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quantoptr (version 0.1.3)

Algorithms for Quantile- And Mean-Optimal Treatment Regimes

Description

Estimation methods for optimal treatment regimes under three different criteria, namely marginal quantile, marginal mean, and mean absolute difference. For the first two criteria, both one-stage and two-stage estimation method are implemented. A doubly robust estimator for estimating the quantile-optimal treatment regime is also included.

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Version

Install

install.packages('quantoptr')

Monthly Downloads

207

Version

0.1.3

License

GPL (>= 2)

Maintainer

Yu Zhou

Last Published

February 5th, 2018

Functions in quantoptr (0.1.3)

dr_quant_est

The Doubly Robust Quantile Estimator for a Given Treatment Regime
IPWE_Mopt

Estimate the Mean-optimal Treatment Regime
TwoStg_Mopt

Estimate the Two-stage Mean-Optimal Treatment Regime
get_os

Get the OS from R
IPWE_Qopt

Estimate the Quantile-optimal Treatment Regime
DR_Qopt

The Doubly Robust Estimator of the Quantile-Optimal Treatment Regime
qestimate

The Quantile-Optimal Treatment Regime Wrapper Function
TwoStg_Qopt

Estimate the Two-stage Quantile-optimal Treatment Regime
IPWE_MADopt

Estimation of the Optimal Treatment Regime defined as Minimizing Gini's Mean Differences
mean_est

The Inverse Probability Weighted Estimator of the Marginal Mean Given a Specific Treatment Regime
abso_diff_est

Estimate the Gini's mean difference/mean absolute difference(MAD) for a Given Treatment Regime
mestimate

The Mean-Optimal Treatment Regime Wrapper Function
augX

Generate Pseudo-Responses Based on Conditional Quantile Regression Models
quant_est

Estimate the Marginal Quantile Given a Specific Treatment Regime