The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
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[@]dogado.de.
First stable release. Since 0.5.0 the package has grown from a curve-plotting tool into a full specification-curve workflow – estimate the curve, compare standard errors, test it, decompose it, and report it – and the public API is now considered stable. The deprecated camelCase function and argument names introduced this cycle continue to work (with a warning).
sca_test(): a permutation-based joint-inference
test for the whole specification curve, following Simonsohn, Simmons,
and Nelson (2020). It tests the sharp null that the focal variable has
no effect in any specification by repeatedly permuting that variable
(blocked within fixed effects when present) and re-estimating the entire
curve, then comparing the observed curve to the resulting null
distribution via three statistics: the median estimate, the share of
specifications significant in the predicted direction, and a Stouffer
combination of the per-specification p-values. Supports the same model
interface as sca() (formula or arguments, glm families,
fixed effects, weights), one- and two-sided tests, parallelisation, and
reproducible seeding. plot_sca_test() visualises each
statistic’s null distribution against the observed value, and
plot_sca_test_specs() (with
sca_test(keep_curves = TRUE)) draws the specification curve
against each specification’s own permutation null band.sca_test() gains a null_type argument with
two design-preserving nulls alongside the default
"shuffle_x". "freedman_lane" (Freedman &
Lane 1983) permutes the residuals of a reduced model
y ~ controls (the focal variable omitted) and refits the
curve, preserving the focal variable’s correlation with the controls –
so it stays calibrated under collinearity, where shuffling the focal
variable is anti-conservative. "residual_bootstrap" imposes
the null on the response (y - betahat * x) and resamples
rows, per Simonsohn, Simmons, and Nelson’s (2020) observational scheme.
Both are for linear models (including fixed effects) and fit every
specification on a common sample; a new common_sample
argument is also exposed.sca_test() is run with keep_curves = TRUE and
a confound-preserving null (null_type = "freedman_lane" or
"residual_bootstrap"), it now attaches a
multiple-comparison-corrected p-value for every specification,
answering which specifications are more extreme than chance
once you account for having searched all of them. It uses the min-P /
max-statistic permutation method of Westfall and Young (1993), reusing
the permutation null already computed (no extra permutations), and
compares each specification to its own null – which keeps it
calibrated under the confound-preserving nulls, where an
under-controlled specification’s null is not centred on zero. The new
exported sca_minp() recomputes the adjustment from an
existing result with a different method ("single_step", the
default, controls weak FWER; "step_down" is the more
powerful Westfall-Young free step-down) or significance threshold.
plot_sca_test_specs() highlights the specifications that
survive correction, and print(), sca_report(),
sca_table(), glance(), and
as.data.frame(what = "specs") surface the results.tidy() and
glance() methods (the generics that broom and
modelsummary dispatch on, so no broom
dependency is added) are provided for sca() curves and
sca_test() results, along with as.data.frame()
for sca_test. sca_table() renders a
publication results block – a dependency-free data frame by default, or
Markdown / LaTeX / gt / kableExtra /
flextable – and sca_report() writes a
one-paragraph, manuscript-ready summary. sca() output now
carries an "sca" class (it remains a data frame in every
other respect) so these methods can dispatch.sca_variance() and plot_variance():
decompose the variance of the focal coefficient across the specification
curve into the share attributable to each control choice (plus a
residual). Uses an LMG / Shapley decomposition of R-squared by default
(order-invariant shares that sum to the model R-squared), with a Type II
partial sums-of-squares option; adds no new dependency. Analogous to
specr::icc_specs() but suited to speccurvieR’s binary
control-inclusion choices.plot_influence() shows how
including vs excluding each control shifts the independent variable’s
coefficient; plot_coef_fit() plots the coefficient against
model fit (are the best-fitting specifications outliers?); and
plot_multi_se() draws the specification curve faceted by
standard error type, so you can see which specifications stay
significant under each choice of standard error.plot_se(): visualises the output of
se_compare(), plotting each coefficient’s estimate with a
confidence interval for every standard error type so the sensitivity of
inference to the choice of standard error is easy to see.theme_sca() are now applied
across all plots. Specification-curve and standard-error points are
filled by significance with a thin white outline so they read as
distinct markers against their own error bars; the zero reference line
is a softened red and gridlines are lighter. plot_curve()
and the model-fit plots combine their panels with patchwork
(so the combined plot is now customisable and panels align precisely);
plot_curve() gains median_line and
point_size arguments; and
plot_control_distributions() gains a zero_line
argument and a cohesive fill. theme_sca() gains a
base_family argument (defaulting to the
speccurvieR.base_family option, else the device font) for
setting the plot font without giving up portability.se_compare() now supports two-way and multiway
clustered standard errors. In addition to a character vector (each
element a separate one-way clustering, unchanged), cluster
accepts a list whose elements are each a character vector of dimensions
to cluster on jointly, so
cluster = list("a", "b", c("a", "b")) returns one-way SEs
by a, by b, and two-way clustered by both.
