add /doc/ folder

doc/HowTo.R

+## ---- include = FALSE---------------------------------------------------------
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+
+## ----setup--------------------------------------------------------------------
+library(kafino)
+library(dplyr)
+library(ggplot2)
+
+## -----------------------------------------------------------------------------
+data(spring1)
+
+# Dimension of this dataset
+dim(spring1)
+
+head(spring1)
+
+## ----error=TRUE---------------------------------------------------------------
+# --- Without Optimisation on initial parameters
+resu2<-kafino_fit(datain=spring1,
+              Tvar="dateNum",Yvar="Poids",
+              expertMin=30,expertMax=75,
+              Max=100,Min=10, 
+              X=c(56,0.5,42),
+              doOptim=FALSE,aa=0.001)
+              
+# flags are qualitative
+kafino_plot(resuin=resu2,typeG="quali",
+            Xvar="Day",Yvar="Poids",Ident="IDE")
+            
+# flags are quantitative
+kafino_plot(resuin=resu2,typeG="quanti",
+            Xvar="Day",Yvar="Poids",Ident="IDE")
+
+## ----error=TRUE---------------------------------------------------------------
+# --- With Optimisation on initial parameters
+resu1<-kafino_fit(datain=spring1,
+              Tvar="dateNum",Yvar="Poids",
+              expertMin=30,expertMax=75,
+              Max=100,Min=10, 
+              X=c(56,0.5,42),
+              doOptim=TRUE,aa=0.001)
+              
+# flags are qualitative
+kafino_plot(resuin=resu1,typeG="quali",
+            Xvar="Day",Yvar="Poids",Ident="IDE")
+            
+# flags are quantitative
+kafino_plot(resuin=resu1,typeG="quanti",
+            Xvar="Day",Yvar="Poids",Ident="IDE")
+
+## ----session,echo=FALSE,message=FALSE, warning=FALSE--------------------------
+  sessionInfo()
+

doc/HowTo.Rmd

+---
+title: "How to perform a kafino outlier detection"
+author: "B. Cloez & I. Sanchez"
+date:  "`r format(Sys.time(), '%B %d, %Y')`"
+output:
+  html_document:
+    toc: yes
+    toc_float: true
+    number_sections: true
+vignette: >
+  %\VignetteIndexEntry{How to perform a kafino outlier detection}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r setup}
+library(kafino)
+library(dplyr)
+library(ggplot2)
+```
+
+
+This vignette describes how to use the **kafino** algorithm on time courses in order to detect impulse noised outliers and predict the parameter of interest.
+
+RAJOUTER DU TEXTE
+
+# Description of the `spring1` dataset
+
+blablabla
+
+```{r}
+data(spring1)
+
+# Dimension of this dataset
+dim(spring1)
+
+head(spring1)
+```
+
+# Kafino algorithm
+## Initial parameters not optimized
+
+```{r,error=TRUE}
+# --- Without Optimisation on initial parameters
+resu2<-kafino_fit(datain=spring1,
+              Tvar="dateNum",Yvar="Poids",
+              expertMin=30,expertMax=75,
+              Max=100,Min=10, 
+              X=c(56,0.5,42),
+              doOptim=FALSE,aa=0.001)
+              
+# flags are qualitative
+kafino_plot(resuin=resu2,typeG="quali",
+            Xvar="Day",Yvar="Poids",Ident="IDE")
+            
+# flags are quantitative
+kafino_plot(resuin=resu2,typeG="quanti",
+            Xvar="Day",Yvar="Poids",Ident="IDE")
+```
+
+## Initial parameters optimized
+```{r,error=TRUE}
+# --- With Optimisation on initial parameters
+resu1<-kafino_fit(datain=spring1,
+              Tvar="dateNum",Yvar="Poids",
+              expertMin=30,expertMax=75,
+              Max=100,Min=10, 
+              X=c(56,0.5,42),
+              doOptim=TRUE,aa=0.001)
+              
+# flags are qualitative
+kafino_plot(resuin=resu1,typeG="quali",
+            Xvar="Day",Yvar="Poids",Ident="IDE")
+            
+# flags are quantitative
+kafino_plot(resuin=resu1,typeG="quanti",
+            Xvar="Day",Yvar="Poids",Ident="IDE")
+```
+
+
+# Session informations
+```{r session,echo=FALSE,message=FALSE, warning=FALSE}
+  sessionInfo()
+```