data science practitioner
machine learning researcher
A collection of my R packages for machine learning, data visualization, and reproducible research.
msaenet implements the multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions. Multi-step adaptive estimation based on MCP-net or SCAD-net is also supported.
Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection. The OHPL method takes the homogeneity structure in high-dimensional data into account and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.
A collection of my Shiny apps for reproducible interactive data analysis.
Proof-of-concept project exploring the technical possibility and complexity for bioinformatics workflow containerization and orchestration using Docker and liftr. All 18 available Bioconductor workflows were containerized.
hdnom.io is the web application for the hdnom package. All the 9 model types in the hdnom package are supported. It streamlined the process of nomogram building, model validation, model calibration, and reproducible report generation.
The web app has been included in the Shiny User Showcase.
Web application for predicting the binding probability of 623 potential drug targets for given molecule(s). Driven by machine learning modeling of large-scale public chemogenomics data.
ImgSVD is a Shiny app for image compression via singular value decomposition (SVD). ImgSVD is inspired by Yihui Xie's comment in Yixuan Qiu's article on image compression via singular value decomposition with the R package rARPACK. [Photo credit: Crouching Tiger, Hidden Dragon]
Created by Jeffery Horner, rApache is a creative project supporting web application development using the R statistical language and environment and the Apache web server.
Here is the translated documentation in Chinese, which was firstly released in March, 2010. The current version was revised in December, 2011.
Google's style guide for R gurus. The style guide conveys that consistency and detailed usages are the very elements in R coding.
Translated in Dec, 2011.