Multiway estimates use the native Cameron-Gelbach-Miller computation in
sandwich::vcovCL (non-fixed-effects, including glm) and
fixest (fixed effects); multiway columns are labelled with
the dimensions joined by _BY_
(e.g. "HC1_a_BY_b").se_compare() gains family and
link arguments mirroring sca(), so the full
range of glm() model families (e.g. logistic, Poisson) can
be compared across standard error types. The iid,
heteroskedasticity-consistent, clustered, and bootstrapped standard
error machinery all carry over to glm fits. Fixed effects remain
OLS-only and are dropped with a warning when a non-linear family is
supplied, matching sca()’s behaviour.sca() gains a formula interface:
sca(y ~ x + control1 + control2 | fixed_effect, data). The
first right-hand-side term is the focal independent variable, the
remaining terms are controls, and anything after | is
treated as fixed effects. The original y / x /
controls / fixed_effects argument interface is
unchanged.sca() now reports n_obs, the number of
observations each specification was fit on, and gains a
common_sample argument. With default listwise deletion,
specifications with different control sets can be fit on different
samples (which conflates control effects with sample changes);
n_obs makes this visible and
common_sample = TRUE fits every specification on the rows
that are complete across all model variables. New
plot_samplesizes() plots the per-specification sample
sizes.plotCurve() ->
plot_curve(), fixedEffects ->
fixed_effects). The previous camelCase names are kept as
deprecated aliases that warn and forward to the new names, so existing
code keeps working; they are scheduled for removal in version
2.0.0.sca() through
the joint-inference test.plot_sca_test_specs() no longer shows blank
legend keys for tiers with no specifications (e.g. when every
specification survives correction); unused tiers are dropped from the
legend.sca() no longer drops the focal variable (and
crash with “subscript out of bounds”) when a control variable’s name
contains the focal variable’s name as a substring (e.g. focal
"Temp" with a control "TempX") or the focal
name contains regular-expression metacharacters. The focal term is now
matched by exact equality rather than a substring/regex test.se_compare() accepts a formula object
(y ~ x | fe), not only a formula string
("y ~ x | fe"). Previously a formula object crashed with
“the condition has length > 1”, because as.character()
on a formula returns a length-3 vector that broke the internal
fixed-effects check. se_compare() also now skips an
individual clustered standard-error type that fails to compute (e.g. a
numerically singular clustered HC3) with a clear warning,
instead of letting the whole call error on a raw LAPACK message.sqrt(sample_size / nrow(data)) (an m-out-of-n bootstrap).
Previously resamples were drawn without replacement and were not
rescaled, which overstated the standard error by roughly
sqrt(nrow(data) / sample_size) and collapsed it to ~0 when
boot_sample_size equalled the number of rows."CL_FE" column of
se_compare() is now clustered by the first fixed effect, as
its name and documentation describe. Modern fixest (>=
0.10) defaults feols() to IID standard errors, so the
previous code (which read off the feols() default) was
returning IID rather than cluster-robust standard errors under that
label.sca() now raises an informative error when the
focal variable x does not correspond to a single model
coefficient (for example a factor or interaction term), instead of
failing with a cryptic “subscript out of bounds” error or silently
returning NA for every specification.sca()’s control-indicator columns (used by
plot_vars() and the plot_curve() control
panel) are now built by exact term membership instead of substring
matching on the model terms. Previously a control whose name was a
substring of another term (for example "O2" inside
"O2Sat") was marked present in specifications that did not
contain it.sca() and se_compare() now validate that
the supplied column names exist in data, failing with an
informative message instead of a cryptic deep-stack error.sca() accepts family = "gaussian" as an
alias for OLS and defaults to a glm family’s canonical link when
link is not supplied (e.g. family = "binomial"
no longer requires explicitly specifying the logit link). An
unrecognised family now raises a clear error.se_compare() no longer errors on a fixed-effects model
when clustering with specific (non-"all") standard error
types, and no longer warns that the fixed-effects-only
"CL_FE" type is invalid when the formula has fixed
effects.se_compare() standard-error type
validation and bootstrap sample-size expansion, a stray debug print, and
a crash when combining fixed effects with a non-linear family in
sca().testthat test suite and refactored the
internals of sca() and se_compare(); no change
to their output for valid input.Added support for weights in se_compare().
Added support for weights in sca().
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.
Health stats visible at Monitor.