R Packages
This section contains documentation for R packages I’ve developed, demonstrating best practices in R package development, documentation, and testing.
Available Packages
myrpackage
A multilingual greeting and farewell package that serves as an example of proper R package structure.
Features:
- Multilingual support (English, Spanish, French, Portuguese, German, Italian)
- Comprehensive documentation with roxygen2
- Full test coverage with testthat
- Continuous integration with GitHub Actions
- Proper package structure following R standards
unconfoundedr
Test (un)confoundedness by comparing an effect from an RCT-like dataset to the same estimand from an observational dataset. Includes robust estimators, inference, and transportability tools.
Features:
- IPW and AIPW (doubly robust) estimators for the marginal ATE
- Bootstrap confidence intervals and Wald test
- Transport modes:
none,rct_to_obs, andauto(KS/energy shift detection) - Diagnostics for propensity overlap, stabilized weights, trimming, and transport ESS
Python Packages
This section includes Python libraries I’ve developed or contributed to, with a focus on statistical modeling, survival analysis, and reproducibility.
Available Packages
genSurvPy
A Python package for generalized survival analysis. It includes tools for simulation, estimation, and diagnostics in complex time-to-event models, including proportional hazards and accelerated failure time frameworks.
Features:
- Flexible simulation of survival datasets under user-defined models
- Support for right-censored and interval-censored data
- Estimation under both PH and AFT models using parametric or semi-parametric methods
- Modular API for custom hazard and survival functions
- Publication-ready plots using matplotlib and seaborn
- Documentation built with Sphinx, deployed to both Read the Docs and this site
For more R and Python packages, as well as general data science content, visit my GitHub profile.