etwfe v0.6.0 returns to CRAN after addressing some failures due to upstream dependency changes. Apologies for any inconvenience caused.
etwfe(..., fe = <string>) argument now hinges on the
type of model family. For Gaussian models, we use the same
"vs" (varying slopes) option as before. But for non-linear
families like Poission, we now default to "none". This is
to preserve the correct VCOV behaviour of these models once they are
passed to emfx(), thus matching the new upstream logic of
marginaleffects. (Briefly: the standard errors of these
non-linear families cannot be computed correctly with derivative methods
on estimations where the fixed-effects have been abstracted away; see marginaleffects#1487
for more details.) Technically this is a breaking change, but it
requires no changes on the user side and the end result should be
identical in most cases. The only change you might see is that the
standard errors from estimations with non-linear families will be
slightly different/corrected. (#67)emfx(..., window = <numeric>) argument
allows users to restrict the temporal window around the treatment event,
thereby narrowing the consideration horizon. This may prove helpful for
cases with a large number of pre- and/or post-treatment periods. Thanks
to @fhollenbach
for the initial suggestion and implementation. (#46)emfx(..., compress = TRUE) to fail.
Thanks to @umor for
reporting in #66.emfx arguments:
emfx(..., predict = c("response", "link")), where the
latter allows for obtaining the linear prediction for non-linear models.
Internally passed as
marginaleffects::slopes(..., type = predict), thus avoiding
a clash with the topline
emfx(..., type = <aggregration_type>) argument.
(#49)emfx(..., lean = <logical>). Default value is
FALSE, but switching to TRUE will ensure a
light return object that strips away data-heavy attributes (e.g., copies
of the original model). These attributes are unlikely to be needed as
part of the emfx() workflow, so we may change the default
to lean = TRUE in a future version of
etwfe. (#51, #58)plot.emfx() method (via a
tinyplot backend) for visualizing emfx
objects. (#54)collapse argument in emfx() is
superseded by compress. The older argument is retained as
an alias for backwards compatibility, but will now trigger a message,
nudging users to switch to compress instead. The end result
will be identical, though. This cosmetic change was motivated by a
desire to be more consistent with the phrasing used in the literature
(i.e., on performance-boosting within group compression and weighting).
See Wong et al. (2021), for example.
(#57)cgroup = "never) due to incorrect subsetting prior to
recovering the marginal effects. Thanks to @paulofelipe for reporting (#37).Fixed internal centering procedure and handling of multiple
covariate levels (#30, #31). These fixes should have no impact on the
main ATT estimates (i.e., typical use of the package). But it may lead
to differences in the heterogeneous ATTs—i.e., via the xvar
arg—which were incorrectly estimated in some cases. Thanks to @PhilipCarthy for
flagging and to @frederickluser for helpful
discussions
Fixed internal and upstream bug which was causing model offsets to error (#28, thanks @mariofiorini for the initial report and several others for helpful discussion). Note that this fix requires insight >= 0.19.1.8, which is the development version at the time of writing. More information available here: https://github.com/easystats/insight/pull/759
emfx().\dontrun to avoid
triggering CRAN NOTEs on Windows for exceeding 5 seconds execution time.
Note that the package homepage still runs these Examples if users want
to inspect the output online.Support for estimating heterogeneous treatment effects via the
new etwfe(..., xvar = <xvar> argument (#16, thanks to
@frederickluser). Automatically
extends to emfx() via the latter’s by_xvar
argument (#21). More details are provided in the dedicated
“Heterogeneous treatment effects” section of the vignette and help
documentation
The new emfx(..., collapse = TRUE) argument can
substantially reduce estimation times for large datasets (#19, thanks
@frederickluser). This performance
boost does trade off against a loss in estimate accuracy. But testing
suggests that the difference is relatively minor for typical use cases
(i.e., results are equivalent up to the 1st or 2nd significant decimal
place, and sometimes even better). Please let us know if you find edge
cases where this is not true. More details are available in the
dedicated “Performance tips” section of the vignette and help
documentation, including advice for combining collapsing with
emfx(..., vcov = FALSE) (which yields an even more dramatic
speed boost but at a cost of not reporting any standard
errors).
Users can now use a 1 on the fml RHS to indicate that there are
no control variables as part of the etwfe call,
e.g. etwfe(y ~ 1, ...). This provides a second way of
indicating no controls, alongside the existing 0 option,
e.g. etwfe(y ~ 0, ...)
summary() on emfx objects for pretty printing,
and that the (former) “dydx” column of the resulting object is now named
“estimate”. These changes are reflected in the updated
documentation.Various documentation improvements. For example, the aforementioned sections on Heterogeneous TEs and Performance tips. I have also removed some warnings about the use of time-varying controls (#17). In truth, I can’t quite recall why I included these warnings in the first place and testing confirms that it does not appear to pose a problem for the ETWFE framework. Thanks to Felix Pretis for prompting me to revisit this implicit restriction, including forwarding some relevant correspondence with Prof. Wooldridge.
data.table is added to Imports and thus becomes a direct dependency. It was already an indirect dependency through marginaleffects.
It’s now possible to install the development version of the package from R-universe. Details are provided in the README.
The .Dtreat indicator variable created during the
etwfe call is now logical instead of integer (#14). This
fix yields slightly different effect sizes for emfx output
when applied to non-linear model families (e.g.,
etwfe(..., family = "poisson"). The reason is that we are
now implicitly calling marginaleffects::comparisons under
the hood rather than marginaleffects::marginaleffects. Note
that the main etwfe coefficients (for any family) are
unaffected, and the same is also true for emfx when applied
to a linear model (i.e., the default).
The (optional) ivar argument of etwfe()
has been moved down the argument order list from second position to
fifth (i.e., after the data argument). This means that the
four required arguments of function now occupy the top positions, which
could enable shorter, unnamed notation like
etwfe(y ~ x, year, cohort, dat).
emfx now allows (time-invariant) interacted control
variables on the fml RHS.
emfx now has a post_only logical
argument, which may be useful for plotting aesthetics (but not
inference). See the example in the introductory vignette.
Various improvements to the documentation (restructuring, fixed typos, etc.)