[FLR-list] FLa4a 1.0.0 is out !

[Ernesto]

Dear all,

We're happy to announce that FLa4a is now in version 1.0.0 ! A milestone 
in every software project.

FLa4a, is an R package which implements the a4a stock assessment 
framework and the methods to model M and growth (see intro here 
http://www.flr-project.org/FLa4a/articles/introduction.pdf). It's based 
on FLR and ADMB. The first for data structures and supporting methods 
and the second for fitting stock assessment models.

The package is released under the open source license EUPL 1.1 
(http://ec.europa.eu/idabc/servlets/Doc027f.pdf?id=31096). All the code 
is available in github (https://github.com/flr/FLa4a). There's also a 
new webpage (http://www.flr-project.org/FLa4a/) where you'll find the 
package and all the documentation.

La4a runs on R versions above 3.1. To install the package open your R 
session and type:

install.packages(c(“copula”,“triangle”, “coda”))
|install.packages(c("FLa4a","FLCore"), repos="http://flr-project.org/R")|

The most important addition to version 1.0.0 was documentation. There's 
now a full set of man pages, reached through the common '?' method, and 
a set of vignettes:
- Introduction to FLa4a 
(http://www.flr-project.org/FLa4a/articles/introduction.pdf)
- Natural Mortality Modelling 
(http://www.flr-project.org/FLa4a/articles/M.pdf)
- Modelling Individual Growth and Using Stochastic Slicing to Convert 
Length-based Data Into Age-based Data 
(http://www.flr-project.org/FLa4a/articles/growth.pdf)
- The a4a Stock Assessment Modelling Framework 
(http://www.flr-project.org/FLa4a/articles/sca.pdf)

To access the list of vignettes use 'vignette(package="FLa4a")' from 
within your R session, or download from the webpage 
(http://www.flr-project.org/FLa4a/articles/index.html).

Additionally a large number of small bugs and improvements were included 
in this version, new plots for residuals, etc. I won't claim the package 
is bug-free as I don't believe such thing exists. So please report any 
odd results you may find.

A quick trial can be done with:

# lib and data
library(FLa4a)
data(ple4)
data(ple4.indices)
# fit
fit <- sca(ple4, ple4.indices)
# diagnostics
res <- residuals(fit, ple4, ple4.indices)
plot(res)
qqmath(res)
plot(fit, ple4)
plot(fit, ple4.indices)
AIC(fit)
BIC(fit)
# summary
stk <- ple4 + fit
plot(stk)

As usual all comments are welcome.

Best regards

EJ

PS: sorry for any cross posting

-- 
Ernesto Jardim<ernesto.jardim at jrc.ec.europa.eu>
Fisheries Scientist
Joint Research Centre (JRC), Sustainable resources directorate, Water and Marine Resources unit
TP 051, Via Enrico Fermi 2749
I-21027 Ispra (VA), Italy
Office : +39 0332 785311
Fax: +39 0332 789658


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