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NEWS.md
In haldensify: Highly Adaptive Lasso Conditional Density Estimation
haldensify 0.2.2
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.
haldensify 0.2.1
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.
haldensify 0.2.0
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.
haldensify 0.1.5
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)).
haldensify 0.1.0
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.
haldensify 0.0.9
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.
haldensify 0.0.8
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.
haldensify 0.0.7
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.
haldensify 0.0.6
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.
haldensify 0.0.5
- First CRAN release.
- Documentation updates.
haldensify 0.0.3
- First stable version of the package.
Try the haldensify package in your browser
Any scripts or data that you put into this service are public.
haldensify documentation built on Feb. 10, 2022, 1:07 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
haldensify 0.2.2
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.
haldensify 0.2.1
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.
haldensify 0.2.0
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.
haldensify 0.1.5
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)).
haldensify 0.1.0
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.
haldensify 0.0.9
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.
haldensify 0.0.8
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.
haldensify 0.0.7
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.
haldensify 0.0.6
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.
haldensify 0.0.5
- First CRAN release.
- Documentation updates.
haldensify 0.0.3
- First stable version of the package.
Try the haldensify package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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