diff --git a/NAMESPACE b/NAMESPACE
index 1a3e3ed54360390a81eda0dde944787f01011902..54c0e1f1b2ff02ae5f1622d53479c3a292c62f5a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -3,9 +3,9 @@
export(KBO_EM)
export(KBO_L)
export(KBO_known)
+export(doutlier)
export(kfino_fit)
export(kfino_plot)
-export(loi_outlier)
importFrom(dplyr,"%>%")
importFrom(dplyr,.data)
importFrom(dplyr,arrange)
diff --git a/R/utils_functions.R b/R/utils_functions.R
index 0adf66ef4d48aaa19d57b0417ae72969d9b468ed..ef18f9cf312e67575796599618504778f112d71a 100644
--- a/R/utils_functions.R
+++ b/R/utils_functions.R
@@ -1,12 +1,12 @@
#-------------------------------------------------------------------
# utils_functions.R: some useful functions for kfino method
-# loi_outlier()
+# doutlier()
# KBO_known()
# KBO_L()
# KBO_EM()
#-------------------------------------------------------------------
-#' loi_outlier This function defines an outlier distribution (Surface of a
+#' doutlier This function defines an outlier distribution (Surface of a
#' trapezium) and uses input parameters given in the main function kfino_fit()
#'
#' @param y numeric, point
@@ -20,8 +20,8 @@
#' @export
#'
#' @examples
-#' loi_outlier(2,5,10,45)
-loi_outlier<-function(y,
+#' doutlier(2,5,10,45)
+doutlier<-function(y,
K,
expertMin,
expertMax){
@@ -93,7 +93,7 @@ KBO_known<-function(param,threshold,kappa=10,Y,Tps,N){
m1= (sigma2_pp*m0 + Y[1]*sigma2_m0)/(sigma2_m0+sigma2_pp)
sigma1=(sigma2_m0*sigma2_pp)/(sigma2_m0+sigma2_pp)
- l0<- loi_outlier(Y[1],K,expertMin,expertMax)
+ l0<- doutlier(Y[1],K,expertMin,expertMax)
loinorm1<-dnorm(Y[1],m0, sqrt(sigma2_m0+sigma2_pp))
p0= ((1-pp)*l0) / (pp*loinorm1 + (1-pp)*l0)
@@ -124,7 +124,7 @@ KBO_known<-function(param,threshold,kappa=10,Y,Tps,N){
qnew=rep(0 ,2^(k+1))
diffTps<-Tps[k+1] - Tps[k]
#--- numérateur de pu0
- tpbeta<-loi_outlier(Y[k+1],K,expertMin,expertMax)
+ tpbeta<-doutlier(Y[k+1],K,expertMin,expertMax)
pnew[1:(2^k)]=p[1:(2^k)]*(1-pp)*tpbeta
Lnew[1:(2^k)]=L[1:(2^k)]*tpbeta
@@ -170,7 +170,7 @@ KBO_known<-function(param,threshold,kappa=10,Y,Tps,N){
diffTps<-Tps[k+1] - Tps[k]
#--- pu0 numerator
- tpbeta<-loi_outlier(Y[k+1],K,expertMin,expertMax)
+ tpbeta<-doutlier(Y[k+1],K,expertMin,expertMax)
pnew[1:(2^kappa)]=p[1:(2^kappa)]*(1-pp)*tpbeta
Lnew[1:(2^kappa)]=L[1:(2^kappa)]*tpbeta
@@ -284,7 +284,7 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,dix){
m1= (sigma2_pp*m0 + Y[1]*sigma2_m0)/(sigma2_m0+sigma2_pp)
sigma1=(sigma2_m0*sigma2_pp)/(sigma2_m0+sigma2_pp)
- l0<- loi_outlier(Y[1],K,expertMin,expertMax)
+ l0<- doutlier(Y[1],K,expertMin,expertMax)
loinorm1<-dnorm(Y[1],m0, sqrt(sigma2_m0+sigma2_pp))
p0= ((1-pp)*l0) / (pp*loinorm1 + (1-pp)*l0)
@@ -308,7 +308,7 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,dix){
qnew=rep(0 ,2^(k+1))
diffTps<-Tps[k+1] - Tps[k]
#--- numerator of pu0
- tpbeta<-loi_outlier(Y[k+1],K,expertMin,expertMax)
+ tpbeta<-doutlier(Y[k+1],K,expertMin,expertMax)
