Implements the DAAREM method for accelerating the convergence of slow, monotone sequences
from smooth, fixed-point iterations such as the EM algorithm.
Faculty: Nicholas Henderson. Download:Github, CRAN.
IRLS
Implementation of iteratively re-weighted least squares algorithm (IRLS) algorithm
for generalized linear model in C++.
Reference: Tang, L., Zhou, L., and Song, P.X.K. (2016). Method of Divide-and-Combine in Regularised
Generalised Linear Models for Big Data. arXiv preprint arXiv:1611.06208.
HDDesign
Determine the sample size for high dimensional classification studies.
Reference: Sanchez, B.N., Wu, M., Song, P.X.K., and Wang W. (2016). Study design in high-dimensional
classification analysis. Biostatistics, doi: 10.1093/biostatistics/kxw018.
RCD
Scalable and efficient statistical inference with estimating functions in the MapReduce
paradigm for big data.
Misclassification of EHR (Electronic Health Record)-derived disease status and lack
of representativeness of the study sample can result in substantial bias in effect
estimates and can impact power and type I error for association tests. 'SAMBA' implements
several methods for obtaining bias-corrected point estimates along with valid standard
errors as proposed in Beesley and Mukherjee (2020) <doi:10.1101/2019.12.26.19015859>, currently under review.
References: Beesley, L.J. and Mukherjee, B., 2019. Statistical inference for association studies
using electronic health records: handling both selection bias and outcome misclassification.
medRxiv.
Reference: Oetting, A., Levy, J., Weiss, R. and Murphy, S. (2007), "Statistical methodology
for a SMART design in the development of adaptive treatment strategies ," in Causality
and Psychopathology: Finding the Determinants of Disorders and their Cures (American
Psychopathological Association), Arlington, VA: American Psychiatric Publishing, Inc.,
pp. 179-205.
