Manuel Castejón Limas, Joaquín B. Ordieres Meré,Eliseo P. Vergara González, Francisco Javier Martínez de Pisón Ascacibar, Alpha V. Pernía Espinoza, Fernando Alba Elías

Manuel Castejón Limas, Joaquín B. Ordieres Meré, Ana González
Marcos, Francisco Javier Martínez de Pisón Ascacibar, Alpha V.
Pernía Espinoza, Fernando Alba Elías
This package was born to release the TAO robust neural
network algorithm to the R users. It has grown and I think it
can be of interest for the users wanting to implement their own
training algorithms as well as for those others whose needs lye
only in the "user space".

Manuel Castejon Limas, Joaquin B. Ordieres Mere, Ana Gonzalez
Marcos, Francisco Javier Martinez de Pison Ascacibar, Alpha V.
Pernia Espinoza, Fernando Alba Elias, Jose Maria Perez Ramos

Artificial Neural Network Training and Simulating
Commands for training a simulating an artificial neural network.

Manuel Castej� Limas, Joaqu� B. Ordieres Mer� Eliseo P. Vergara Gonz�ez, Francisco Javier Mart�ez de Pis� Ascacibar, Alpha V. Pern� Espinoza, Fernando Alba El�s

Common Code for the 'AMPED' and 'PIMPLE' 'shiny' Apps for
Stakeholder Engagement and Harvest Strategies
person(given = "Finlay",
family = "Scott",
role = c("aut", "cre"),
email = "finlays@spc.int")
A collection of common code for the 'AMPED' and 'PIMPLE' 'shiny' applications for stakeholder engagement in developing fisheries harvest strategies. The package is not that useful on its own but provides common functionality including plots, stochasticity options, life history options and a simple surplus production model.

Shiny Apps to Support Capacity Building on Harvest Control Rules
c(person("Finlay", "Scott", , "finlays@spc.int", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-9950-9023")),
person(given = "Pacific Community (SPC)", role="cph"))
Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management.
'Introduction to HCRs' provides a simple overview to how HCRs work. Users are able to select their own HCR and
step through its performance, year by year. Biological variability and estimation uncertainty are introduced.
'Measuring performance' builds on the previous app and introduces the idea of using performance indicators
to measure HCR performance.
'Comparing performance' allows multiple HCRs to be created and tested, and their performance compared so that the
preferred HCR can be selected.
knitr, rmarkdown, bookdown, testthat (>= 3.0.0)
markdown, graphics, grDevices, RColorBrewer, stats, R6 (>=
2.5.1), scales (>= 1.1.1), shinyjs (>= 2.0.0), ggplot2 (>=
3.3.5), shinyscreenshot (>= 0.1.0)
Finlay Scott [aut, cre] (<https://orcid.org/0000-0001-9950-9023>),
Pacific Community (SPC) [cph]