Changelog
Source:NEWS.md
fwildclusterboot 0.14.3
 Fix a bug with CI inversion when
r
was set to be close to the estimated parameter. (CI inversion failed). See #138. Thanks to Achim Zeileis & team for raising this issue!
fwildclusterboot 0.14.1
 brings back the
print()
method, it had a use case after all  some fixes to the documentation
fwildclusterboot 0.14
Breaking Changes
 the
print.boottest()
andprint.mboottest()
method have been deprecated, as both did not have a distinct use case.  Bugfix:
boottest()
should never have run withfixest::feols()
and varying slopes syntax viavar1[var2]
. Unfortunately it did for the heteroskedastic bootstrap  it’s a bug. I am very sorry if you are affected by this! This version adds an error message for this case.
Performance
Version 0.14 …
 sparsifies the “fast and reliable” bootstraps  bootstrap types 31, 33, 13 (which leads to good speed gains for problems with high dimensional fixed effects)
 allows to project out cluster fixed effects when running the “fast and reliable” algorithms “11” and “31”
 computes the generalized inverse
pinv
via rcpp eigen instead ofMASS::ginv()
wheneverMatrix::solve()
fails  unlocks parallelization (nthreads was internally set to 1 for some reason)
rOpenSci Review feedback
 update docs:
 add a vignette on wild bootstrap concepts (wild bootstrap 101)
 better explanation of plot method in docs and vignette
 some guidelines on how to turn messages and warnings off
 reorganization of ropensci ssr tags into code
 it is now possible to interrupt rcpp loops
fwildclusterboot 0.13
Potentially Breaking Changes:
boottest()
,mboottest()
andboot_aggregate()
no longer have a dedicatedseed
argument. From version 0.13, reproducibility of results can only be controlled by setting a global seed viadrqng::dqset.seed()
andset.seed()
. For more context, see the discussion below. As a consequence, results produced via old versions offwildlcusterboot
are no longer exactly reproducible.When the bootstrap is run via
engine = "WildBootTests.jl"
, the bootstrapped tstatistics and the original tstatistic are now returned as vectors (to align with the results from otherenginges
). Previously, they were returned as matrices.
Other Changes:

boottest()
receives a new argument,sampling
, which controls if random numbers are drawn via functions frombase
or thedqrng
package.  Some code refactoring. All bootstrap algorithms and their associated files have been renamed (e.g.
boot_algo2.R
is not calledboot_algo_fastnwild.R
, etc.).  Much nicer error and message formatting, via
rlang::abort()
,warn()
andinform()
.rlang
is added as a dependency.
Background on the Change to Seeding
Prior to the changes introduced in v0.13
, boottest()
will always call set.seed()
or dqrng::dqset.seed()
internally, regardless of whether the seed
argument is specified or not (in the ladder case, it will create an internal seed by randomly drawing from a large set of integers). I consider this harmless, as setting seeds inside boottest()
in this way does not affect the reproducibility of scripts run endtoend.
However, I have learned that is generally considered bad practice to overwrite global variables without notification  for example, the authors of numpy have deprecated their np.random.seed()
function for this reason.
Here is a quick example on what happens if a function “reseeds”: it affects the future chain of random draws.
fn_reseed < function(x){set.seed(x)}
set.seed(123)
rnorm(1)
# [1] 0.5604756
fn_reseed(1)
rnorm(1)
# [1] 0.6264538
set.seed(123)
rnorm(1); rnorm(1)
# [1] 0.5604756
# [1] 0.2301775
The two ‘second’ calls to rnorm(1)
are based on different global seed states.
As a result, I have decided to deprecate the seed' function argument. Random number generation must now **be** set outside of
boottest()using
set.seed()and
dqrng::dqset.seed()`.
This means that bootstrap results generated via versions < 0.13 will no longer be exactly replicable under the new version, but with a sufficiently large number of bootstrap iterations, this change should not affect any of your conclusions.
fwildclusterboot 0.12.1
CRAN release: 20230123
This is a hotfix release which turns of tests on CRAN that fail in nonstandard CRAN test environments.
fwildclusterboot 0.12
CRAN release: 20221015
This is the first CRAN release since version 0.9
. It comes with a set of new features, but also potentially breaking changes. This section summarizes all developments since version 0.9
.
