This version has switched the default degrees of freedom calculation for linear
wbm models to Satterthwaite, which are more computationally efficient and less prone to breaking R. They are also calculated on a per-variable basis. Kenward-Roger standard errors and degrees of freedom can be requested with the
t.df = "Kenward-Roger" argument.
This version includes some major under-the-hood changes, converting from an S3 object representation to S4. This allows the
wbm objects to formally be extensions of
merMod objects, meaning any method that could apply to
wbm but isn’t formally implemented will fall back to the
panel_data class no longer hardcodes the id and wave variables as “id” and “wave”. Instead, they remain whatever they are named and the
panelr functions will simply know which variables are these special ones.
A new function,
make_wb_data, allows users to do the data prepping that
wbm does internally without having to use all the modeling choices made by
A series of helper functions have been added to make
wbm objects behave more like regular model objects. Now
predict, and several more are defined for
summary function for
wbm has been refined and had some minor bugs squished.
More tweaks to
widen_panel, giving users the option to opt out of the feature introduced in
0.3.2 that stores data about varying and constant variables from
long_panel. Since poor data labeling in the original wide data can cause those stored attributes to be wrong, users can use
ignore.attributes = TRUE with
widen_panel to force checking for varying variables with
are_varying. Users can now also supply a vector of varying variables, similar to
reshape in base R.
This small update adds an enhancement to
widen_panel. If you start with wide data, convert it to long format, and then want to convert back to wide, the
panel_data object in long format will cache information about the variables to drastically speed up
widen_panel when you run it again.
are_varying was sped up by about 50%, though it slows
widen_panel down for data with many variables.
long_panelwould error when supplied a
tibblerather than a base
magrittroperators used internally.
panel_dataobject to wide format, with one row per entity. This can be useful for SEM analysis and some other things.
long_paneldoes a much more difficult thing, which is convert wide-formatted data to the more conventional long panel data format. It contains several means for parsing the variable names of the wide formatted data to produce a sensible long data frame with all the time-variant variables accounted for properly. Unlike
reshape, it can deal with unbalanced data.
are_varyingis a function that can let you check whether variables in long-formatted panel data vary over time or not.
balance_correctionarguments were added to
wbmto implement the procedures described in Curran and Bauer (2011). These, respectively, account for over-time trends in the predictors and correcting between-subject effects when panels are unbalanced.
NEWS.mdfile to track changes to the package.