diff --git a/DESCRIPTION b/DESCRIPTION
index a6074eef6714be39fc082d8ad56b648b53dae38d..2d31e79c54e9669fbb9557f1fbaa7cc25b679f06 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -38,3 +38,4 @@ BugReports: https://gitlab.irstea.fr/HYCAR-Hydro/airgr/-/issues
NeedsCompilation: yes
Encoding: UTF-8
VignetteBuilder: knitr
+RoxygenNote: 7.1.1
diff --git a/NAMESPACE b/NAMESPACE
index fe1f04810b57149b90498c4180c05cb87c03e55e..138db937ab679c768065c17552e32dd0effebf62 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -27,7 +27,7 @@ export(CreateCalibOptions)
export(CreateErrorCrit_GAPX)
export(CreateIniStates)
export(CreateInputsCrit)
-export(CreateInputsCrit_DeLavenne)
+export(CreateInputsCrit_Lavenne)
export(CreateInputsModel)
export(CreateRunOptions)
export(DataAltiExtrapolation_Valery)
diff --git a/R/CreateInputsCrit_DeLavenne.R b/R/CreateInputsCrit_Lavenne.R
similarity index 96%
rename from R/CreateInputsCrit_DeLavenne.R
rename to R/CreateInputsCrit_Lavenne.R
index 92f1ea4dca7a523502ec7c2112bf4887140bc287..f9bed6d1ac15d31ed5eb627821663e9dfc6c2a0a 100644
--- a/R/CreateInputsCrit_DeLavenne.R
+++ b/R/CreateInputsCrit_Lavenne.R
@@ -1,4 +1,4 @@
-CreateInputsCrit_DeLavenne <- function(FUN_CRIT = ErrorCrit_KGE,
+CreateInputsCrit_Lavenne <- function(FUN_CRIT = ErrorCrit_KGE,
InputsModel,
RunOptions,
Obs,
diff --git a/man/CreateInputsCrit_DeLavenne.Rd b/man/CreateInputsCrit_Lavenne.Rd
similarity index 91%
rename from man/CreateInputsCrit_DeLavenne.Rd
rename to man/CreateInputsCrit_Lavenne.Rd
index a16fd96f6e2856c048b312935af8bfc4df60a2fb..045973000d816da8d114ce4dfa5ba78bdddeff03 100644
--- a/man/CreateInputsCrit_DeLavenne.Rd
+++ b/man/CreateInputsCrit_Lavenne.Rd
@@ -1,8 +1,8 @@
\encoding{UTF-8}
-\name{CreateInputsCrit_DeLavenne}
-\alias{CreateInputsCrit_DeLavenne}
+\name{CreateInputsCrit_Lavenne}
+\alias{CreateInputsCrit_Lavenne}
\title{Creation of the InputsCrit object for De Lavenne Criterion}
@@ -14,7 +14,7 @@ Creation of the \code{InputsCrit} object required to the \code{\link{ErrorCrit}}
\usage{
-CreateInputsCrit_DeLavenne(FUN_CRIT = ErrorCrit_KGE,
+CreateInputsCrit_Lavenne(FUN_CRIT = ErrorCrit_KGE,
InputsModel,
RunOptions,
Obs,
@@ -55,7 +55,7 @@ CreateInputsCrit_DeLavenne(FUN_CRIT = ErrorCrit_KGE,
\value{
-\code{CreateInputsCrit_DeLavenne} returns an object of class \emph{Compo} that is a list of lists such as the one described in \code{\link{CreateInputsCrit}}.
+\code{CreateInputsCrit_Lavenne} returns an object of class \emph{Compo} that is a list of lists such as the one described in \code{\link{CreateInputsCrit}}.
Items \code{Weights} of the criteria are respectively equal to \code{k} and \code{k * max(0,AprCrit)}.
@@ -70,7 +70,7 @@ The parameters \code{FUN_CRIT}, \code{Obs}, \code{VarObs}, \code{BoolCrit}, \cod
\code{\link{ErrorCrit_RMSE}} cannot be used in a composite criterion since it is not a unitless value.
-\code{CreateInputsCrit_DeLavenne} creates a composed criterion in respect with Equations 1 and 2 of de Lavenne et al. (2019).
+\code{CreateInputsCrit_Lavenne} creates a composed criterion in respect with Equations 1 and 2 of de Lavenne et al. (2019).
