Modification of layout appearance, as well as the template, change te…

…mplate to cosmo

.Rproj.user/6A9A55E1/pcs/files-pane.pper

@@ -5,5 +5,5 @@
             "ascending": true
         }
     ],
-    "path": "~/Desktop/WHIMs"
+    "path": "~/Desktop/WHIMs/R"
 }

.Rproj.user/6A9A55E1/pcs/windowlayoutstate.pper

@@ -1,12 +1,12 @@
 {
     "left": {
-        "splitterpos": 351,
+        "splitterpos": 345,
         "topwindowstate": "NORMAL",
         "panelheight": 682,
         "windowheight": 720
     },
     "right": {
-        "splitterpos": 523,
+        "splitterpos": 373,
         "topwindowstate": "NORMAL",
         "panelheight": 682,
         "windowheight": 720

NAMESPACE

@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+export(PartitionPipeline)
 export(eve_Fl)
 export(eve_T1)
 export(run_partition_pipeline)

R/PartitionPipeline.R

@@ -1,5 +1,11 @@
-#Purpose: Make an object for this pipeline, to streamline them
+#' Partition Pipeline for Image Analysis
+#'
+#' This R6 class is designed to streamline the processing pipeline for image analysis,
+#' including steps from initial processing to combining independent variables with
+#' reduced variables by tissue type by ROI.
+#'
 #' @import R6
+#' @export
 PartitionPipeline <- R6Class("PartitionPipeline",
                            public = list(
                              tind = NULL,

README.Rmd

@@ -11,6 +11,11 @@ knitr::opts_chunk$set(echo = TRUE)
 [![USC IMAGE](https://raw.githubusercontent.com/USCbiostats/badges/master/tommy-image-badge.svg)](https://image.usc.edu)
 <!-- badges: end -->
 
+## Introduction
+
+NeuroPartitioner is an advanced R package designed to convert NIfTI format T1/FL neuroimages into structured, high-dimensional 2D data frames, focusing on region of interest (ROI) based processing. This package incorporates a key algorithm called ‘partition’, which offers a fast, flexible framework for agglomerative partitioning based on the Direct-Measure-Reduce approach. This method ensures that each reduced variable maintains a user-specified minimum level of information while being interpretable, as each maps uniquely to one variable in the reduced dataset. The ‘partition’ algorithm, detailed in Millstein et al. (2020), allows for customization in variable selection, measurement of information loss, and data reduction methods. NeuroPartitioner is indispensable for researchers requiring efficient, accurate preparation of neuroimaging data for detailed statistical analysis and machine learning applications, enhancing the interpretability and utility of neuroimaging studies.
+
+
 ## Installation Instructions
 
 This document provides detailed steps to install the necessary dependencies for the package. Please follow the instructions carefully to ensure all dependencies are correctly installed.

README.md

@@ -7,6 +7,24 @@ NeuroPartitioner
 IMAGE](https://raw.githubusercontent.com/USCbiostats/badges/master/tommy-image-badge.svg)](https://image.usc.edu)
 <!-- badges: end -->
 
+## Introduction
+
+NeuroPartitioner is an advanced R package designed to convert NIfTI
+format T1/FL neuroimages into structured, high-dimensional 2D data
+frames, focusing on region of interest (ROI) based processing. This
+package incorporates a key algorithm called ‘partition’, which offers a
+fast, flexible framework for agglomerative partitioning based on the
+Direct-Measure-Reduce approach. This method ensures that each reduced
+variable maintains a user-specified minimum level of information while
+being interpretable, as each maps uniquely to one variable in the
+reduced dataset. The ‘partition’ algorithm, detailed in Millstein et
+al. (2020), allows for customization in variable selection, measurement
+of information loss, and data reduction methods. NeuroPartitioner is
+indispensable for researchers requiring efficient, accurate preparation
+of neuroimaging data for detailed statistical analysis and machine
+learning applications, enhancing the interpretability and utility of
+neuroimaging studies.
+
 ## Installation Instructions
 
 This document provides detailed steps to install the necessary

_pkgdown.yml

@@ -1,4 +1,18 @@
 url: https://jtian123.github.io/WHIMs/
 template:
   bootstrap: 5
+  bootswatch: cosmo  # Using the Flatly theme from Bootswatch
 
+navbar:
+  title: NeuroPartitioner
+  left:
+    - text: Home
+      href: index.html
+    - text: Get Started
+      href: articles/NeuroPartitioner.html  # Link directly to your vignette
+    - text: Reference
+      href: reference/index.html
+  right:
+    - text: View on source code
+      href: https://github.com/jtian123/WHIMs
+      icon: fab fa-github

vignettes/NeuroPartitioner.Rmd

@@ -35,8 +35,9 @@ datatable(lab_df[c("integer_label", "text_label", "structure")],
              caption = 'Interactive table of complete data from the brain regions dataset.')
 ```
 
-## Process T1-weighted Brain MRI Data with FSL and Register to EVE Atlas
+<br><br>  <!-- Adds two line breaks -->
 
+## Process T1-weighted Brain MRI Data with FSL and Register to EVE Atlas
 
 Ensure FSL is installed as per the instructions provided in the [package README](https://github.com/jtian123/WHIMs).
 
@@ -59,23 +60,31 @@ eve_T1(fpath, outpath, fsl_path , fsl_outputtype = "NIFTI_GZ")
 
 **fpath**: A character string specifying the path to one T1-weighted MRI file. The file should be in NIFTI file format (.nii.gz). Processing may take some time, so please be patient. For handling multiple MRI files, consider using parallel processing with R's parallel computation packages or through high-performance computing resources to improve efficiency.
 
+<br><br>  <!-- Adds two line breaks -->
+
 ## Run Partition Pipeline on Neuroimaging Data
 
 This section describes how to utilize the pipeline for processing neuroimaging data through sequential application of sophisticated algorithms and segmentation based on Regions of Interest (ROIs).
 
+<br>  <!-- Adds one line breaks -->
+
 ### Pipeline Overview:
 
 - **Super-Partition**: Applies Josh's super-partition algorithm, which considers 3D locations to group data based on ROIs.
 - **Partition Algorithm**: Processes data post Super-Partition using the [Partition algorithm](https://github.com/USCbiostats/partition), enhancing data structuring.
 - **Tissue Segmentation**: Segments the processed data by tissue type within each ROI.
 
+<br>  <!-- Adds one line breaks -->
+
 ### Practical Example:
 
 To process the ROI named "inferior_frontal_gyrus", identify the corresponding `tind` (in this example, `tind = 5`) from the **Region Labels and Structures** section. You'll need to set up a directory to manage all processing files and datasets. Note that the outputs from this pipeline will not be returned directly but will be stored at specified locations:
 
 - Intensity data: **/main_dir/partition/roi/thresh/tissue_type/cmb/intensities_whole.rds**
 - Volume data: **/main_dir/partition/roi/thresh/tissue_type/cmb/volume_whole.rds**
 
+<br>  <!-- Adds one line breaks -->
+
 ### Parallel Processing:
 
 This function is equipped with parallel processing capabilities, allowing users to specify the number of cores they wish to utilize. Increasing the number of cores will proportionally speed up the Partition process, offering significant time savings for large datasets.