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GBOP2 (version 0.1.4)

Generalized Bayesian Optimal Phase II Design (G-BOP2)

Description

Provides functions for implementing the Generalized Bayesian Optimal Phase II (G-BOP2) design using various Particle Swarm Optimization (PSO) algorithms, including: - PSO-Default, based on Kennedy and Eberhart (1995) , "Particle Swarm Optimization"; - PSO-Quantum, based on Sun, Xu, and Feng (2004) , "A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization"; - PSO-Dexp, based on Stehlík et al. (2024) , "A Double Exponential Particle Swarm Optimization with Non-Uniform Variates as Stochastic Tuning and Guaranteed Convergence to a Global Optimum with Sample Applications to Finding Optimal Exact Designs in Biostatistics"; - and PSO-GO.

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Version

Install

install.packages('GBOP2')

Monthly Downloads

173

Version

0.1.4

License

GPL-2

Maintainer

Wanni Lei

Last Published

December 22nd, 2025

Functions in GBOP2 (0.1.4)

stop_cluster

Stop and clean up the cluster
GBOP2_maxP_TE

PSOGO: Power maximizing design with efficacy and toxicity boundaries
init_cluster

Initialize parallel cluster
GBOP2_maxP_dualE

PSOGO: Power maximizing design with dual boundaries
GBOP2_minSS_dualE

PSOGO: Optimal/Minimax design with dual boundaries
get_cluster

Get current cluster
GBOP2_minSS_singleE

PSOGO: Optimal/Minimax design with single boundary for futility
GBOP2_minSS_TE

PSOGO: Optimal/Minimax design with efficacy and toxicity boundaries
summary.gbop2

Summary function Summary function for gbop2 objects
GBOP2_maxP_singleE

PSOGO: Power maximizing design with single boundary for futility