Renamed some of the main files from the old NDTr to the new NeuroDeco…

…deR (and NDR)

DESCRIPTION

@@ -1,5 +1,5 @@
-Package: NDTr
-Title: The Neural Decoding Toolbox in R
+Package: NeuroDecodeR
+Title: Neural Population Decoding in R
 Version: 0.0.0.9003
 Authors@R: c(
   person("Ethan", "Meyers", email = "[email protected]", 
@@ -8,11 +8,11 @@ Authors@R: c(
 Description: A package that allows one to do a neural decoding analysis in R. 
     The package is based on 5 abstract object types: datasources (DS), 
     features preprocessors (FP), classifiers (CL), and result metrics (RM) 
-    and cross-validators (CV). By combing different versions of these 4 object 
+    and cross-validators (CV). By combing different versions of these 5 object 
     types together, it is possible to run a range of different decoding
     analyses. See www.readout.info for more information. 
-URL: https://github.com/emeyers/NDTr
-BugReports: https://github.com/emeyers/NDTr/issues
+URL: https://github.com/emeyers/NeuroDecodeR
+BugReports: https://github.com/emeyers/NeuroDecodeR/issues
 Depends: R (>= 3.5.0)
 License: GPL-3
 LazyData: true

R/NDTr.R → R/NeuroDecodeR.R

@@ -1,10 +1,10 @@
-#' NDTr: A package for neural decoding analyses
+#' NeuroDecodeR: A package for neural decoding analyses
 #'
-#' The NDTr is a package that makes it easy to do neural decoding analyses.
+#' The NeuroDecodeR makes it easy to do neural decoding analyses in R!
 #'
-#' The NDTr is built around five abstract object types that work together in a
-#' modular way to allow a range of neural decoding analyses. These five object
-#' types are:
+#' The NeuroDecodeR (NDR) is built around five abstract object types that work
+#' together in a modular way to allow a range of neural decoding analyses. These
+#' five object types are:
 #'
 #' 1. Datasources (DS): Generate training and test splits of the data.
 #'
@@ -32,13 +32,13 @@
 #'  2. `binned format` contains data from multiple sites where the data is more
 #'  coarsely binned across time.
 #'
-#'  A user of the NDTr will typically store their data in `raster format` and
+#'  A user of the NDR will typically store their data in `raster format` and
 #'  then use the [create_binned_data()] to create a `binned format` data file
 #'  that will be used in the decoding analysis.
 #'
 #'
 #' @docType package
-#' @name NDTr
+#' @name NeuroDecodeR
 #'
 #' @import foreach
 #' @import dplyr

README.md

@@ -1,18 +1,18 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# NDTr: The Neural Decoding Toolbox in R
+# NeuroDecodeR: Neural Decoding in R
 
 <!-- badges: start -->
 
 [![Travis build
-status](https://travis-ci.com/emeyers/NDTr.svg?branch=master)](https://travis-ci.com/emeyers/NDTr)
+status](https://travis-ci.com/emeyers/NeuroDecodeR.svg?branch=master)](https://travis-ci.com/emeyers/NeuroDecodeR)
 
 [![AppVeyor build
-status](https://ci.appveyor.com/api/projects/status/github/emeyers/NDTr?branch=master&svg=true)](https://ci.appveyor.com/project/emeyers/NDTr)
+status](https://ci.appveyor.com/api/projects/status/github/emeyers/NeuroDecodeR?branch=master&svg=true)](https://ci.appveyor.com/project/emeyers/NeuroDecodeR)
 
 [![Coveralls test
-coverage](https://coveralls.io/repos/github/emeyers/NDTr/badge.svg)](https://coveralls.io/r/emeyers/NDTr?branch=master)
+coverage](https://coveralls.io/repos/github/emeyers/NeuroDecodeR/badge.svg)](https://coveralls.io/r/emeyers/NeuroDecodeR?branch=master)
 
 <!-- badges: end -->
 
@@ -22,16 +22,16 @@ coverage](https://coveralls.io/repos/github/emeyers/NDTr/badge.svg)](https://cov
 
 *Neural decoding* is a data analysis method that uses pattern
 classifiers to predict experimental conditions based on neural activity.
-The Neural Decoding Toolbox in R (NDTr) makes it easy to do neural
+The Neural Decoding in R (NDR) package makes it easy to do neural
 decoding analyses in R.
 
 ## Installation
 
-You can install NDTr from github using:
+You can install NDR from github using:
 
 ``` r
 # install.packages("devtools")
-devtools::install_github("emeyers/NDTr")
+devtools::install_github("emeyers/NDR")
 ```
 
 ## Usage
@@ -50,16 +50,17 @@ The package is based on 5 abstract object types:
 By combing different versions of these 5 object types together, it is
 possible to run a range of different decoding analyses.
 
-Below is a brief illustration of how to use the NDTr to do a simple
-decoding analysis. To learn how to use the NDTr please see the
-[documentation website](https://emeyers.github.io/NDTr/) and the package
+Below is a brief illustration of how to use the NDR to do a simple
+decoding analysis. To learn how to use the NDR please see the
+[documentation website](https://emeyers.github.io/NeuroDecodeR/) and the package
 vignettes.
 
 ``` r
-library(NDTr)
+library(NeuroDecodeR)
 
 # file to data in "binned format"
-basedir_file_name <- system.file(file.path("extdata", "ZD_150bins_50sampled.Rda"), package="NDTr")
+basedir_file_name <- system.file(file.path("extdata", "ZD_150bins_50sampled.Rda"),
+                                 package="NeuroDecodeR")
 
 # create the DS, FP, CL, RM, and CV objects
 ds <- ds_basic(basedir_file_name, 'stimulus_ID', 5, num_label_repeats_per_cv_split = 3)
@@ -89,7 +90,7 @@ plot(DECODING_RESULTS$rm_main_results)
 ## Documentation
 
 The documentation for this package is available at:
-<https://emeyers.github.io/NDTr/>
+<https://emeyers.github.io/NeuroDecodeR/>
 
 To get started we recommend you read the [introductory
-tutorial](https://emeyers.github.io/NDTr/articles/introduction_tutorial.html)
+tutorial](https://emeyers.github.io/NeuroDecodeR/articles/introduction_tutorial.html)