In emeyers/NDTr: The Neural Decoding Toolbox in R

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NDTr: The Neural Decoding Toolbox in R

## Overview *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 decoding analyses in R. ## Installation You can install NDTr from github using: wzxhzdk:1 ## Usage The package is based on 5 abstract object types: 1. `Datasources (DS)`: generate training and test sets. 2. `Feature preprocessors (FP)`: apply preprocessing to the training and test sets. 3. `Classifiers (CL)`: learn relationships on the training set and make predictions on the test data. 4. `Result Metrics (RM)`: summarize the prediction accuracies. 5. `Cross-validators (CV)`: take the DS, FP and CL objects and run a cross-validation decoding procedure. 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 vignettes. wzxhzdk:2 wzxhzdk:3 wzxhzdk:4 ## Documentation The documentation for this package is available at: https://emeyers.github.io/NDTr/ To get started we recommend you read the [introductory tutorial](https://emeyers.github.io/NDTr/articles/introduction_tutorial.html)

emeyers/NDTr documentation built on Aug. 8, 2020, 3:41 p.m.