Overview | Introduction and Installation | i. Introduction | ii. Install R and RStudio | iii. Install and Load Package | Using GENEAcore | i. Folder preparation | ii. geneacore() wrapper function | iii. Expected outputs | iv. Bin file summary | v. MPI summary | vi. Other ways to use geneacore | Individual access to GENEAcore functions | i. Sampling your files | ii. Creating calibration values from your data | iii. Applying calibration values to your data | iv. Detecting transitions in your data for event aggregation | v. Applying calculations to raw data to obtain additional measures | vi. Get events indices from transitions | vii. Aggregating your raw data into epochs/events | GENEAbout | i. geneabout() wrapper function | ii. Expected outputs | Individual access to GENEAbout functions | i. Rest intervals | ii. Nonwear and rest coverage | iii. Decision tree | COEL Behavioural Atoms 2.0 | Appendix | i. GENEAcore functions flowchart | ii. Sample code to loop functions for a folder of files | iii. Sample code to aggregate events by day | Citation

Overview | Function Name Changes | Parameter Name Changes | MPI Name Changes

GENEAclassify | Overview | Summary | Contents | 1. Introduction and Installation. | i. Preface | ii. Installing R. | iii. Using GENEAclassifiyDemonstration.R | The script provided will run through these steps: | iv. Installing and loading required libraries | v. Installing GENEAclassify | vi. Development of GENEAclassify on GitHub. | 2. Segmentation | i. Introduction | ii. Loading Data | iii. Segmenting Data | iv. Segmentation Variables, Functions and Features | v. Varying Step Counting Algorithms | 3. Applying a Classification Model | ii. Creating a classification model from Training Data | iii. Classifying a file | iv. Classifying a directory | 4. Creating a Classification Model | ii. Manually Classifying files | iii. Creating a Training Data set