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license: LGPL-3.0-only
title: 'JQL: Jump Q-Learning for Individualized Interval-Valued Dose Rule'
version: 3.6.9
doi: 10.32614/CRAN.package.JQL
abstract: We provide tools to estimate the individualized interval-valued dose rule
  (I2DR) that maximizes the expected beneficial clinical outcome for each individual
  and returns an optimal interval-valued dose, by using the jump Q-learning (JQL)
  method. The jump Q-learning method directly models the conditional mean of the response
  given the dose level and the baseline covariates via jump penalized least squares
  regression under the framework of Q learning. We develop a searching algorithm by
  dynamic programming in order to find the optimal I2DR with the time complexity O(n2)
  and spatial complexity O(n). To alleviate the effects of misspecification of the
  Q-function, a residual jump Q-learning is further proposed to estimate the optimal
  I2DR. The outcome of interest includes the best partition of the entire dosage of
  interest, the regression coefficients of each partition, and the value function
  under the estimated I2DR as well as the Wald-type confidence interval of value function
  constructed through the Bootstrap.
authors:
- family-names: Cai
  given-names: Hengrui
  email: hcai5@ncsu.edu
- family-names: shi
  given-names: Chengchun
  email: cshi4@ncsu.edu
- family-names: Song
  given-names: Rui
  email: rsong@ncsu.edu
- family-names: Lu
  given-names: Wenbin
  email: wlu4@ncsu.edu
repository: https://cranhaven.r-universe.dev
commit: a6f648327667479128ed852c2336771c9b20cad0
date-released: '2026-05-14'
contact:
- family-names: Cai
  given-names: Hengrui
  email: hcai5@ncsu.edu