# FAST.R 0.2.1

* Adjusted tuning parameters used for ML classifier training

# FAST.R 0.2.0

* Added a new machine learning random forest classifier approach to distinguish between senescent and non-senescent cells based on the markers measured (SA-B-Gal, EdU, and Nuclear Area). This additional analysis can be easily performed by modifying the input metadata as described in the app.

* Added a new tab, 'Example Data', which allows users to download example data. Such data can be used to test the FAST.R app or can serve as a benchmark to ensure your own data is structured correctly and ready for analysis.

# FAST.R 0.1.3

* Fixed `FAST.R(Browser = FALSE)` to correctly open the app in a new RStudio window

* Added a 'Download all graphs' button in the Data Visualization tab. This allows to download all graphs generated at once in a zip file.

# FAST.R 0.1.2

# FAST.R 0.1.1

# FAST.R 0.1.0

* Initial CRAN submission.
