Nan Xiao

Software

Python Packages
R Packages
Web Applications
Machine Learning Workflows
Open Source Contributions
Awesome Lists
VS Code Extensions
Hugo Themes

Python packages

  • tinytopics
    GPU-accelerated topic modeling via sum-to-one constrained neural Poisson NMF
    PyPI | GitHub
  • tinyvdiff
    Minimalist visual regression testing plugin for pytest
    PyPI | GitHub
  • py-pkglite
    A simple framework for packing source packages written in any programming language into a text file and restoring them into the original directory structure
    PyPI | GitHub
  • rtflite
    Lightweight RTF composer for Python
    PyPI | GitHub

R packages

Infrastructure and automation

Statistical machine learning

  • msaenet
    Multi-step adaptive estimation for sparse regressions
    CRAN | GitHub | Paper
  • stackgbm
    Minimalist implementation of model stacking for gradient boosted tree models built by xgboost, lightgbm, and catboost
    CRAN | GitHub
  • oneclust
    Maximum homogeneity clustering for univariate data
    CRAN | GitHub
  • logreg
    Regularized logistic regressions with computational graphs
    GitHub
  • OHPL
    Ordered homogeneity pursuit lasso for group variable selection
    CRAN | GitHub | Paper
  • RECA
    Relevant component analysis for supervised distance metric learning
    CRAN | GitHub
  • enpls
    Ensemble partial least squares regression
    CRAN | GitHub

Statistical graphics and computing

Bioinformatics and cheminformatics

Web applications

JavaScript apps

Shiny apps

Shiny apps, widgets, and templates for interactive data analysis.

Machine learning workflows

Open source contributions

  • gsDesign
    Group sequential clinical trial design, largely as presented in Jennison and Turnbull (2000)
    CRAN | GitHub | gsDesign: 15 Years of Development in 5 Minutes (4K 60fps)
  • gsDesign Shiny app
    Web application for group sequential clinical trial design
    Mirror 1 | Mirror 2
  • gsDesign2
    Group sequential design with non-constant effect
    CRAN | GitHub
  • simtrial
    Clinical trial simulation
    CRAN | GitHub
  • gMCPLite
    Lightweight fork of gMCP for graph-based multiple comparison procedures
    CRAN | GitHub
  • gMCPShiny
    Shiny app for graphical multiplicity control
    GitHub
  • r2rtf
    Create production-ready Rich Text Format (RTF) tables and figures
    CRAN | GitHub
  • metalite
    Unified data structure for metadata information in clinical analysis & reporting (A&R), leveraging the Analysis Data Model (ADaM) datasets for consistent and accurate metadata representation
    CRAN | GitHub
  • metalite.ae
    Analyzes adverse events in clinical trials using the metalite data structure. Simplifies the workflow to create production-ready tables, listings, and figures discussed in the adverse events analysis chapters of R for Clinical Study Reports and Submission.
    CRAN | GitHub
  • boxly
    Interactive box plot using plotly for clinical data analysis
    CRAN | GitHub
  • forestly
    Interactive forest plot for clinical trial safety analysis using metalite, reactable, plotly, and Analysis Data Model (ADaM) datasets
    CRAN | GitHub
  • PDXNet Portal
    Patient-derived xenograft (PDX) model, data, workflow, and tool discovery
    Paper
  • ashr
    Methods for adaptive shrinkage, using Empirical Bayes
    CRAN | Paper
  • dml
    Distance metric learning in R
    CRAN | Paper

Awesome lists and code recipes

  • awesome-shiny-extensions
    A curated list of R packages that offer extended UI or server components for Shiny
  • awesome-webr
    A curated list of awesome resources for learning WebR, a version of the statistical language R compiled for the browser and Node.js using WebAssembly
  • r-base-shortcuts
    A collection of lesser-known base R idioms and shortcuts for writing concise and fast base R code
  • r-rust-pkgs
    R packages using Rust on CRAN
  • r-future-recipes
    Guides and examples for the R future framework for parallel computing
  • r-windows-paths
    Key R toolchain paths for Windows systems
  • deep-learning-recipes
    R implementation for selected machine learning methods with deep learning frameworks
  • llm-cliches
    A collection of commonly used clichés and phrases in Large Language Models (LLMs) outputs.

Visual Studio Code extensions

Hugo/blogdown themes

Collections

Archives

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