title: "How to perform a kfino outlier detection using the EM or ML method"
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 kfino outlier detection using the EM or ML method}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup}
library(kfino)
library(dplyr)
library(ggplot2)
```
This vignette describes how to use the **kfino** algorithm on time courses in order to detect impulse noised outliers and predict the parameter of interest.
EM or ML methods
# Description of the `spring1` dataset
blablabla
```{r}
data(spring1)
# Dimension of this dataset
dim(spring1)
head(spring1)
```
# Kfino algorithm with optimized initial parameters
