- Extra step to check consistency of
`delta_n`

with exposure range. - Software paper examples were added.

- Plotting pseudo population includes object details. Set
`include_details = TRUE`

.

*`generate_pseudo_pop`

does not take`Y`

as an input.

- 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.

`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.

- A bug with swapping transformed covairates with original one.

- Some of unit tests have less accuracy to overcome the bug with
`stats::density`

function.

- Unit tests support new
`wCorr`

release (#193). - Only optimized compilation is supported. In the previous versions,
this approach is known as
`optimzied_compile == TRUE`

.

- The
`earth`

package is part of suggested packages.

- fixed a bug based on covariate balance threshold (#178, @naeemkh).
`estimate_npmetric_erf`

assigns user-defined log file.

- The process now prints the progress message based on the selected thresholds.
- In
`estimate_npmetric_erf`

:`matched_Y`

–>`m_Y`

`matched_w`

–>`m_w`

`matched_cw`

–>`counter_weight`

- In
`estimate_npmetric_erf`

function, the`matched_cw`

input is now mandatory. - Internal kernel smoothing now uses
`locpol::locpol`

function. - The entire data set is trimmed based on trimming quantiles.
`earth`

and`ranger`

are not installed automatically. They can be installed manually if needed.`sysdata.rda`

is modified to reflect transition from`counter`

and`ipw`

to`counter_weight`

`counter_weight`

is used as a counter or weight, in`matching`

or`weighting`

approaches.`counter`

and`ipw`

are dropped.`sl_lib`

becomes a required argument.- The package has been transferred into NSAPH-Software Github account.
- Summary function of
`gpsm_pspop`

S3 object returns details of the adjusting process.

- Now
`Kolmogorov-Smirnov(KS)`

statistics are provided for the computed pseudo population. `effect size`

for the generated pseudo population is computed and reported.- Binary search approach is used when scale = 1.
`pseodo_pop`

also includes covariate column names.`compute_closest_wgps_helper_no_sc`

is added to take care of the mostly used special case (scale = 1).

- Dropped importing
`KernSmooth`

and`tidyr`

packages. `pred_model`

argument dropped. The package only predicts using SuperLearner.

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

- 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.

`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.

- Naming covariate balance scores.

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

- 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.

- added the status of optimized compile to generate_pseudo_pop function output.
- compute_closest_wgps accepts the number of user-defined threads.

- Vignette file names.
- The trim condition from > and < into >= and <=.
- Removed seed input from generate_syn_data function. In R package, setting seed value inside function is not recommended. Users can set the seed before using the function.
- OpenMP uses user defined number of cores.

- Initial covariate balance for weighted approach. The counter column was not preallocated correctly.
- Counter value for compiling. The initial value was set to one, which, however, zero is the correct one.
- Private variable issue with OpenMP.
- Fixed OpenMP option on macOS checks.

- User needs to activate the logger

- CRAN package URLs are in canonical forms.

- OpenMP for Rcpp code
- optimized_compile
- log_system_info()
- Frequently asked questions
- logo

- estimate_gps.Rmd
- estimate_semi_erf -> estimate_semipmetric_erf
- estimate_erf -> estimate_npmetric_erf
- estimate_hr -> estimate_pmetric_erf
- gen_pseudo_pop -> generate_pseudo_pop
- gen_syn_data -> generate_syn_data
- estimate_erf accepts counter as an input
- estimate_erf can use multiple cores
- generating_pseudo_population.Rmd
- estimate_erf function description
- estimate_hr function description
- estimate_semi_erf function description
- compute_risk function description and return value
- outcome_models.Rmd
- generate_synthetic_data.Rmd

- Rcpp parLapply worker processors arguments

- running_appr

- Fixed documentations

- estimate_semi_erf
- estimate_hr

- Package name: GPSmatching –> CausalGPS

- User defined bin sequence in compiling pseudo population.
- Non-parametric option for estimating GPS.
- Adaptive approach to transform features in training sessions.
- Cpp code for computing pair of w and GPS.
`set_logger`

function.- Customized wrapper for ranger package.
- Extended plot function for gen_pseudo_pop object (plot.R).
- Extended plot function for estimate_erf object (plot.R).
- Extended print function for estimate_erf object (print.R).
- test-estimate_erf.R.
- create_weighting.R.
- Steps for adding test data into ‘sysdata.rda’.
`weighting`

option as causal inference approach.

- absolute_weighted_corr_fun.R
- Testing and running example guidelines for developers
- Customized wrapper for xgboost package.
`param`

as an argument to accept hyperparameters from users.

- R dependency 2.7 –> 3.5
- mclapply –> parLapply
- estimate_erf output returns S3 object.
- test-Covariate_balance.R –> test-absolute_corr_fun.R
- covariate_balance.R –> absolute_corr_fun.R
- User needs to pass
`m_xgboost`

instead of`SL.xgboost`

to use XGBoost package for prediction purposes.

- mclapply memory issue (compute_closest_wgps.R).

- Covariate balance check for categorical data.
- Contribution guidelines
- Parallel flag in training models (
`mcSuperLearner`

) - gen_syn_data function for generating synthetic data
- Unittest for gen_syn_data
- Function to compute residuals and unittest
- Function to impute NA values based on density and unittest
- Function to separate prediction model training (train_it)
- Function to separate min and max value estimation and unittest
- Function to find the closest data based on GPS and w
- Wrapper function to generate pseudo population and test it for covariate balance (gen_pseudo_pop)
- Function to estimate only GPS value (estimate_gps)
- Helper function to take the input data + GPS values and return pseudo population based on selected causal inference approach. The output of this function may or may not satisfy the covariate balance test. (compile_pseudo_pop)
- check_args function to check availability of the required parameters.
- check_covar_balance function to check if the generated pseudo population statistically acceptable.
- create_matching function to generate pseudo population based on matching approach.
- acknowledgments to index file

- create_matching only generates matched dataset.
- Covariate_balance.R –> covariate_balance.R
- matching_smooth –> estimate_erf.R
- risk_fun –> compute_risk
- smooth_fun –> smooth_erf
- hatvals –> estimate_hat_vals
- kernel_fun –> generate_kernel
- GPSmatching-package.R –> gpsmatching_package.R
- GPSmatching_smooth.R –> gpsmatching_smooth.R

- GPSmatching.R functions are separated into smaller functions, and the file is removed.