Potentially breaking changes:

boottest()'s
function argumentboot_algo
has been renamed toengine
 the
setBoottest_boot_algo()
function was renamed tosetBoottest_engine()
Bug fixes and internal changes
 When a multiparameter hypothesis of the form R beta = r was tested, the heteroskedastic wild bootstrap would nevertheless always test “beta_k = 0” vs “beta_k != 0”, with “beta_k = param”. I am sorry for that bug!
 The
Matrix.utils
package is at danger of CRAN removal  it has been replaced by custom functions for internal use.
New features and Improvements
 A new function argument has been added 
bootstrap_type
. In combination with theimpose_null
function argument, it allows to choose between different cluster bootstrap types  WCx11, WCx13, WCx31, WCx33. For more details on these methods, see the working paper by MacKinnon, Nielsen & Webb (2022). Currently, these new bootstrap types only compute pvalues. Adding support for confidence intervals is work in progress.  A
boot_aggregate()
method now supports the aggregation of coefficients in staggered differenceindifferences following the methods by Sun & Abraham (2021, Journal of Econometrics) in combination with thesunab()
function fromfixest
has been added. Essentially,boot_aggregate()
is a copy ofaggregate.fixest
: the only difference is that inference is powered by a wild bootstrap.  The heteroskedastic bootstrap is now significantly faster, and WCR21 and WCR31 versions are now supported (i.e. HC2 and HC3 ‘imposed’ on the bootstrap dgp.)
fwildclusterboot 0.11.2
 significant speed improvements for the x1 bootstrap algorithms,
bootstrap_type %in% c("11", "31")
, both for WCR and WCU
fwildclusterboot 0.11.1
New bootstrap algorithms following MNW (2022)
 A new function argument has been added 
bootstrap_type
. In combination with theimpose_null
function argument, it allows to choose between different cluster bootstrap types  WCx11, WCx13, WCx31, WCx33. For more details on these methods, see the working paper by MacKinnon, Nielsen & Webb (2022).
boot_aggregate()
method for SunAbrahams Event Studies
A boot_aggregate()
method to supports the aggregation of coefficients in staggered differenceindifferences following the methods by Sun & Abraham (2021, Journal of Econometrics) in combination with the sunab()
function from fixest
has been added. Essentially, boot_aggregate()
is a copy of aggregate.fixest
: the only difference is that inference is powered by a wild bootstrap.
Other syntax changes, potentially breaking!
 The
boot_algo
function argument has been renamed toengine
.  The
setBoottest_boot_algo()
function has been renamed tosetBoottest_engine()
. In consequence, the syntax introduced in 0.11 changes to
boottest(
lm_fit,
param = ~treatment,
clustid = ~group_id1,
B = 9999,
impose_null = TRUE,
engine = "R",
bootstrap_type = "11"
)
To run everything through WildBootTests.jl
, you would have to specify
boottest(
lm_fit,
param = ~treatment,
clustid = ~group_id1,
B = 9999,
impose_null = TRUE,
engine = "WildBootTests.jl",
bootstrap_type = "11"
)
fwildclusterboot 0.11
 This release introduces new wild cluster bootstrap variants as described in MacKinnon, Nielsen & Webb (2022). The implementation is still quite barebone: it only allows to test hypotheses of the form β_{k} = 0 vs β_{k} ≠ 0, does not allow for regression weights or fixed effects, and further does not compute confidence intervals.
