberto@gmail.com", role = c("aut", "cre")),
person("Miguel", "de Alba Aparicio", email = "Miguel.de-Alba-Aparicio@bfr.bund.de", role = "aut"),
person("Pablo S.", "Fernandez", email = "pablo.fernandez@upct.es", role = "aut"),
person("Matthias", "Filter", email = "Matthias.Filter@bfr.bund.de", role = "aut")
Description: Functions for importing, creating, editing and
using the 'R' programming environment. Furthermore, it enables users
to run simulations contained in the 'FSK' files and visualize the results.
Suggests: knitr (>= 1.9), rmarkdown (>= 1.12), testthat