xts 0.11-1 on CRAN

xts version 0.11-1 was published to CRAN this morning. xts provides data structure and functions to work with time-indexed data.  This release contains some awesome features that will transparently make your xts code even faster!

• There’s a new window.xts() method, thanks to Corwin Joy (#100, #240). Corwin also refactored and improved the performance of the binary search algorithm used to subset xts objects. Tom Andrews reported and fixed a few related regressions (#251, #263, #264).
• The na.locf.xts() method loops over columns of multivariate objects in C code, for improved speed and memory performance. Thanks to Chris Katsulis and Tom Andrews for their reports and patches (#232, #233, #234, #235, #237).
• After many years, merge.xts() can finally handle multiple character or complex xts objects. Thanks to Ken Williams for the report (#44).
• You can use “quarters” to specify tick/grid mark locations on plots. Thanks to Marc Weibel for the report (#256).

There are also a few notable bug fixes:

• make.index.unique() always returns a unique and sorted index. Thanks to Chris Katsulis for the report and example (#241).
• Plots have better axis tick mark locations, thanks to Dirk Eddelbuettel (#246).
• periodicity() now warns instead of errors if the xts object contains less than 2 observations (#230).
• first() and last() now keep dims when they would otherwise be dropped by a regular row subset. This is consistent with head() and tail(). Thanks to Davis Vaughan for the report (#226).
• An invalid ISO8601 range subset now returns no data instead of all rows (#96).

As always, I’m looking forward to your questions and feedback! If you have a question, please ask on Stack Overflow and use the [r] and [xts] tags. Or you can send an email to the R-SIG-Finance mailing list (you must subscribe to post). Open an issue on GitHub if you find a bug or want to request a feature, but please read the contributing guide first!

If you love using my open-source work (e.g. quantmod, TTR, xts, IBrokers, microbenchmark, blotter, quantstrat, etc.), you can give back by sponsoring me on GitHub. I truly appreciate anything you’re willing and able to give!