R Shinny for COVID-19 Prediction

Role: Developer (2020)

Predicting COVID-19 cases and deaths for your own region at https://minlu.shinyapps.io/killCOVID19/

Min Lu (Maintainer)

Lu M. and Ishwaran H. (2021). Cure and death play a role in understanding dynamics for COVID-19: data-driven competing risk compartmental models, with and without vaccination. PloS one 16(7): e0254397. [pdf] [supplemental pdf] [url] [.bib cite /exp]

Lu M. (2020). Dynamic Modeling COVID-19 for Comparing Containment Strategies in a Pandemic Scenario. Annals of Biostatistics & Biometric Application 4(1):1–4. [pdf] [url] [.bib cite /exp]

Vignettes for randomForestSRC

Role: Website Developer; Vignette Coauthor (2021–2022)

Fast Unified Random Forests with randomForestSRC

Link for website and vignettes: http://randomforestsrc.org/

Hemant Ishwaran, Min Lu (Website Maintainer), Udaya B. Kogalur (Package Maintainer, ubk@kogalur.com)

Complete List of References

R Package metavcov

Role: Developer (2017–2022)

Computing Within-Study Variances and Covariances, Visualization and Missing Data Solution for Multivariate Meta-Analysis

Download the package at https://cran.r-project.org/web/packages/metavcov/

Find Website and Vignettes at https://luminwin.github.io/metavcov/

Min Lu (Maintainer)

Ahn S., Lu M., Lefevor G.T., Fedewa A.L. and Celimli S. (2015). “Application of Meta-Analysis in Sport and Exercise Science.” In An introduction to intermediate and advanced statistical analyses for sport and exercise scientists, eds. Ntoumanis N and Myers ND (John Wiley & Sons), chapter 11, 233–253. [url] [.bib cite /exp]

B. J. Becker. (2009) “Model-based meta-analysis”. In H. Cooper, L. V. Hedges, and J. C. Valentine, (Ed.), The handbook of research synthesis and meta-analysis, chapter 20, pages 377-395. Russell Sage Foundation.

Wei, Y., & Higgins, J. (2013). Estimating within study covariances in multivariate meta-analysis with multiple outcomes. Statistics in Medicine, 32(7), 119-1205.

R Shiny for Personalized Treatment

Role: Developer (2018)

Precision Surgical Therapy for Adenocarcinoma of the Esophagus and Esophagogastric Junction

Summarizing the sample at https://minlu.shinyapps.io/ICM_minlu/

Predicting for a new patient at https://minlu.shinyapps.io/newpatient/

Min Lu (Maintainer)

Rice T.W., Lu M, Ishwaran H., and Blackstone, E.H. (2019). Precision Surgical Therapy for Adenocarcinoma of the Esophagus and Esophagogastric Junction: A Machine Learning Causal Analysis. Journal of Thoracic Oncology, 14(12): 2164-2175. [pdf] [url] [.bib cite /exp]


Role: Coauthor (2016)

REordering and/or REflecting FACTors for EFA simulations

Download the package at https://cran.r-project.org/web/packages/REREFACT/.

The REREFACT package is an open source package for R, which provides user-defined functions for accessing a post-rotation algorithm that REorders and/or REflects FACTors for each replication of a simulation study with exploratory factor analysis (EFA), [(see details)].

Soyeon Ahn (maintainer, s.ahn@miami.edu), Cengiz Zopluoglu, Seniz Celimli, Min Lu, & Nicholas D. Myers

Myers, N. D., Ahn, S., Lu, M., Celimli, S., Zopluoglu, C. (2016). REREFACT: An R package for reordering and reflecting factors Myers N.D., Ahn S., Lu M., Celimli S., and Zopluoglu C. (2017). Reordering and Reflecting Factors for Simulation Studies with Exploratory Factor Analysis. Structural Equation Modeling: A Multidisciplinary Journal, 24: 112-128. [pdf] [url] [.bib cite /exp]