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v1.0.9.62 URL corrected in vignettes in order to pass the cran check

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dev 115-add-pcict-date-format-managment 118-plot-outputsmodel-remove-case-sensitivity-to-the-which-argument 136-decreased-performance-of-calibration-execution-time 161-runmodel_lag-proposal-for-a-better-transformation-and-screening-of-the-parameters 165-add-missing-isintstore-argument-in-createcaliboptions 171-check-error-direct-call-of-as-data-frame-posixct-is-deprecated-use-as-data-frame-vector-or-as 191-speed-up-runmodel_lag-when-it-is-used-alone 33-merge-airgr-and-airgrplus-functions 60-migrate-transfoparam-functions-and-createcaliboptions-to-s3-methods 96-parallelize-the-grid-screening-step-during-calibration airGRplus master rmOptions CRAN_v1.7.6 CRAN_v1.7.5 CRAN_v1.7.4 CRAN_v1.7.3 CRAN_v1.7.2 CRAN_v1.7.1 CRAN_v1.7.0 CRAN_v1.6.12 CRAN_v1.6.11 CRAN_v1.6.10.4 CRAN_v1.6.9.27 CRAN_v1.6.9.24 CRAN_v1.6.9.23 CRAN_v1.6.9.21 CRAN_v1.4.3.65 CRAN_v1.4.3.60 CRAN_v1.4.3.52 CRAN_v1.3.2.42 CRAN_v1.3.2.41 CRAN_v1.3.2.40 CRAN_v1.3.2.39 CRAN_v1.3.2.23 CRAN_v1.3.2.17 CRAN_v1.2.13.16 CRAN_v1.2.13.13 CRAN_v1.0.14.1 CRAN_v1.0.13.19 CRAN_v1.0.10.11 CRAN_v1.0.9.64
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DESCRIPTION
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Package: airGR Package: airGR
Type: Package Type: Package
Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
Version: 1.0.9.61 Version: 1.0.9.62
Date: 2017-11-09 Date: 2017-11-09
Authors@R: c( Authors@R: c(
person("Laurent", "Coron", role = c("aut", "trl")), person("Laurent", "Coron", role = c("aut", "trl")),
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NEWS.rmd
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...@@ -14,7 +14,7 @@ output: ...@@ -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 #### New features
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vignettes/V02.1_param_optim.Rmd
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...@@ -123,9 +123,9 @@ The existence of several local minima illustrates the importance of defining an ...@@ -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. 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: Here we use the following R implementation of some popular strategies:
* [DEoptim: differential evolution](https://cran.r-project.org/web/packages/DEoptim/index.html) * [DEoptim: differential evolution](https://cran.r-project.org/package=DEoptim)
* [hydroPSO: particle swarm](https://cran.r-project.org/web/packages/hydroPSO/index.html) * [hydroPSO: particle swarm](https://cran.r-project.org/package=hydroPSO)
* [Rmalschains: memetic algorithms](https://cran.r-project.org/web/packages/Rmalschains/index.html) * [Rmalschains: memetic algorithms](https://cran.r-project.org/package=Rmalschains)
## Differential Evolution ## Differential Evolution
```{r, warning=FALSE, results='hide'} ```{r, warning=FALSE, results='hide'}
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vignettes/V02.2_param_mcmc.Rmd
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...@@ -43,7 +43,7 @@ Please note that this vignette is only for illustration purposes and does not pr ...@@ -43,7 +43,7 @@ Please note that this vignette is only for illustration purposes and does not pr
## Standard Least Squares (SLS) Bayesian inference ## 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. 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. 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) { ...@@ -118,7 +118,7 @@ mcmcDRAM <- apply(ListIniParam, 2, function(iListIniParam) {
## MCMC diagnostics and visualisation tools ## MCMC diagnostics and visualisation tools
There are several diagnostics that can be used to check the convergence of the chains. 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. 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. 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)$mpsrf ...@@ -133,7 +133,7 @@ GelRub <- coda::gelman.diag(MultDRAM, autoburnin = TRUE)$mpsrf
GelRub 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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