From b35454e88708a78c24eff7cc429ec533e3058366 Mon Sep 17 00:00:00 2001
From: unknown <olivier.delaigue@ANPI1430.antony.irstea.priv>
Date: Thu, 9 Nov 2017 15:00:40 +0100
Subject: [PATCH] v1.0.9.62 URL corrected in vignettes in order to pass the
cran check
---
DESCRIPTION | 2 +-
NEWS.rmd | 2 +-
vignettes/V02.1_param_optim.Rmd | 6 +++---
vignettes/V02.2_param_mcmc.Rmd | 6 +++---
4 files changed, 8 insertions(+), 8 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 55f17f92..2f8e52f3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
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")),
diff --git a/NEWS.rmd b/NEWS.rmd
index 2c8c74ad..97bfe056 100644
--- a/NEWS.rmd
+++ b/NEWS.rmd
@@ -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
diff --git a/vignettes/V02.1_param_optim.Rmd b/vignettes/V02.1_param_optim.Rmd
index 14751001..42da9a00 100644
--- a/vignettes/V02.1_param_optim.Rmd
+++ b/vignettes/V02.1_param_optim.Rmd
@@ -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'}
diff --git a/vignettes/V02.2_param_mcmc.Rmd b/vignettes/V02.2_param_mcmc.Rmd
index fb578d89..5db9cfa0 100644
--- a/vignettes/V02.2_param_mcmc.Rmd
+++ b/vignettes/V02.2_param_mcmc.Rmd
@@ -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.
--
GitLab