pnew[1:(2^k)]=p[1:(2^k)]*(1-pp)*tpbeta
Lnew[1:(2^k)]=L[1:(2^k)]*tpbeta
mnew[1:(2^k)]= m[1:(2^k)]*exp(-aa*diffTps) + mm*(1-exp(-aa*diffTps)) #m_u0
@@ -343,7 +343,7 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,dix){
qnew=rep(0 ,2^(kappa+1))
diffTps<-Tps[k+1] - Tps[k]
#--- numerator of pu0
- tpbeta<-loi_outlier(Y[k+1],K,expertMin,expertMax)
+ tpbeta<-doutlier(Y[k+1],K,expertMin,expertMax)
pnew[1:(2^kappa)]=p[1:(2^kappa)]*(1-pp)*tpbeta
Lnew[1:(2^kappa)]=L[1:(2^kappa)]*tpbeta
mnew[1:(2^kappa)]= m[1:(2^kappa)]*exp(-aa*diffTps) + mm*(1-exp(-aa*diffTps)) #m_u0
@@ -438,7 +438,7 @@ KBO_EM<-function(param,kappaOpt,Y,Tps,N,dix){
m1= (sigma2_pp*m0 + Y[1]*sigma2_m0)/(sigma2_m0+sigma2_pp)
sigma1=(sigma2_m0*sigma2_pp)/(sigma2_m0+sigma2_pp)
- l0<- loi_outlier(Y[1],K,expertMin,expertMax)
+ l0<- doutlier(Y[1],K,expertMin,expertMax)
loinorm1<-dnorm(Y[1],m0, sqrt(sigma2_m0+sigma2_pp))
p0= ((1-pp)*l0) / (pp*loinorm1 + (1-pp)*l0)
@@ -477,7 +477,7 @@ KBO_EM<-function(param,kappaOpt,Y,Tps,N,dix){
qnew=rep(0 ,2^(k+1))
diffTps<-Tps[k+1] - Tps[k]
#--- numérateur de pu0
- tpbeta<-loi_outlier(Y[k+1],K,expertMin,expertMax)
+ tpbeta<-doutlier(Y[k+1],K,expertMin,expertMax)
pnew[1:(2^k)]=p[1:(2^k)]*(1-pp)*tpbeta
Lnew[1:(2^k)]=L[1:(2^k)]*tpbeta
mnew[1:(2^k)]= m[1:(2^k)]*exp(-aa*diffTps) + mm*(1-exp(-aa*diffTps)) #m_u0
@@ -540,7 +540,7 @@ KBO_EM<-function(param,kappaOpt,Y,Tps,N,dix){
qnew=rep(0 ,2^(kappa+1))
diffTps<-Tps[k+1] - Tps[k]
#--- pu0 numerator
- tpbeta<-loi_outlier(Y[k+1],K,expertMin,expertMax)
+ tpbeta<-doutlier(Y[k+1],K,expertMin,expertMax)
pnew[1:(2^kappa)]=p[1:(2^kappa)]*(1-pp)*tpbeta
Lnew[1:(2^kappa)]=L[1:(2^kappa)]*tpbeta
# m_uO
diff --git a/man/loi_outlier.Rd b/man/doutlier.Rd
similarity index 71%
rename from man/loi_outlier.Rd
rename to man/doutlier.Rd
index 15b1de6d9bca1c468a192fd2027ea7b749d659a9..efd7eaa4a3348bd58deace1e7ef4401e3586dfcd 100644
--- a/man/loi_outlier.Rd
+++ b/man/doutlier.Rd
@@ -1,11 +1,11 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils_functions.R
-\name{loi_outlier}
-\alias{loi_outlier}
-\title{loi_outlier This function defines an outlier distribution (Surface of a
+\name{doutlier}
+\alias{doutlier}
+\title{doutlier This function defines an outlier distribution (Surface of a
trapezium) and uses input parameters given in the main function kfino_fit()}
\usage{
-loi_outlier(y, K, expertMin, expertMax)
+doutlier(y, K, expertMin, expertMax)
}
\arguments{
\item{y}{numeric, point}
@@ -20,7 +20,7 @@ loi_outlier(y, K, expertMin, expertMax)
a numeric value
}
\description{
-loi_outlier This function defines an outlier distribution (Surface of a
+doutlier This function defines an outlier distribution (Surface of a
trapezium) and uses input parameters given in the main function kfino_fit()
}
\details{
@@ -28,5 +28,5 @@ this function is used to calculate an outlier distribution
following a trapezium shape
}
\examples{
-loi_outlier(2,5,10,45)
+doutlier(2,5,10,45)
}