Packaged: 2022-11-08 11:14:13 UTC; sukruacitas

Title: Robust Solution to the Behrens-Fisher Problem
person("Hatice", "Samkar", role = c("aut"),
Description: Robust tests (RW and RF) are provided for testing the equality of two long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW test is a robust version of Welch's two sample t test and the RF is a robust fiducial based test. The RW and RF tests are proposed using the adaptive modified maximum likelihood (AMML) estimators derived by Tiku and Surucu (2009) <doi:10.1016/j.spl.2008.12.001> and Donmez (2010) <https://open.metu.edu.tr/bitstream/handle/11511/19440/index.pdf>.
Packaged: 2021-06-30 09:55:40 UTC; sukruacitas

Packaged: 2021-11-15 13:38:56 UTC; sukruacitas

Title: Robust Bayesian Copas Selection Model
Description: Implementation of the robust Bayesian Copas (RBC) selection model for correcting and quantifying publication bias, as introduced in Bai et al. (2020) <arXiv:2005.02930>. This package also implements standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.

Description: Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) <arXiv:2005.02930> for correcting and quantifying publication bias in univariate meta-analysis. Also fits standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.

Title: Robust Calibration of Imperfect Mathematical Models
Description: Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang (2017) <arXiv:1707.08215>.
Packaged: 2018-02-27 17:41:05 UTC; gumengyang

Packaged: 2018-05-14 04:08:10 UTC; gumengyang

Description: Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang (2018) <arXiv:1707.08215>, Mengyang Gu, Fangzheng Xie and Long Wang (2021) <arXiv:1807.03829>, Mengyang Gu, Kyle Anderson and Erika McPhillips (2021) <arXiv:1810.11664>.
Packaged: 2022-01-11 06:33:46 UTC; mengyanggu