You can run one of the ‘new’ variants  e.g. the “WCR13”, by specifying the bootstrap_type
function argument accordingly:
boottest(
lm_fit,
param = ~treatment,
clustid = ~group_id1,
B = 9999,
impose_null = TRUE,
engine = "R",
bootstrap_type = "31"
)
fwildclusterboot 0.10
 introduces a range of new methods:
nobs()
,pval()
,teststat()
,confint()
andprint()
 multiple (internal) changes for ropensci standards alignment
 drop the
t_boot
(teststat_boot
) function arguments > they are now TRUE by default  fix a bug in the lean algorithms  it always tested hypotheses of the form beta = 0 instead of R’beta = r, even when R != 1 and r != 0
 enable full enumeration for Rlean tests
 enable deterministic ‘full enumeration tests’  these are exact
fwildclusterboot 0.9
CRAN release: 20220610
v0.9 moves data preprocessing from
model.frame
methods tomodel_matrix
methods. I had wanted to do so for a while, but issue #42, as raised by Michael Topper, has finally convinced me to start working on this project.Moving to
model_matrix
methods unlocks new functionality for howboottest()
plays withfixest
objects  it is now possible to runboottest()
afterfeols()
models that use syntactic sugar:
library(fwildclusterboot)
library(fixest)
data(voters)
feols_fit < feols(proposition_vote ~ i(treatment, ideology1) ,
data = voters
)
boot1 < boottest(feols_fit,
B = 9999,
param = "treatment::0:ideology1",
clustid = "group_id1"
)
feols_fits < fixest::feols(proposition_vote ~ treatment  sw(Q1_immigration, Q2_defense), data = voters)
res < lapply(feols_fits, \(x) boottest(x, B = 999, param = "treatment", clustid = "group_id1"))
voters$split < sample(1:2, nrow(voters), TRUE)
feols_fits < fixest::feols(proposition_vote ~ treatment, split = ~split, data = voters)
res < lapply(feols_fits, \(x) boottest(x, B = 999, param = "treatment", clustid = "group_id1"))
Some formula sugar still leads to errors, e.g.
feols_fit2 < feols(proposition_vote ~ treatment  Q1_immigration^Q2_defense,
data = voters
)
boot1 < boottest(feols_fit2,
B = 9999,
param = "treatment",
clustid = "group_id1"
)
The release further fixes a multicollinearity bug that occured when
lm()
orfixest()
silently deleted multicollinar variable(s). Thanks to Kurt Schmidheiny for reporting!The
na_omit
function argument has been dropped. If the cluster variable is not included in the regression model, it is now not allowed to contain NA values.Several function arguments can now be fed to
boottest()
as formulas (param
,clustid
,bootcluster
,fe
).
data(voters)
feols_fit < feols(proposition_vote ~ treatment ,
data = voters
)
boot < boottest(feols_fit,
B = 9999,
param = ~ treatment,
clustid = ~ group_id1
)
fwildclusterboot 0.8
CRAN release: 20220418
Two new bootstrap algorithms: ‘WildBootTests.jl’ and ‘Rlean’
boot_algo = ‘WildBootTests.jl’

fwildclusterboot
now supports calling WildBootTests.jl, which is a very fast Julia implementation of the wild cluster bootstrap algorithm. To do so, a new function argument is introduced,boot_algo
, through which it is possible to control the executed bootstrap algorithm.
# load data set voters included in fwildclusterboot
data(voters)
# estimate the regression model via lm
lm_fit < lm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration , data = voters)
boot_lm < boottest(
lm_fit,
clustid = "group_id1",
param = "treatment",
B = 9999,
boot_algo = "WildBootTests.jl"
)
WildBootTests.jl is (after compilation) orders of magnitudes faster than
fwildclusterboot's
native R implementation, and speed gains are particularly pronounced for large problems with a large number of clusters and many bootstrap iterations.Furthermore,
WildBootTests.jl
supports a range of models and tests that were previously not supported byfwildclusterboot
: most importantly a) wild cluster bootstrap tests of multiple joint hypotheses and b) the WRE bootstrap by Davidson & MacKinnon for instrumental variables estimation. On top of the cake … the WRE is really fast.
library(ivreg)
data("SchoolingReturns", package = "ivreg")
# drop all NA values from SchoolingReturns
SchoolingReturns < na.omit(SchoolingReturns)
ivreg_fit < ivreg(log(wage) ~ education + age + ethnicity + smsa + south + parents14 
nearcollege + age + ethnicity + smsa + south + parents14, data = SchoolingReturns)
boot_ivreg < boottest(
object = ivreg_fit,
B = 999,
param = "education",
clustid = "kww",
type = "mammen",
impose_null = TRUE
)
generics::tidy(boot_ivreg)
# term estimate statistic p.value conf.low conf.high
# 1 1*education = 0 0.0638822 1.043969 0.2482482 0.03152655 0.2128746
For guidance on how to install and run
WildBooTests.jl
, have a look at the associated article.Also, note that running the wild cluster bootstrap through
WildBootTests.jl
is often very memoryefficient.
