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As of October 2021: * Minor updates to ensure compatibility with
v0.4.1 of hal9001 and v0.2.1 of haldensify,
both recently updated on CRAN. * Removal of the LazyData
field from the DESCRIPTION, since no data
directory is included with the package. * Minor tweaks to existing unit
tests to remove rlang from the Suggests field
of the DESCRIPTION. * Vignettes for the standard and
IPCW-augmented estimation procedures have been combined to reduce
redundancy and reduce build time per CRAN requests.
As of May 2021: * The use of hal9001::fit_hal()
internally for evaluation of a conditional mean of the full-data EIF has
been revised for compatibility with v0.4.0+ of the hal9001
package. * Defaults passed in through the argument
g_exp_fit_args, and to the function
est_g_exp(), have been updated for compatibility with
v0.1.5+ of the haldensify package.
As of April 2021: * The print() methods have been
updated to remove the use of cli functions,
which, for simplicity, has been replaced by the use of
message(). * Addition of a hidden slot
.eif_mat to the txshift_msm class, supporting
export of the matrix of EIF estimates for each shift in
delta_grid.
As of February 2021: * Remove cross-linking to sl3
functions as per request from CRAN. This can be reversed once
sl3 is available on CRAN.
As of January 2021: * Simulation experiments testing the performance of the procedures in the presence of loss to follow-up censoring indicate that the TML estimator outperforms the one-step for the EIF-based two-phase sampling correction. Generally, we recommend use of the TML estimator (the default) across all settings, though performance of the one-step estimator is much worse.
As of December 2020: * A delta slot has been added to
the txshift class to record the shift. * Hidden slots have
been similarly added to the txshift_msm class. * The
summary method has been removed, with the functionality now
supported by the print methods for the txshift
and txshift_msm classes. * The plot method has
been amended to support simultaneous confidence bands.
As of October 2020: * Changes all references to the argument
C to C_samp for the indicator of inclusion in
the second-stage sample. * Adds the new argument C_cens to
denote censoring due to loss to follow-up, i.e., prior to the occurrence
of the outcome. * Adds a nuisance regression for censoring
C_cens and adjusts the estimation procedure so as to use
inverse censoring weights in the full-data EIF procedure (NOTE: these
are not updated in the two-phase sampling correction). * Renaming of
arguments to internal functions and functions themselves: * From
est_g to est_g_exp for the exposure mechanism
density estimation * From est_ipcw to est_samp
for the two-phase sampling mechanism * Add est_g_cens for
the loss to follow-up censoring mechanism
As of September 2020: * Moved sl3 dependency to an
Enhances designation for CRAN submission. * As above,
removed sl3 from Remotes and added
installation safety checks.
As of June 2020: * Add single-knot spline to MSM summarization
(msm_vimshift). * Add class and plot method
for MSM summarization (msm_vimshift). * Fix bug in
msm_vimshift for computing CIs for binary outcomes by
switching from manually computing CIs to internally using custom
confint method. * Fix bug in msm_vimshift for
building lm model objects through weighted regression; move
models from plot method to msm_vimshift. *
Finish drafting brief paper for Journal of Open Source
Software.
As of April 2020: * Change export status of internal functions (e.g.,
no longer exporting onestep_txshift and
tmle_txshift). * Finish adding Roxygen “details” and
“return” slots throughout functions. * Add examples to main estimation
functions (txshift, vimshift_msm). * Update
argument names and add several assert_that checks. * Change
fit_spec terminology to fit_ext for external
fits. * Add unit tests for MSM functionality and nuisance parameter
estimation.
As of March 2020: * Extensive documentation, including fixing estimation terminology (e.g., one-step instead of AIPW) and adding Roxygen “details” and “return” slots. * Begin adding examples to exported functions.
As of March 2020: * Corrections to dependencies in preparation for
eventual CRAN release. * Change several previously exported functions to
internal, including eif, est_Hn,
est_Q, est_g, est_ipcw,
fit_fluctuation, ipcw_eif_update). *
Remove/reduce GitHub-only dependencies (now only sl3). *
Change title partially (from “Targeted Learning” to “Efficient
Estimation”). * Lock dependency versions (e.g., sl3 >=
v1.3.7) * Extensive documentation updates.
As of December 2019: * Changes arguments of
hal9001::fit_hal in pseudo-outcome regression for efficient
estimation by explicitly including max_degree = NULL. *
Change to TMLE convergence criterion: use a less strict criterion such
that | Pn D | sigma / (sqrt(n) max(10, log(n))) instead of /n. Empirical
studies suggest this curbs issues addressed by over-agressive updates
from the targeting step. * Remove pinning of sl3 dependency
to a specific tag (formerly v1.2.0). * Lock dependency version:
sl3 >= v1.3.6 and hal9001 >= v0.2.5.
As of October 2019: * Change use of as.data.table to
data.table in internal functions to catch up with changes
in dependencies.
As of September 2019: * Remove errant intercept term and lower iterations for fluctuation models. * Change weighting scheme in marginal structural model summarization to weight all estimates identically rather than by inverse variance as a default. * Updates to documentation.
As of September 2019: * Add safety checks for convergence of
fluctuation regressions based on those appearing in drtmle
and/or survtmle. * Change default confidence interval type
to use marginal CIs across multiple parameters instead of a simultaneous
confidence band. * Switch internal parametric regressions to use
sl3::Lrnr_glm instead of
sl3::Lrnr_glm_fast.
As of July 2019: * Improve argument names for clarity and update documentation. * Addition of tighter unit tests for both one-step and TML estimators.
As of June 2019: * Pin sl3 dependency to version 1.2.0
of that package for stability.
As of June 2019: * Changes to arguments of
hal9001::fit_hal for pseudo-outcome EIF regression. *
Addition of clarifying notes to core internal functions. * Removal of
outdated (and commented out) code in core internal functions. *
Clarifying alterations to internal function and argument names. *
Renaming internal function tx_shift to
shift_additive.
As of June 2019: * Remove inverse weights from estimated efficient influence function necessary for pseudo-outcome regression for efficient IPCW-augmented estimators. * Reduce use of redundant variables across core functions, reorganize functions across files, clarify documentation. * Tweak arguments for fitting pseudo-outcome regression with HAL in order to diagnose performance issues revealed by simulation. * Fix how inverse weights are passed to full-data estimators. * Pare down arguments for the one-step estimation routine.
As of June 2019: * Introduce bound_propensity function
to bound the propensity score away from zero by a factor 1/n, rather
than to numerical precision.
As of April 2019: * Minor improvements to documentation and
vignettes. * Fix a bug in the output of the IPCW one-step estimator. *
Pare down packages listed in imports, moving several to suggests. *
Introduce option to compute simultaneous confidence band for working
MSMs. * Fix a bug introduced by newly added imputation functionality in
sl3.
As of March 2019: * Introduce functionality for computing one-step estimators to complement the the available TMLEs. * Add initial functionality for summarizing estimated effects across a grid of shifts via working marginal structural models (MSMs).
As of February 2019: * Added helper functions and caught edge cases in auxiliary covariate for TMLE fluctuation models. * Fixed a bug in how the auxiliary covariate for TMLEs is computed by keeping track of an extra shift g(a+2delta|w). Revised inference machinery to create confidence intervals on the logit scale in the case of binary outcomes.
As of May 2018: * An initial public release of this package, version 0.2.0. * This version including complete functionality for both standard TML and IPCW-TML estimators.
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.
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