v0.5.1

• Relaxes strict tests.

v0.5.0

• Modify the package structure
• Collect features from formula

v0.4.2

• Adding warning message for upcoming changes.

v0.4.1

Added

• Extra step to check consistency of delta_n with exposure range.
• Software paper examples were added.

Changed

• Plotting pseudo population includes object details. Set include_details = TRUE.
  *generate_pseudo_pop does not take Y as an input.

v0.4.0

Changed

• Docker image supports R 4.2.3
• generate_syn_data supports vectorized_y to accelerate data generation.
• matching_fun --> dist_measure
• matching_l1 --> matching_fn
• estimate_semipmetric_erf now takes the gam models optional arguments.
• estimate_pmetric_erf now takes the gnm models optional arguments.
• trim_quantiles --> exposure_trim_qtls
• generate_pseudo_pop function accepts gps_obj as an optional input.
• internal_use is not part of parameters for estimate_gps function.
• estimate_gps function only returns id, w, and computed gps as part of dataset.
• Now the design and analysis phases are explicitly separated.
• gps_model --> gps_density. Now it takes, normal and kernel options instead of parametric and non-parametric options.

Added

• estimate_npmetric_erf supports both locpol and KernSmooth approaches.
• There is gps_trim_qtls input parameter to trim data samples based on gps values.
• Now users can also collect the original data in the pseudo population object.

Fixed

• A bug with swapping transformed covairates with original one.

v0.3.1

Fixed the failing unit tests due to the following bug report:
https://bugs.r-project.org/show_bug.cgi?id=18337

v0.3.0

• Fixed regression tests that happened due to changes in wCorr.
• Dropped optimized_compile flag.

v0.2.9

In this version upgrade we:

• Dropped importing KernSmooth and tidyr packages.
• Droppedpred_model argument. The package only uses SuperLearner for prediction models.
• Added features to use a more optimized algorithm for a commonly used simplified case (scale = 1).
• Added effective sample size.
• Added Kolmogorov-Smirnov (KS) statistics for the generated pseudo-population (uses Ecume package).
• Made sl_lib a required argument.
• Removed earth and ranger packages from mandatory imports.
• Standardized the trimming approach to be less confusing for the users.
• Modified internal kernel smoothing approach.
• Renamed a couple of internal parameters for clarity and uniformity in the package.
• Fixed a bug on the covariate balance threshold.

v0.2.8

Fixed

• Message for not implemented methods changed to reduce misunderstanding.
• Empty counter will raise error in estimating non-parameteric response function.

Changed

• matching_l1 returns frequency table instead of entire vector.
• Vectorized population compilation and used data.table for multi-thread assignment.
• Removed nested parallelism in compiling pseudo population, which results in close control on memory.
• estimate_npmetric_erf also returns optimal h and risk values.

Added

• estimate_gps returns the optimal hyperparameters.
• estimate_gps returns S3 object.
• Internal xgboost approach support verbose parameter.
• Pseudo-population object now report the parameters that are used for the best covariate balance.

v0.2.7

Fixed

• Naming covariate balance scores.

Changed

• Restarting adaptive approach to keep trying up to maximum attempt.

Added

• Synthetic data (synthetic_us_2010)
• Check on not defined covariate balance (absolute_corr_fun, absolute_weighted_corr_fun)
• Covariate balance threshold type: mean, median, maximal.
• Improved test coverage.
• Singularity definition file.