improve some document parts

DESCRIPTION

@@ -5,12 +5,19 @@ Authors@R: c(
   person("Bertrand", "Cloez", email = "bertrand.cloez@inrae.fr", role = c("aut")),
   person("Isabelle", "Sanchez", email = "isabelle.sanchez@inrae.fr", role = c("aut", "cre")),
   person("Benedicte", "Fontez", email = "benedicte.fontez@supagro.fr", role = c("ctr")))
-Author: Bertrand Cloez [aut, cre],
-  Isabelle Sanchez [ctr],
+Author: Bertrand Cloez [aut],
+  Isabelle Sanchez [aut, cre],
   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.
+  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>.
 License: GPL-3
 Depends: R (>= 4.1.0)
 Encoding: UTF-8

R/utils_functions.R

@@ -6,7 +6,7 @@
 # KBO_EM()
 #-------------------------------------------------------------------
 
-#' doutlier This function defines an outlier distribution (Surface of a
+#' doutlier defines an outlier distribution (Surface of a
 #' trapezium) and uses input parameters given in the main function kfino_fit()
 #'
 #' @param y numeric, point
@@ -15,7 +15,14 @@
 #' @param expertMax numeric, the maximal weight expected by the user
 #'
 #' @details this function is used to calculate an outlier distribution
-#'          following a trapezium shape
+#'          following a trapezium shape. 
+#'  \eqn{y \mapsto \text{doutlier}(y,K,\text{expertMin},\text{expertMax})}
+#'          is the probability density function on 
+#'  \eqn{[\text{expertMin},\text{expertMax}]} which is linear and verifies
+#'  \eqn{\text{doutlier}(\text{expertMax},K,\text{expertMin},\text{expertMax}) 
+#'  =K*\text{doutlier}(\text{expertMin},K,\text{expertMin},\text{expertMax}).}
+#'  In particular, when $K=1$ this corresponds to the uniform distribution.
+#'  
 #' @return a numeric value
 #' @export
 #'
@@ -34,7 +41,7 @@ doutlier<-function(y,
 #' KBO_known a function to calculate a likelihood on given parameters
 #'
 #' @param param list, see initial parameter list in \code{kfino_fit}
-#' @param threshold numeric, threshold for CI, default 0.5
+#' @param threshold numeric, threshold for confidence interval, default 0.5
 #' @param kappa numeric, truncation setting for likelihood optimization, 
 #'        default 10
 #' @param Y character, name of the numeric variable to predict in the  
@@ -50,8 +57,8 @@ doutlier<-function(y,
 #'  \item{prediction}{vector, the prediction of weights}
 #'  \item{label}{vector, probability to be an outlier}
 #'  \item{likelihood}{numeric, the calculated likelihood}
-#'  \item{lwr}{vector of lower bound CI of the prediction }
-#'  \item{upr}{vector of upper bound CI of the prediction }
+#'  \item{lwr}{vector of lower bound confidence interval of the prediction }
+#'  \item{upr}{vector of upper bound confidence interval of the prediction }
 #'  \item{flag}{char, is an outlier or not}
 #' }
 #' @export

man/KBO_known.Rd

@@ -9,7 +9,7 @@ KBO_known(param, threshold, kappa = 10, Y, Tps, N)
 \arguments{
 \item{param}{list, see initial parameter list in \code{kfino_fit}}
 
-\item{threshold}{numeric, threshold for CI, default 0.5}
+\item{threshold}{numeric, threshold for confidence interval, default 0.5}
 
 \item{kappa}{numeric, truncation setting for likelihood optimization, 
 default 10}
@@ -29,8 +29,8 @@ a list
  \item{prediction}{vector, the prediction of weights}
  \item{label}{vector, probability to be an outlier}
  \item{likelihood}{numeric, the calculated likelihood}
- \item{lwr}{vector of lower bound CI of the prediction }
- \item{upr}{vector of upper bound CI of the prediction }
+ \item{lwr}{vector of lower bound confidence interval of the prediction }
+ \item{upr}{vector of upper bound confidence interval of the prediction }
  \item{flag}{char, is an outlier or not}
 }
 }

man/doutlier.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/utils_functions.R
 \name{doutlier}
 \alias{doutlier}
-\title{doutlier This function defines an outlier distribution (Surface of a
+\title{doutlier defines an outlier distribution (Surface of a
 trapezium) and uses input parameters given in the main function kfino_fit()}
 \usage{
 doutlier(y, K, expertMin, expertMax)
@@ -20,12 +20,18 @@ doutlier(y, K, expertMin, expertMax)
 a numeric value
 }
 \description{
-doutlier This function defines an outlier distribution (Surface of a
+doutlier defines an outlier distribution (Surface of a
 trapezium) and uses input parameters given in the main function kfino_fit()
 }
 \details{
 this function is used to calculate an outlier distribution
-         following a trapezium shape
+         following a trapezium shape. 
+ \eqn{y \mapsto \text{doutlier}(y,K,\text{expertMin},\text{expertMax})}
+         is the probability density function on 
+ \eqn{[\text{expertMin},\text{expertMax}]} which is linear and verifies
+ \eqn{\text{doutlier}(\text{expertMax},K,\text{expertMin},\text{expertMax}) 
+ =K*\text{doutlier}(\text{expertMin},K,\text{expertMin},\text{expertMax}).}
+ In particular, when $K=1$ this corresponds to the uniform distribution.
 }
 \examples{
 doutlier(2,5,10,45)

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.
+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}.
 }
 \details{
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