make use of libraries slot

moved some dependencies into suggests and have struct check for packages on first use of object instead by populating the libraries slot of objects

Author: grlloyd

DESCRIPTION

@@ -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

NAMESPACE

@@ -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)

R/PLSDA_charts.R

@@ -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',

R/PLSDA_class.R

@@ -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',

R/PLSFC_class.R

@@ -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')
     )
 )

R/PLSR_class.R

@@ -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')
     )

R/sb_corr.R

@@ -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',

R/tSNE_class.R

@@ -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'
 
     )
 )

doc/iterator_example.R

@@ -1,8 +1,6 @@
 ## ----setup, include=FALSE--------------------------------------------------
 knitr::opts_chunk$set(
-    collapse = TRUE,
-    comment = "#>",
-    fig.align = 'center'
+    dpi=72
 )
 library(structToolbox)
 library(gridExtra)

doc/model_example.R

@@ -1,8 +1,6 @@
 ## ----setup, include=FALSE--------------------------------------------------
 knitr::opts_chunk$set(
-    collapse = TRUE,
-    comment = "#>",
-    fig.align = 'center'
+    dpi=72
 )
 library(structToolbox)
 library(gridExtra)