fixed a bug in itemize correction

R/combine.kernels.R

@@ -2,17 +2,6 @@
 #' 
 #' Compute multiple kernels into a single meta-kernel
 #'
-#' @details
-#' The arguments \code{method} allows to specify the Unsupervised Multiple
-#' Kernel Learning (UMKL) method to use: 
-#'   \item{\code{"STATIS-UMKL"}}{: combines input kernels into the best 
-#'   consensus of all kernels;}
-#'   \item \code{"full-UMKL"}{: computes a kernel that minimizes the distortion 
-#'   between the meta-kernel and the k-NN graphs obtained from all input 
-#'   kernels;}
-#'   \item \code{"sparse-UMKL"}{: a sparse variant of the \code{"full-UMKL"} 
-#'   approach.}
-#'
 #' @param ... list of kernels (called 'blocks') computed on different datasets 
 #' and measured on the same samples.
 #' @param scale boleean. If \code{scale = TRUE}, each block is standardized to 
@@ -26,14 +15,25 @@
 #' @param rho integer. Parameters for the augmented Lagrangian method. Default: 
 #' \code{20}.
 #' 
-#' @return \code{combine.kernels} returns an object of classes \code{"kernel"} and 
-#' \code{"metaKernel"}, a list that contains the following components: \itemize{
+#' @return \code{combine.kernels} returns an object of classes \code{"kernel"} 
+#' and \code{"metaKernel"}, a list that contains the following components: 
 #'   \item{kernel}{: the computed meta-kernel matrix;}
 #'   \item{X}{: the dataset from which the kernel has been computed, as given by
 #'   the function \code{\link{compute.kernel}}. Can be \code{NULL} if a kernel
 #'   matrix was passed to this function;}
 #'   \item{weights}{: a vector containing the weights used to combine the 
 #'   kernels.} 
+#'   
+#' @details
+#' The arguments \code{method} allows to specify the Unsupervised Multiple
+#' Kernel Learning (UMKL) method to use: \itemize{
+#'   \item{\code{"STATIS-UMKL"}}{: combines input kernels into the best 
+#'   consensus of all kernels;}
+#'   \item \code{"full-UMKL"}{: computes a kernel that minimizes the distortion 
+#'   between the meta-kernel and the k-NN graphs obtained from all input 
+#'   kernels;}
+#'   \item \code{"sparse-UMKL"}{: a sparse variant of the \code{"full-UMKL"} 
+#'   approach.}
 #' }
 #' 
 #' @author Jerome Mariette <jerome.mariette@@inrae.fr>

R/compute.kernel.R

@@ -11,7 +11,7 @@
 #' \code{"gaussian.radial.basis"}, \code{"poisson"} or \code{"phylogenetic"}. 
 #' Default: \code{"linear"}.
 #' @param ... the kernel function arguments. Valid parameters for 
-#' pre-implemented kernels are: 
+#' pre-implemented kernels are: \itemize{
 #'     \item \code{phylogenetic.tree} (\code{"phylogenetic"}): an instance of 
 #'     phylo-class that contains a phylogenetic tree (required).
 #'     \item \code{scale} (\code{"linear"} or \code{"gaussian.radial.basis"}): 
@@ -30,6 +30,7 @@
 #'     \item \code{normalization} (\code{"poisson"}): character. Can be 
 #'     \code{"deseq"} (more robust), \code{"mle"} (less robust) or 
 #'     \code{"quantile"}.
+#' }
 #' @param test.pos.semidef boleean. If \code{test.pos.semidef = TRUE}, the 
 #' positive semidefiniteness of the resulting matrix is checked.
 #' 

man/combine.kernels.Rd

@@ -31,22 +31,21 @@ local topology of the datasets from each kernel. Default: \code{5}.}
 \code{20}.}
 }
 \value{
-\code{combine.kernels} returns an object of classes \code{"kernel"} and 
-\code{"metaKernel"}, a list that contains the following components: \itemize{
+\code{combine.kernels} returns an object of classes \code{"kernel"} 
+and \code{"metaKernel"}, a list that contains the following components: 
   \item{kernel}{: the computed meta-kernel matrix;}
   \item{X}{: the dataset from which the kernel has been computed, as given by
   the function \code{\link{compute.kernel}}. Can be \code{NULL} if a kernel
   matrix was passed to this function;}
   \item{weights}{: a vector containing the weights used to combine the 
-  kernels.} 
-}
+  kernels.}
 }
 \description{
 Compute multiple kernels into a single meta-kernel
 }
 \details{
 The arguments \code{method} allows to specify the Unsupervised Multiple
-Kernel Learning (UMKL) method to use: 
+Kernel Learning (UMKL) method to use: \itemize{
   \item{\code{"STATIS-UMKL"}}{: combines input kernels into the best 
   consensus of all kernels;}
   \item \code{"full-UMKL"}{: computes a kernel that minimizes the distortion 
@@ -55,6 +54,7 @@ Kernel Learning (UMKL) method to use:
   \item \code{"sparse-UMKL"}{: a sparse variant of the \code{"full-UMKL"} 
   approach.}
 }
+}
 \examples{
 data(TARAoceans)
 

man/compute.kernel.Rd

@@ -18,7 +18,7 @@ pre-implemented, that can be used by setting \code{kernel.func} to one of the
 Default: \code{"linear"}.}
 
 \item{...}{the kernel function arguments. Valid parameters for 
-pre-implemented kernels are: 
+pre-implemented kernels are: \itemize{
     \item \code{phylogenetic.tree} (\code{"phylogenetic"}): an instance of 
     phylo-class that contains a phylogenetic tree (required).
     \item \code{scale} (\code{"linear"} or \code{"gaussian.radial.basis"}): 
@@ -36,7 +36,8 @@ pre-implemented kernels are:
     \code{"cao"}.
     \item \code{normalization} (\code{"poisson"}): character. Can be 
     \code{"deseq"} (more robust), \code{"mle"} (less robust) or 
-    \code{"quantile"}.}
+    \code{"quantile"}.
+}}
 
 \item{test.pos.semidef}{boleean. If \code{test.pos.semidef = TRUE}, the 
 positive semidefiniteness of the resulting matrix is checked.}