From 4670c982747e7a86c8ffc13be94622cceb1e33cf Mon Sep 17 00:00:00 2001
From: unknown <isabelle.sanchez@inra.fr>
Date: Thu, 6 Oct 2022 10:49:00 +0200
Subject: [PATCH] MaJ DESCRIPTION - CRAN suggestions
---
DESCRIPTION | 16 ++++++++--------
man/kfino.Rd | 4 ++--
2 files changed, 10 insertions(+), 10 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index ab69e37..14a81b1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -10,20 +10,20 @@ Author: Bertrand Cloez [aut],
Benedicte Fontez [ctr]
Maintainer: Isabelle Sanchez <isabelle.sanchez@inrae.fr>
Description: A method for detecting outliers with a Kalman filter on impulsed
- noised outliers and prediction on cleaned data. kfino is a robust sequential
- algorithm allowing to filter data with a large number of outliers. This
- algorithm is based on simple latent linear Gaussian processes as in the
+ noised outliers and prediction on cleaned data. 'kfino' is a robust
+ sequential algorithm allowing 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
- are data points that differ significantly from other observations. `ML`
- (Maximization Likelihood) and `EM` (Expectation-Maximization algorithm)
- algorithms were implemented in `kfino`. The method is described in full
- details in the following arxiv preprint: <https://arxiv.org/abs/2208.00961>.
+ are data points that differ significantly from other observations. 'ML'
+ (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm)
+ algorithms were implemented in 'kfino'. The method is described in full
+ details in the following arXiv e-Print: <arXiv:2208.00961>.
License: GPL-3
Depends: R (>= 4.1.0)
Encoding: UTF-8
LazyData: TRUE
URL: https://forgemia.inra.fr/isabelle.sanchez/kfino
-BugReports: https://forgemia.inra.fr/isabelle.sanchez/kfino/issues
+BugReports: https://forgemia.inra.fr/isabelle.sanchez/kfino/-/issues
Imports:
ggplot2,
dplyr,
diff --git a/man/kfino.Rd b/man/kfino.Rd
index a2ec5bf..86e5301 100644
--- a/man/kfino.Rd
+++ b/man/kfino.Rd
@@ -8,7 +8,7 @@
\description{
\if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
-A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. kfino is a robust sequential algorithm allowing 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 are data points that differ significantly from other observations. `ML` (Maximization Likelihood) and `EM` (Expectation-Maximization algorithm) algorithms were implemented in `kfino`. The method is described in full details in the following arxiv preprint: \url{https://arxiv.org/abs/2208.00961}.
+A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. 'kfino' is a robust sequential algorithm allowing 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 are data points that differ significantly from other observations. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following arXiv e-Print: \href{https://arxiv.org/abs/2208.00961}{arXiv:2208.00961}.
}
\details{
xxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx.
@@ -17,7 +17,7 @@ xxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx.
Useful links:
\itemize{
\item \url{https://forgemia.inra.fr/isabelle.sanchez/kfino}
- \item Report bugs at \url{https://forgemia.inra.fr/isabelle.sanchez/kfino/issues}
+ \item Report bugs at \url{https://forgemia.inra.fr/isabelle.sanchez/kfino/-/issues}
}
}
--
GitLab