A tool to obtain activity counts, originally a translation of the 
  'python' package 'agcounts' <https://github.com/actigraph/agcounts>. This tool
  allows the processing of data from any accelerometer brand, with a more flexible 
  approach to handle different sampling frequencies.

Provides sleep duration estimates using a Pruned Dynamic
  Programming (PDP) algorithm that efficiently identifies
  change-points. PDP applied to physical activity data can identify
  transitions from wakefulness to sleep and vice versa. Baek, Jonggyu, Banker,
  Margaret, Jansen, Erica C., She, Xichen, Peterson, Karen E., Pitchford,
  E. Andrew, Song, Peter X. K. (2021) An Efficient Segmentation Algorithm to
  Estimate Sleep Duration from Actigraphy Data <doi:10.1007/s12561-021-09309-3>.

A tool to analyse 'ActiGraph' accelerometer data and to implement 
    the use of the PROactive Physical Activity in COPD (chronic obstructive pulmonary disease) instruments. Once analysis
    is completed, the app allows to export results to .csv files and to generate
    a report of the measurement. All the configured inputs relevant for interpreting
    the results are recorded in the report. In addition to the existing 'R' packages 
    that are fully integrated with the app, the app uses some functions from the 
    'actigraph.sleepr' package developed by Petkova (2021) <https://github.com/dipetkov/actigraph.sleepr/>.

This contains helpful functions for parsing, managing, plotting, and
    visualizing activities, most often from GPX (GPS Exchange Format) files
    recorded by GPS devices. It allows easy parsing of the source files into
    standard R data formats, along with functions to compute derived data for
    the activity, and to plot the activity in a variety of ways.

A mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalised linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers. Reference: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Juri Reimand and Gary D Bader. Molecular Systems Biology (2013) 9:637 <doi:10.1038/msb.2012.68>.