Commit b35454e8 authored by unknown's avatar unknown
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v1.0.9.62 URL corrected in vignettes in order to pass the cran check

parent d304150f
Package: airGR
Type: Package
Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
Version: 1.0.9.61
Version: 1.0.9.62
Date: 2017-11-09
Authors@R: c(
person("Laurent", "Coron", role = c("aut", "trl")),
......
......@@ -14,7 +14,7 @@ output:
### 1.0.9.61 Release Notes (2017-11-09)
### 1.0.9.62 Release Notes (2017-11-09)
#### New features
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......@@ -123,9 +123,9 @@ The existence of several local minima illustrates the importance of defining an
Global optimization is most often used when facing a complex response surface, with multiple local mimina.
Here we use the following R implementation of some popular strategies:
* [DEoptim: differential evolution](https://cran.r-project.org/web/packages/DEoptim/index.html)
* [hydroPSO: particle swarm](https://cran.r-project.org/web/packages/hydroPSO/index.html)
* [Rmalschains: memetic algorithms](https://cran.r-project.org/web/packages/Rmalschains/index.html)
* [DEoptim: differential evolution](https://cran.r-project.org/package=DEoptim)
* [hydroPSO: particle swarm](https://cran.r-project.org/package=hydroPSO)
* [Rmalschains: memetic algorithms](https://cran.r-project.org/package=Rmalschains)
## Differential Evolution
```{r, warning=FALSE, results='hide'}
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......@@ -43,7 +43,7 @@ Please note that this vignette is only for illustration purposes and does not pr
## Standard Least Squares (SLS) Bayesian inference
We show how to use the DRAM algorithm for SLS Bayesian inference, with the `modMCMC()` function of the [FME](https://cran.r-project.org/web/packages/FME/) package.
We show how to use the DRAM algorithm for SLS Bayesian inference, with the `modMCMC()` function of the [FME](https://cran.r-project.org/package=FME) package.
First, we need to define a function that returns twice the opposite of the log-likelihood for a given parameter set.
Nota: in the `RunAirGR4J()` function, the computation of the log-likelihood is simplified in order to ensure a good computing performance. It corresponds to a translation of the two following lines.
......@@ -118,7 +118,7 @@ mcmcDRAM <- apply(ListIniParam, 2, function(iListIniParam) {
## MCMC diagnostics and visualisation tools
There are several diagnostics that can be used to check the convergence of the chains.
The R package [coda](https://cran.r-project.org/web/packages/coda/index.html) provides several diagnostic tests.
The R package [coda](https://cran.r-project.org/package=coda) provides several diagnostic tests.
Among others, the Gelman and Rubin's convergence can be used. A value close to 1 suggests acceptable convergence.
The result will be better with more iterations than 2000. As we kept the iterations during the convergence process, we have to set the `autoburnin` argument to `TRUE` in order to consider only the second half of the series.
......@@ -133,7 +133,7 @@ GelRub <- coda::gelman.diag(MultDRAM, autoburnin = TRUE)$mpsrf
GelRub
```
In addition, graphical tools can be used, with for example the [ggmcmc](https://cran.r-project.org/web/packages/ggmcmc/) package.
In addition, graphical tools can be used, with for example the [ggmcmc](https://cran.r-project.org/package=ggmcmc) package.
......
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