The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
inv_sympd() by Armadillo
inv() in C++ Kalman Filter to improve numerical robustness
at a minor performance cost.summary.dfm: print method showed
that model had AR(1) errors even though idio.ar1 = FALSE by
default.Added argument idio.ar1 = TRUE allowing estimation
of approximate DFM’s with AR(1) observation errors.
Added a small theoretical vignette entitled ‘Dynamic Factor Models: A Very Short Introduction’. This vignette lays a foundation for the present and future functionality of dfms. I plan to implement all features described in this vignette until summer 2023.
na.keep = TRUE to
fitted.dfm. Setting na.keep = FALSE allows
interpolation of data based on the DFM. Thanks @apoorvalal (#45).summary.dfm occurring if only one
factor was estimated (basically an issue with dropping matrix dimensions
which lead the factor summary statistics to be displayed without
names).New default em.method = "auto", which uses
"BM" if the data has any missing values and
"DGR" otherwise.
Added vignette providing a walkthrough of the main features.
DFM(). The new name was inspired by the vars
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.