sim_slopes() and johnson_neyman()
from the interactions package. Note that this compatibility is pending an
update to that package.
(#57)tibble. (#55)skimr, dplyr, and purrr. Users should not notice any change in behavior from these.long_panel() to check whether values are varying,
it no longer performs a series of operations that are rendered unnecessary. This
will slightly speed up performance when check.varying = FALSE. (#44)long_panel() and
are_varying() functions.Compatibility update for upcoming changes to the clubSandwich. Thanks to
James Pustejovksy for submitting the necessary fixes.
Re-release: There are no changes, but panelr was removed from CRAN because
one of the packages it depended on had also been removed. That package is
now back on CRAN, so panelr will return as well.
Re-release: There are no changes, but panelr was removed from CRAN because
one of the packages it depended on had also been removed. That package is
now back on CRAN, so panelr will return as well.
Bugfixes:
dplyr updates. (#28, #29)wave variable.broom package. (#30)Bugfix:
long_panel() now handles numeric waves correctly when the input data are
unbalanced.brms package's interface for autocorrelated
errors.tidyr package.Bugfixes:
wbm() (#14; thanks
@strengejacke).pdata.frame to panel_data has been fixed.interaction.style argument to make_wb_data().predict.wbm() and predict.wbgee() has been improved. Notably,
the DV does not need to be included in newdata and the ID variable is only
required when necessary.Lots of new stuff! CRAN coming soon as well.
wbgee() works just like wbm(), except uses GEE (via the geepack package)
for estimation. This can give you more trustworthy results under some
circumstances and is much less likely to have convergence problems.fdm() estimates first differences models via GLS (from the nlme package).asym() estimates the linear asymmetric effects model described by
Allison (2019)
via first differences.asym_gee() estimates a similar asymmetric effects model to the one using
cumulative differences described in Allison (2019), but using GEE rather than
conditional logit.heise() produces stability and reliability estimates via the popular
method described in Heise (1969).nlsy and teen_poverty).New stuff:
are_varying() can now also assess individual-level variation, so using
the type = "individual" argument you can instead assess variables like age
that vary over time but change equally for every case.wbm() can now handle transformed dependent variables (e.g. log(y)).
Transformations on the right-hand side of the equation were always supported.panel_data objects are now quite a bit more difficult to break by
accidentally subsetting the ID and wave columns out of existence. Now,
subsetting via data[], select() and implicitly via transmute() will
never remove the ID and wave columns. You will also be warned if you arrange()
a panel_data object since it will generally break lag() functions.panel_data objects now store information about what the periods are for
the data, which you can access with the get_periods() function. For example,
if the waves in your data are the numbers 1 through 7, that's what you'll get.
This is more useful when the periods are irregular, such as if the waves are
the years of a biennial survey.Bugfixes:
wave variable to wbm() in the formula without
running into cryptic errors.lag(x)) could not be
included as a user-specified random effect. Pre-0.5.0, these could be included
if they were surrounded by backticks, but now that hack is unnecessary and
does not work.make_wb_data() is now updated to work with other internal updates
introduced in 0.5.0.long_panel() was never really working right when the source data's labels
were located at the beginning (i.e., label_location = "beginning"). It is
now much more robust.wbm()'s wave.factor argument had become non-functional for some time but
is now fixed.Starting to polish things up for CRAN.
Key changes:
panel_data frames now always place the id and wave columns first (in
that order).wbm() can now handle time-varying factors appropriately. Do note that it
only uses treatment contrasts, however. (#8)line_plot(), to help you explore trends in data.
It's a little rough around the edges for now.wbm objects are now a bit more streamlined and nice-looking.tidy() and glance() methods (from the broom package) for
wbm objects. (#4)as_panel_data() is an alias for panel_data() when supplying a data frame
and an S3 method otherwise. It can be used to convert pdata.frame objects
from the plm package to panel_data.wbm() are now converted to Formula objects to make
working with their multiple parts easier (see the Formula package for more
info).summary method for panel_data frames, which works best
if you have skimr installed. You can use dplyr::select() style syntax
to select which variables you want to describe and choose to get descriptives
by wave and/or entity.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 merMod implementation.
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
wbm.
A series of helper functions have been added to make wbm objects behave
more like regular model objects. Now update, formula, terms,
model.frame, coef, predict, and several more are defined for wbm.
The 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 long_panel and 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.
Additionally, are_varying was sped up by about 50%, though it slows
widen_panel down for data with many variables.
Tiny bugfixes:
long_panel would error when supplied a tibble rather than a base
data.frame.magrittr operators used
internally.New functions:
widen_panel converts your panel_data object to wide format, with one row
per entity. This can be useful for SEM analysis and some other things.long_panel does 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_varying is a function that can let you check whether variables in
long-formatted panel data vary over time or not.New feature:
detrend and balance_correction arguments were added to wbm to 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.md file to track changes to the package.