Software
Software
Here you can showcase the software tools, packages, and applications you’ve developed or frequently use in your statistical work.
Statistical Software
R Programming
- R Studio: Integrated development environment for R
- R Markdown: Dynamic documents with R code
- Shiny: Interactive web applications
- ggplot2: Data visualization
- dplyr: Data manipulation
- tidyr: Data tidying
Python
- Jupyter Notebooks: Interactive computing
- Pandas: Data analysis and manipulation
- NumPy: Numerical computing
- Matplotlib: Plotting library
- Seaborn: Statistical data visualization
GIS and Spatial Analysis
- QGIS: Geographic Information System
- ArcGIS: Professional GIS software
- R spatial packages: sf, sp, raster
- Python spatial: GeoPandas, Folium
Custom Applications
Shiny Apps
- Interactive dashboards for data visualization
- Statistical analysis tools
- Data exploration applications
Web Applications
- Modern web interfaces for statistical tools
- API development for data services
- Real-time data processing applications
Development Tools
Version Control
- Git: Source code management
- GitHub: Code hosting and collaboration
Documentation
- Quarto: Scientific and technical publishing
- R Markdown: Reproducible research
- Jupyter: Interactive documentation
Cloud and Deployment
Cloud Platforms
- AWS: Cloud computing services
- Google Cloud: Data processing and storage
- Azure: Microsoft cloud platform
Containerization
- Docker: Application containerization
- Kubernetes: Container orchestration
This section showcases the software tools and technologies I use in my statistical and data science work.