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As of October 2021: * Removed LazyData field in
DESCRIPTION since no data directory present. *
Removed reference to glmnet in documentation to avoid
adding the package to dependencies.
As of October 2021: * Reduced time-intensive nature of unit tests as
per CRAN policies. * Added smoothness_orders as a named
argument to haldensify, fit_haldensify, and
cv_haldensify, with a default of zero. This was previously
passed to hal9001::fit_hal via ... arguments.
* Submission accepted by and posted to CRAN.
As of September 2021: * Refinements of internal calls to
hal9001::fit_hal() in keeping with updates to that package,
for compatibility with its v0.4.0 CRAN release. * The
smoothness_orders argument of
hal9001::fit_hal() previously was set through the
... argument of haldensify; however, it has
now been made a named argument to both haldensify and the
internal cv_haldensify and fit_haldensify
functions. The default is set to zero, for indicator basis functions,
which differs from the default of hal9001::fit_hal() as of
its v0.4.0 release.
As of April 2021: * Changes to internal calls of
hal9001::fit_hal() in order to correctly use the pared-down
interface introduced in v0.4.0, contributed by @rachaelvp. * The default for the grid of
bins used for discretization of the variable A has been
altered to be multiples of sqrt(length(A)).
As of April 2021: * Updates to haldensify arguments
(removal of hal_max_degree as a named argument) to simplify
and better match use of fit_hal in hal9001
v0.3.0+. This overhaul also included the addition of ...
arguments, now passed through haldensify and
fit_haldensify to cv_haldensify, allowing all
internal calls to hal9001::fit_hal() to specify the same
arguments be passed for the fitting of HAL models. * Changes to the
default values of the argument n_bins, now setting this to
(much) larger values that are themselves based on the sample size. This
is in accordance with evidence from simulation experiments indicating
that higher values of n_bins lead to significantly improved
density estimates. * Addition of argument trim and
trim_dens to predict.haldensify to support the
use of truncation more transparently. While the default was to set
predictions for values of new_A outside the training
support to zero, this has been changed to avoid trimming and, when the
choice is made to trim the predictions, to set this value to
1/sqrt(length(new_A)). * Addition of a new method
print.haldensify for a more user-friendly display of the
prediction procedure’s output, including the selected number of bins,
the CV-selected choice of the regularization parameter, and the
summary of the fitted HAL model.
As of February 2021: * Addition of a method
plot.haldensify to simplify visualizing the empirical risks
of the sequence of HAL-based conditional density estimators across the
grid of the regularization parameter, and necessary changes to the
vignette. * Preparation to add an option to visualize the conditional
density estimates (of the estimator selected by cross-validation) via a
type argument in the plot.haldensify method.
Not yet implemented. * Simplification of unit tests to remove
unnecessary reliance on dplyr. * Limit re-fitting of HAL
model (after CV-selection of tuning parameters) in
haldensify() to full-data fit by explicitly passing
n_folds = 1. * Avoid cross-validation procedure
conditionally when the arguments n_bin and
grid_type are fixed; add related assertion check in
predict() when haldensify() skips
cross-validation (since lambda selection skipped). * Change how
long-format repeated measures data is passed around in both
haldensify() and predict() to clarify variable
passing. * Correct predict() method to truncate small
conditional density estimates to a minimum value of [1 / sqrt(n)], based
on the prediction set sample size.
As of January 2021: * Addition of argument
hal_basis_list to haldensify(), allowing for a
HAL basis produced by fit_hal() to be passed into the HAL
regression used for density estimation. This facilitates reduced
computational overhead when requiring external cross-validation of
nuisance functions (e.g., CV-TMLE) as well as working with bootstrap
samples. * Addition of argument hal_max_degree to
haldensify(), allowing for control of the highest degree of
interactions considered in the HAL model for density estimation. Like
the above, this can reduce computational overhead. * Fix a minor bug in
haldensify() by passing cv_folds to the
n_folds argument of fit_hal() when fitting HAL
regression for density estimation. Previously, cv_folds was
only used in constructing cross-validation (CV) folds for choosing
tuning parameters, but the subsequent HAL regression was fiex to use the
default number of folds specified in fit_hal() to choose
the regularization parameter of the HAL regression for density
estimation. Now, both CV to choose density estimation tuning parameters
and CV to choose the lasso tuning parameter use the same number of
folds. * Addition of argument ... to
haldensify() so that arbitrary arguments can be passed to
fit_hal() for density estimation, when not already
specified as other arguments of the haldensify()
constructor. * Remove the unnecessary argument use_future,
specifying parallel evaluation in a note instead. * Add an option
"all" to the lambda_select argument of the
predict() method, allowing for predictions on the full
(non-truncated) sequence of lambdas fitted on to be returned. * Change
truncation option in predict() method to 1/n instead of
zero.
As of January 2021: * Adds support to facilitate convenient marginal
density estimation by creating automatically a constant vector when
W = NULL is set in haldensify(). * The
hal9001 dependency has been upgraded to v0.2.8 of that
package, which introduced breaking changes in the names of slots in
fitted model objects. * The sequence of HAL models re-fit after
identification of the regularization parameter selected by
cross-validation has been padded with more aggressive choices of the
parameter to ameliorate convergence issues in model fitting. *
Re-fitting of the HAL model with cross-validated choices of the number
of bins, binning procedure, and regularization sequence has been altered
to reuse the regularization sequence provided as input rather than
subsetting the sequence to start with the cross-validation selector’s
choice of the parameter. Though convenient for undersmoothing
haldensify estimates, this subsetting proved problematic
for convergence of glmnet(). * The predict()
method’s cv_select argument has been replaced in order to
better facilitate undersmoothing. The new argument
lambda_select defaults to the cross-validation selector but
now easily allows access to the sequence of undersmoothed density
estimates (less restrictive regularization values). * The names of three
slots in the haldensify S3 output class have been changed *
grid_type_tune_opt is now grid_type_cvselect,
* n_bins_tune_opt is now n_bins_cvselect, and
* cv_hal_fits_tune_opt is now
cv_tuning_results.
As of December 2020: * Use of plan(transparent) has been
changed to plan(sequential) based on ongoing development in
the future package ecosystem.
As of June 2020: * A short software paper for inclusion in JOSS has been added.
As of May 2020: * The core cross-validation routine in
haldensify for fitting HAL models has been slightly
abstracted and moved to the new function fit_haldensify. *
The haldensify() wrapper function serves to cross-validate
over choices of the histogram binning strategy and the number of bins. *
The defaults of haldensify() have been changed based on
results of simulation experiments. * The unnecessary argument
seed_int in haldensify() has been removed. *
Fixes a bug introduced by returning predicted hazards as a vector
instead of a matrix. * An argument cv_select, defaulting to
TRUE, has been added to the predict method, to
make undersmoothing more accessible. * A simple vignette has been
added.
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.