boot_algo = ‘Rlean’
A key limitation of the vectorized ‘fast’ cluster bootstrap algorithm as implemented in fwildclusterboot
is that it is very memorydemanding. For ‘larger’ problems, running boottest()
might lead to outofmemory errors. To offer an alternative, boottest()
now ships a ‘new’ rcpp and loopbased implementation of the wild cluster bootstrap (the ‘wild2’ algorithm in Roodman et al).
boot_lm < boottest(
lm_fit,
clustid = "group_id1",
param = "treatment",
B = 9999,
boot_algo = "Rlean"
)
Heteroskeadstic Wild Bootstrap
It is now possible to run boottest()
without specifying a clustid
function argument. In this case, boottest()
runs a heteroskedasticityrobust wild bootstrap (HC1), which is implemented in c++.
boot_hc1 < boottest(lm_fit, param = "treatment", B = 9999)
summary(boot_hc1)
boottest()
function argument beta0
deprecated
For consistency with WildBootTests.jl
, the boottest()
function argument beta0
is now replaced by a new function argument, r
.
Frühjahrsputz
I have spent some time to clean up fwildclusterboot's
internals, which should now hopefully be more readable and easier to maintain.
Testing
fwildclusterboot
is now predominantly tested against WildBootTests.jl
. Tests that depend on Julia are by default not run on CRAN, but are regularly run on Mac, Windows and Linux via github actions.
fwildclusterboot 0.6
Bug fix: for onesided hypotheses for the WRU bootstrap (if impose_null = FALSE), the returned pvalues were incorrect  they were reported as ‘p’, but should have been ‘1p’. E.g. if the reported pvalues was reported as 0.4, it should have been reported as 0.6.
A new function argument
ssc
gives more control over the small sample adjustments made withinboottest()
. It closely mirrors thessc
argument infixest
. The only difference is thatfwildclusterboot::boot_ssc()'s
fixef.K
argument currently has only one option,'none'
, which means that the fixed effect parameters are discarded when calculating the number of estimated parameters k. The default argument ofboot_ssc()
areadj = TRUE, fixef.K = "none", cluster.adj = TRUE
andcluster.df = "conventional"
. In fixest, thecluster.df
argument is"min"
by default. Prior to v 0.6, by default, no small sample adjustments regarding the sample size N and the number of estimated parameters k were applied. The changes in v0.6 may slightly affect the output ofboottest()
. For exact reproducibility of previous results, setadj = FALSE
. Settingadj = TRUE
will not affect pvalues and confidence intervals for oneway clustering, but the internally calculated tstat, which is divided by sqrt(Nk)/(N1). For twoway clustering, it might affect the number and order of invalid bootstrapped tstatistics (due to nonpositive definite covariance matrices) and, through this channel, affect bootstrapped inferential parameters.Testing: unit tests are now run on github actions against wildboottestjlr, which is a JuliaConnectoR based wrapper around WildBootTests.jl, a Julia implementation of the fast wild cluster bootstrap algorithm.
Additionally, minor speed tweaks.
fwildclusterboot 0.5.1
CRAN release: 20211106
 Fixes a bug with Mammen weights introduced in version 0.5 > switch back to
sample()
function. To guarantee reproducibilty with Mammen weights, either a seed needs to be specified inboottest()
or a global seed needs to be set viaset.seed()
.  Deletes some unnecessary computations from boot_algo2() > speed improvements
 For B = 2^(#number of clusters), Rademacher weights should have been enumerated  instead, they were drawn randomly and enumeration only occured for B > 2^(#number of clusters). Now, enumeration occurs if B >= 2^(#number of clusters).
fwildclusterboot 0.5
CRAN release: 20211103
 Version 0.5 fixes an error for the bootstrap with weighted least squares introduced with version 0.4. All unit tests that compare fwildclusterboot with weighted least squares results from boottest.stata pass. In particular, enumerated cases pass with exact equality (in such cases, the bootstrap weights matrices are exactly identical in both R and Stata).

