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VStar 2.16.1 Release

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BSJ's picture
VStar 2.16.1 Release

VStar 2.16.1 has just been released.

This version contains security upgrades and will re-enable the Plug-in Manager and discrepant observation reporting that were disabled in the wake of the AAVSO Security breach. (Please note that the Members-only BSM and APASS Epoch Photometry plugins are still not available until further security measures can be implemented.)

In addition to the security upgrades, David Benn has also corrected several bugs and incorporated other changes based on valuable feedback from participants in his recent VStar CHOICE course. 

And speaking of the CHOICE course participants... several of them wrote useful documentation on how to use various plugins. These documents have now been posted (alongside the applicable plugin) on the VStar Plug-in Library. Kudos to Barbara Harris, Gordon Myers and Brad Walter for helping us all out with these documents.

Many thanks also to Paul York for his work in updating and re-writing the 5-Star Analysis Tutorial. This is a useful and fun way to learn a little more about data analysis in general and using VStar in particular.

To get this latest version of VStar if you don't already have the Java Web Start version on your computer, please visit this page: and click the "Download VStar Now" button. If you have already been using VStar, you will get the new version automatically next time you open the program with vstar.jnlp.

You can read more details about the changes included in this version in the ChangeLog.

Thank you David for all your hard work and patience during these trying times!


David Benn
David Benn's picture
Next steps

Thanks Sara and thanks everyone for your patience. smiley

The recent course had some very productive side-effects, as Sara mentioned.

I've started work on the next release (a couple of weeks away) to make use of Will's new authentication API and to fix some more bugs and add some more features.

I'm also refining a new period search plug-in (phase-based analysis of variance) based on a well-known algorithm.


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