@@ -77,7 +77,6 @@ Depends: struct

 Imports: ggplot2,

  pmp,

  gridExtra,

- pls,

  sp,

  scales,

  stats,

@@ -90,7 +89,6 @@ Imports: ggplot2,

  nlme,

  ggthemes,

  ggdendro,

- Rtsne,

  datasets

 RoxygenNote: 6.1.1

 Suggests: 

@@ -99,7 +97,9 @@ Suggests:

  knitr,

  rmarkdown,

  BiocStyle,

- sbcms

+ pls,

+ sbcms,

+ Rtsne

 Remotes: computational-metabolomics/pmp,

   computational-metabolomics/struct,

   computational-metabolomics/sbcms

@@ -94,7 +94,6 @@ exportMethods(model.predict)

 exportMethods(model.reverse)

 exportMethods(model.train)

 exportMethods(run)

-import(Rtsne)

 import(agricolae)

 import(car)

 import(datasets)

@@ -111,7 +110,5 @@ import(reshape2)

 import(scales)

 import(stats)

 import(struct)

-importFrom(pls,plsr)

-importFrom(pls,scores)

 importFrom(scales,squish)

 importFrom(sp,point.in.polygon)

@@ -23,6 +23,7 @@ plsda_scores_plot<-setClass(

     prototype = list(name='PLSDA scores plot',

         description='scatter plot of PLSDA component scores',

         type="scatter",

+        libraries=c('pls','ggplot2'),

         params.components=entity(name='Components to plot',

             value=c(1,2),

             type='numeric',

@@ -2,7 +2,6 @@

 #'

 #' Partial least squares (PLS) discriminant analysis (DA) model class. This object can be used to train/apply PLS models.

 #' @export PLSDA

-#' @importFrom pls plsr scores

 #' @examples

 #' M = PLSDA()

 PLSDA<-setClass(

@@ -26,6 +25,7 @@ PLSDA<-setClass(

     prototype = list(name='Partial least squares discriminant analysis',

         type="classification",

         predicted='pred',

+        libraries='pls',

         params.number_components=entity(value = 2,

             name = 'Number of PLS components',

             description = 'The number of PLS components to use',

@@ -2,7 +2,6 @@

 #'

 #' Partial least squares (PLS) fold change estimates

 #' @export PLSFC

-#' @importFrom pls plsr scores

 #' @include fold_change_class.R

 #' @examples

 #' C = PLSFC()

@@ -15,6 +14,7 @@ PLSFC<-setClass(

     prototype = list(name='Partial least squares discriminant analysis',

         type="classification",

         predicted='pred',

+        libraries=c('pls'),

         params.number_components=entity(value = 2,name = 'Number of PLS components',description = 'The number of PLS components to use',type = 'numeric')

     )

 )

@@ -2,7 +2,6 @@

 #'

 #' Partial least squares (PLS) Regression model class. This object can be used to train/apply PLS models.

 #' @export PLSR

-#' @importFrom pls plsr scores

 #' @examples

 #' M = PLSR()

 PLSR<-setClass(

@@ -26,6 +25,7 @@ PLSR<-setClass(

     prototype = list(name='Partial least squares regression',

         type="regression",

         predicted='pred',

+        libraries='pls',

         params.number_components=entity(value = 2,name = 'Number of PLS components',description = 'The number of PLS components to use',type = 'numeric'),

         params.factor_name=entity(name='Factor name', description='A vector of sample_meta column names to use')

     )

@@ -23,6 +23,7 @@ sb_corr<-setClass(

         correct for signal drift and batch differences in mass spectrometry data.',

         type = 'correction',

         predicted = 'corrected',

+        libraries='sbcms',

         params.order_col=entity(

             name = 'Sample run order column',

@@ -2,7 +2,6 @@

 #'

 #' t-Distributed Stochastic Neighbor Embedding (tSNE) class. This object can be used to train/apply tSNE models to dataset objects.

 #'

-#' @import Rtsne

 #' @export tSNE

 #' @examples

 #' M = tSNE()

@@ -28,7 +27,7 @@ tSNE<-setClass(

         description='t-Distributed Stochastic Neighbor Embedding.',

         type="preprocessing",

         predicted='tsne',

-

+        libraries='Rtsne',

         params.dims=2,

         params.perplexity=30,

         params.max_iter=1000,

@@ -83,7 +82,8 @@ tSNE_scatter<-setClass(

         ),

     prototype = list(name='Feature boxplot',

         description='plots the new representation of data after applying tSNE.',

-        type="scatter"

+        type="scatter",

+        libraries='Rtsne'

     )

 )

@@ -1,8 +1,6 @@

 ## ----setup, include=FALSE--------------------------------------------------

 knitr::opts_chunk$set(

-    collapse = TRUE,

-    comment = "#>",

-    fig.align = 'center'

+    dpi=72

 )

 library(structToolbox)

 library(gridExtra)

@@ -1,8 +1,6 @@

 ## ----setup, include=FALSE--------------------------------------------------

 knitr::opts_chunk$set(

-    collapse = TRUE,

-    comment = "#>",

-    fig.align = 'center'

+    dpi=72

 )

 library(structToolbox)

 library(gridExtra)

@@ -16,9 +16,7 @@ vignette: >

 ```{r setup, include=FALSE}

 knitr::opts_chunk$set(

-    collapse = TRUE,

-    comment = "#>",

-    fig.align = 'center'

+    dpi=72

 )

 library(structToolbox)

 library(gridExtra)

@@ -17,9 +17,7 @@ vignette: >

 ```{r setup, include=FALSE}

 knitr::opts_chunk$set(

-    collapse = TRUE,

-    comment = "#>",

-    fig.align = 'center'

+    dpi=72

 )

 library(structToolbox)

 library(gridExtra)