boottest()
now stops iffixest::feols()
deletes nonNA values (e.g. singleton fixed effects deletion) and asks the user to delete such rows prior to estimation viafeols()
&boottest()
. Currently,boottest()'s
preprocessing cannot handle such deletions  this remains future work.  To align
fwildclusterboot
with Stata’s boottest command (Roodman et al, 2019), Mammen weights are no longer enumerated infwildclusterboot::boottest()
. 
boottest()
no longer sets an internal seed (previously set.seed(1)) if no seed is provided as a function argument.  Sampling of the bootstrap weights is now powered by the
dqrng
package, which speeds up the creation of the bootstrap weights matrix. To set a “global” seed, one now has use thedqset.seed()
function from thedqrng package
, which is added as a dependency.
fwildclusterboot 0.3.7
CRAN release: 20210914
 Bug fix: the output of
boottest()
varied depending on the class of the input fixed effects for regressions both vialfe::felm()
andfixest::feols()
. This bug occurred becauseboottest()
does not work with a preprocessed model.frame object from eitherfelm()
orfeols()
but works with the original input data. While bothfelm()
andfeols()
change nonfactor fixed effects variables to factors internally,boottest()
did not check but implicitely assumed that all fixed effects used in the regression models are indeed factors in the original data set. As a consequence, if one or more fixed effects were e.g. numeric,boottest()
would produce incorrect results without throwing an error. With version 0.3.7,boottest()
checks internally if all variables in the original data set which are used as fixed effects are factor variables and if not, changes them to factors. Thanks for timotheedotc for raising the issue on github, which can be found here: https://github.com/s3alfisc/fwildclusterboot/issues/14.  Some tests have been added that compare output from
boottest()
with the wild cluster bootstrap implemented viaclusterSEs
.
fwildclusterboot 0.3.6
CRAN release: 20210801
 Bug fix regarding suggested packages and CRAN: see github issue #12. Added
if(requireNamespace("pkgname"))
statements for suggested packages in the vignettes, examples and tests. Note that unit tests will now only execute on CRAN if bothfixest
andlfe
can be installed on the OS.
fwildclusterboot 0.3.5
CRAN release: 20210620
Bug fix: For Rademacher and Mammen weights and cases where (2^ number of clusters) < # boostrap iterations, (deterministic ) full enumeration should have been employed for sampling the bootstrap weights. Full enumeration means the following: for e.g. 6 numbers of clusters, only 2^6 = 64 unique draws from either the Rademacher or Mammen distributions exists. Therefore,
boottest()
overwrites the userprovided number of bootstrap iterations to B = (2^ number of clusters) if a larger number is chosen. The bug now occured because the bootstrap weights were drawn randomly with replacement instead of using full enumeration. Note: full enumeration was introduced with version 0.3.3. Thanks to fschoner for finding the bug! see github issue #11Bug fix: A small bug has been fixed related to missing values in the cluster variables.
By default,
boottest()
now sets an internal seed if no seed is provided by the user via theseed
function argument.Several improvements to the documentation.
fwildclusterboot 0.3.4
CRAN release: 20210501
 Fix CRAN errors caused by a small bug in the vignette
fwildclusterboot 0.3.3
CRAN release: 20210412
 implements full enumeration for Rademacher and Mammen Weights if 2^{k} < B, where k is the number of clusters and B the number of bootstrap iterations
fwildclusterboot 0.3.1
CRAN release: 20210216
 A
glance.boottest()
method was added, which enables the use of themodelsummary
package withfwildclusterboot
.  The
tidy.boottest()
method is no longer exported. You can still access it viafwildclusterboot:::tidy.boottest()
or by loading thegenerics
package vialibrary(generics)
.
fwildclusterboot 0.3.0
 Additional performance improvements through parallelization. By default,
boottest()
uses half the available threads for parallel execution. The number of threads can be set via thenthreads
function argument.  Additional function arguments for
boottest()
 the user can now set the tolerance and maximum number of iterations for the calculation of confidence intervals. By default,tol = 1e6
andmaxiter = 10
.  The package no longer depends on
data.table
andfabricatr
 both are now only suggested. Further, the package now comes with an example data set ‘voters’.