From ccb12e0cb6ee69dd89669fd7fb25e3b658306842 Mon Sep 17 00:00:00 2001
From: Bertrand CLOEZ <’bertrand.cloez@inrae.fr’>
Date: Mon, 5 Sep 2022 15:41:29 +0200
Subject: [PATCH] Modification vignette howto
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vignettes/HowTo.Rmd | 12 ++++++++++--
1 file changed, 10 insertions(+), 2 deletions(-)
diff --git a/vignettes/HowTo.Rmd b/vignettes/HowTo.Rmd
index 43dc2d4..4f96bf0 100644
--- a/vignettes/HowTo.Rmd
+++ b/vignettes/HowTo.Rmd
@@ -29,11 +29,19 @@ 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.
-RAJOUTER DU TEXTE
+
+Kalman filter with impulse noised outliers (Kfino) is a robust sequential algorithm to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These latters are data points that differ significantly from other observations.
+
+The method is described in detail in arxiv preprint https://arxiv.org/abs/2208.00961.
# Description of the `spring1` dataset
-blablabla
+To test the Kfino algorithm, we enclosed a real data set into the Kfino package. This data set was crated for the publication:
+
+https://doi.org/10.1016/j.compag.2018.08.022
+
+To trial the feasibility of using an automated weighing prototype suitable for a range of contrasting sheep farming systems, the authors automatically record the weight of 15 sheeps grazing outdoor in spring.
+
```{r}
data(spring1)
--
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