Package: PLUCR 0.1.0

Laura Fuentes

PLUCR: Policy Learning Under Constraint

Estimating treatment probabilities that maximize a primary clinical outcome while controlling for the occurrence of adverse events. The 'PLUCR' package implements methods for constrained policy learning.

Authors:Laura Fuentes [aut, cre]

PLUCR_0.1.0.tar.gz
PLUCR_0.1.0.tar.gz(r-4.7-any)PLUCR_0.1.0.tar.gz(r-4.6-any)
PLUCR_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PLUCR/json (API)

# Install 'PLUCR' in R:

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

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 5 scripts 437 downloads 63 exports 56 dependencies

Last updated from:f9b6c3fd64. Checks:4 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	123
source / vignettes	OK	163
linux-release-x86_64	OK	124
wasm-release	OK	122

Exports:binary_S_p check_data delta_mu_constant delta_mu_linear delta_mu_mix delta_mu_null delta_mu_realistic delta_mu_threshold delta_nu_linear delta_nu_mix delta_nu_realistic delta_nu_satisfied delta_nu_threshold estimate_mu estimate_nu estimate_ps estimate_real_valued_mu FW generate_data generate_realistic_data get_opt_beta_lambda grad_Lagrangian_p grad_Lagrangian_p_X HX Lagrangian_p learn_threshold lwr_upper_bound_estimators main_algorithm make_psi model_Xi_linear model_Xi_mix model_Xi_realistic model_Xi_satisfied model_Xi_threshold model_Y_constant model_Y_linear model_Y_mix model_Y_null model_Y_realistic model_Y_threshold naive_approach_algorithm Optimization_Estimation oracular_approach_algorithm oracular_process_results phi phi_inv plot_metric_comparison plot_realistic process_results R_p S_p SGD sigma_beta sigma_beta_prime SL.grf synthetic_data_plot update_mu update_mu_XA update_nu update_nu_XA V_p V_Pn visual_treatment_plot

Dependencies:bitops caTools cli codetools cpp11 cvAUC data.table DiceKriging dplyr farver foreach gam generics ggplot2 glue gplots grf gridExtra gtable gtools isoband iterators KernSmooth labeling lattice lifecycle lmtest magrittr Matrix nnls pillar pkgconfig purrr R.methodsS3 R.oo R.utils R6 RColorBrewer Rcpp RcppEigen rlang ROCR S7 sandwich scales stringi stringr SuperLearner tibble tidyr tidyselect utf8 vctrs viridisLite withr zoo

Introduction to Policy Learning Under Constraint R-package (PLUCR)

Rendered fromPLUCR.Rmdusingknitr::rmarkdown

Last update: 2026-03-30
Started: 2026-03-30

Citation

Readme and manuals

Help Manual

Help page	Topics
Constraint function for binary policy	binary_S_p
Check input data for validity	check_data
CVFolds (from SuperLearner package)	CVFolds
Constant Conditional Average Treatment Effect estimator for Y	delta_mu_constant
Linear-shaped Conditional Average Treatment Effect estimator for Y	delta_mu_linear
Mixed-shape Conditional Average Treatment Effect estimator for Y	delta_mu_mix
Null Conditional Average Treatment Effect estimator for Y	delta_mu_null
Realistic Conditional Average Treatment Effect estimator for Y	delta_mu_realistic
Thresholded-shaped Conditional Average Treatment Effect estimator for Y	delta_mu_threshold
Linear-shaped Conditional Average Treatment Effect estimator for Xi	delta_nu_linear
Mixed-shaped Conditional Average Treatment Effect estimator for Xi	delta_nu_mix
Realistic Conditional Average Treatment Effect estimator for Xi	delta_nu_realistic
Computes the difference in expected outcomes under treatment and control.	delta_nu_satisfied
Thresholded Conditional Average Treatment Effect estimator for Xi	delta_nu_threshold
Estimate mu	estimate_mu
Estimate nu	estimate_nu
Estimate propensity score	estimate_ps
Estimate real-valued mu	estimate_real_valued_mu
Frank-Wolfe algorithm	FW
Synthetic data generator and functions generator	generate_data
Realistic synthetic data generator and functions generator	generate_realistic_data
Select Optimal Beta and Lambda Combination	get_opt_beta_lambda
Gradient of the objective function	grad_Lagrangian_p grad_Lagrangian_p_X
Compute the Inverse Propensity Score Weight (IPW)	HX
Objective function taking the form of a Lagrangian	Lagrangian_p
Learn Optimal Decision Threshold	learn_threshold
Lower and upper bound estimators for policy value and constraint	lwr_upper_bound_estimators
Main algorithm	main_algorithm
Generate psi function	make_psi
Linear treatment effect on Xi Component Function	model_Xi_linear
Mixed treatment effect on Xi component function	model_Xi_mix
Realistic treatment effect on Xi Component Function	model_Xi_realistic
Low treatment effect on Xi	model_Xi_satisfied
Thresholded treatment effect on Xi component function	model_Xi_threshold
Constant treatment effect on Y component function	model_Y_constant
Linear treatment effect on Y component function	model_Y_linear
Mixed treatment effect on Y component function	model_Y_mix
No treatment effect on Y component function	model_Y_null
Realistic treatment effect on Y component function	model_Y_realistic
Thresholded treatment effect on Y component function	model_Y_threshold
Naive approach main algorithm	naive_approach_algorithm
Iterative optimization procedure	Optimization_Estimation
Oracular approach main algorithm	oracular_approach_algorithm
Oracular evaluation of a policy	oracular_process_results
Normalize a Matrix by Column Min-Max Scaling	phi
Inverse Min-Max Normalization	phi_inv
Plot metric values for comparison	plot_metric_comparison
Plot realistic data setting	plot_realistic
predict.SL.grf	predict.SL.grf
Evaluate a policy	process_results
Risk function for Conditional Average Treatment Effect (CATE)	R_p
Constraint function	S_p
Stochastic Gradient Descent (SGD) algorithm	SGD
Link function	sigma_beta
Derivative of link function	sigma_beta_prime
SL.grf	SL.grf
SuperLearner.CV.control (from SuperLearner package)	SuperLearner.CV.control
Plot synthetic data setting	synthetic_data_plot
Update mu via augmented covariate adjustment	update_mu
Update mu via augmented covariate adjustment for fixed X	update_mu_XA
Update nu via augmented covariate adjustment	update_nu
Update nu via augmented covariate adjustment for fixed X	update_nu_XA
Oracular approximation of value function	V_p
Estimation of policy value	V_Pn
Visualize treatment assignment probability	visual_treatment_plot

Package: PLUCR 0.1.0

PLUCR: Policy Learning Under Constraint

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)