}
@@ -101,7 +101,7 @@ OutputsModel <- RunModel_GR4J(InputsModel = InputsModel,
AprParamR <- c(X1 = 157, X2 = 0.8, X3 = 100, X4 = 1.5)
## Single efficiency criterion: GAPX with a priori parameters
-IC_DL <- CreateInputsCrit_DeLavenne(InputsModel = InputsModel,
+IC_DL <- CreateInputsCrit_Lavenne(InputsModel = InputsModel,
RunOptions = RunOptions,
Obs = BasinObs$Qmm[Ind_Run],
AprParamR = AprParamR)
diff --git a/tests/testthat/test-CreateInputsCrit_DeLavenne.R b/tests/testthat/test-CreateInputsCrit_Lavenne.R
similarity index 92%
rename from tests/testthat/test-CreateInputsCrit_DeLavenne.R
rename to tests/testthat/test-CreateInputsCrit_Lavenne.R
index 8aa40e49b9c114a3de07d85899e5f3debbb75b08..c61f1a0055f362696e10409ca883e2771db78361 100644
--- a/tests/testthat/test-CreateInputsCrit_DeLavenne.R
+++ b/tests/testthat/test-CreateInputsCrit_Lavenne.R
@@ -1,4 +1,4 @@
-context("CreateInputsCrit_DeLavenne")
+context("CreateInputsCrit_Lavenne")
data(L0123001)
InputsModel <- CreateInputsModel(FUN_MOD = RunModel_GR4J, DatesR = BasinObs$DatesR,
@@ -14,7 +14,7 @@ OutputsModel <- RunModel_GR4J(InputsModel = InputsModel,
RunOptions = RunOptions, Param = Param)
k <- 0.15
-IC_DL <- CreateInputsCrit_DeLavenne(FUN_CRIT = ErrorCrit_KGE,
+IC_DL <- CreateInputsCrit_Lavenne(FUN_CRIT = ErrorCrit_KGE,
InputsModel = InputsModel,
RunOptions = RunOptions,
Obs = BasinObs$Qmm[Ind_Run],
@@ -40,7 +40,7 @@ test_that("should return KGE*(1-k)+k with parameters matching a priori parameter
test_that("should return proper error if mismatch number of parameters", {
expect_error(
- CreateInputsCrit_DeLavenne(FUN_CRIT = ErrorCrit_KGE,
+ CreateInputsCrit_Lavenne(FUN_CRIT = ErrorCrit_KGE,
InputsModel = InputsModel,
RunOptions = RunOptions,
Obs = BasinObs$Qmm[Ind_Run],
diff --git a/vignettes/V05_sd_model.Rmd b/vignettes/V05_sd_model.Rmd
index f7f4415c9a7108818e36cd91034d4c680b54c633..333284e8c64c30178624ab99c070d74ad36d0a94 100644
--- a/vignettes/V05_sd_model.Rmd
+++ b/vignettes/V05_sd_model.Rmd
@@ -201,10 +201,10 @@ ParamDownTheo <- c(Velocity, OutputsCalibUp$ParamFinalR)
The De Lavenne criterion is initialised with the a priori parameter set and the value of the KGE of the upstream basin.
```{r}
-IC_DeLavenne <- CreateInputsCrit_DeLavenne(InputsModel = InputsModelDown2,
+IC_Lavenne <- CreateInputsCrit_Lavenne(InputsModel = InputsModelDown2,
RunOptions = RunOptionsDown,
Obs = QObsDown[Ind_Run],
- AprParamR = ParamDownTheo,
+ AprParamR = ParamDownTheo,
AprCrit = OutputsCalibUp$CritFinal)
```
@@ -213,7 +213,7 @@ The De Lavenne criterion is used instead of the KGE for calibration with regular
```{r}
OutputsCalibDown3 <- Calibration_Michel(InputsModel = InputsModelDown2,
RunOptions = RunOptionsDown,
- InputsCrit = IC_DeLavenne,
+ InputsCrit = IC_Lavenne,
CalibOptions = CalibOptionsDown,
FUN_MOD = RunModel_GR4J)
```
@@ -238,7 +238,7 @@ Both calibrations overestimate this parameter:
```{r}
mVelocity <- matrix(c(Velocity,
OutputsCalibDown1$ParamFinalR[1],
- OutputsCalibDown2$ParamFinalR[1],
+ OutputsCalibDown2$ParamFinalR[1],
OutputsCalibDown3$ParamFinalR[1]),
ncol = 1,
dimnames = list(c("theoretical",
@@ -251,7 +251,7 @@ knitr::kable(mVelocity)
## Value of the performance criteria with theoretical calibration
-Theoretically, the parameters of the downstream GR4J model should be the same as the upstream one with the velocity as extra parameter :
+Theoretically, the parameters of the downstream GR4J model should be the same as the upstream one with the velocity as extra parameter :
```{r}
OutputsModelDownTheo <- RunModel(InputsModel = InputsModelDown2,