Package: drape 0.0.2

drape: Doubly Robust Average Partial Effects

Doubly robust average partial effect estimation. This implementation contains methods for adding additional smoothness to plug-in regression procedures and for estimating score functions using smoothing splines. Details of the method can be found in Harvey Klyne and Rajen D. Shah (2023) <doi:10.48550/arXiv.2308.09207>.

Authors:Harvey Klyne [aut, cre, cph]

drape_0.0.2.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
drape/json (API)

# Install 'drape' in R:

install.packages('drape', repos = c('https://harveyklyne.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/harveyklyne/drape/issues

On CRAN:

4.40 score 5 stars 5 scripts 297 downloads 8 exports 0 dependencies

Last updated from:659d33414b. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	132
source / vignettes	OK	168
linux-release	OK	124
macos-release	OK	169
macos-oldrel	OK	234
windows-devel	OK	99
windows-release	OK	99
windows-oldrel	OK	99
wasm-release	OK	93

Exports:basis_poly cv_resmooth cv_spline_score drape ng_pseudo_response resmooth simulate_data spline_score

Dependencies:

drape

Rendered fromdrape.Rmdusingknitr::rmarkdown

Last update: 2023-08-19
Started: 2023-08-19

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Estimate the score function of the d'th covariate using a polynomial basis.	basis_poly
Generate simulation data and evaluate estimators, with sample splitting.	compare
Evaluate estimators by training nuisance functions on training set and evaluating them on test set.	compare_evaluate
Generate simulation data and evaluate OLS estimator.	compare_lm
Generate simulation data and evaluate partially linear estimator.	compare_partially_linear
Generate simulation data and evaluate Rothenhausler estimator.	compare_rothenhausler
K-fold cross-validation for resmoothing bandwidth.	cv_resmooth
K-fold cross-validation for spline_score.	cv_spline_score
Estimate the doubly-robust average partial effect estimate of X on Y, in the presence of Z.	drape
Fit a lasso regression using quadratic polynomial basis, with interactions.	fit_lasso_poly
Fit pre-tuned XGBoost regression for use in simulations.	fit_xgboost
Compute sums of a Monte Carlo vector for use in resmoothing.	MC_sums
Population score function for the symmetric mixture two Gaussian random variables.	mixture_score
Generate a matrix of covariates for use in resmoothing, in which the d'th column of X is translated successively by the Kronecker product of bw and MC_variates.	new_X
Generate pseudo responses as in Ng 1994 to enable univariate score estimation by standard smoothing spline regression.	ng_pseudo_response
Fit a doubly-robust partially linear regression using the DoubleML package and pre-tuned XGBoost regressions, for use in simulations.	partially_linear
Resmooth the predictions of a fitted model	resmooth
Symmetric mixture two Gaussian random variables.	rmixture
Generate the modified quadratic basis of Rothenhausler and Yu.	rothenhausler_basis
Estimate the average partial effect of using the debiased lasso method of Rothenhausler and Yu, using pre-tuned lasso penalties, for use in simulations.	rothenhausler_yu
Generate simulation data.	simulate_data
Sort and bin x within a specified tolerance, using hist().	sort_bin
Univariate score estimation via the smoothing spline method of Cox 1985 and Ng 1994.	spline_score
Generate n copies of Z ~ N_{p}(0,Sigma), where Sigma_{jj} = 1, Sigma_{jk} = \text{corr} for all j not equal to k.	z_correlated_normal