In emeyers/NeuroDecodeR: Decode Information from Neural Activity

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-"
)

NeuroDecodeR: Neural Decoding in R

## Overview *Neural decoding* is a data analysis method that uses pattern classifiers to predict experimental conditions based on neural activity. The NeuroDecodeR package makes it easy to do neural decoding analyses in R. ## Installation You can install NeuroDecodeR package from CRAN of the development version on GitHub. wzxhzdk:1 ## Documentation The documentation for this package is available at: https://emeyers.github.io/NeuroDecodeR/ To get started we recommend you read the [introductory tutorial](https://emeyers.github.io/NeuroDecodeR/articles/introduction_tutorial.html) ## 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 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. wzxhzdk:2 wzxhzdk:3 wzxhzdk:4 ## Running an analysis using pipes (|>) One can also run a decoding analysis using the pipe (|>) operator to string together the different NDR objects as shown below. wzxhzdk:5

emeyers/NeuroDecodeR documentation built on March 17, 2024, 6:05 p.m.