stemflow
The facts below are what the maker publishes about stemflow, checked and dated. Our editors have not written about it yet.
The facts
Each one read from stemflow's own pages, with the date it was checked.
What it does
- Base models
- Users can supply scikit-learn-style BaseEstimator classes or use a scikit-learn-style Maxent model as a base model.checked 12 Oct 2026
- Contributions and collaboration
- The project welcomes pull requests and application-level research collaboration.checked 11 Oct 2026
- Data import formats
- pandas DataFrame; DuckDB (.duckdb); Parquet (.parquet)checked 25 Sep 2026
- Data support
- It supports continuous and categorical features, plus static and dynamic features.checked 11 Oct 2026
- Deployment
- desktopchecked 25 Sep 2026
- Features
- It supports spatiotemporal modeling and prediction, overall feature importance calculation, and plotting spatiotemporal dynamics.checked 11 Oct 2026
- Features and data
- It supports feature importance calculations, plots of spatiotemporal dynamics, and continuous, categorical, static, and dynamic features.checked 12 Oct 2026
- Indexing
- The package supports user-defined 2D spatial indexing with CRS, 3D spherical indexing, and user-defined temporal indexing.checked 11 Oct 2026
- Installation
- The site gives pip and conda installation commands and a pip command for installing the latest GitHub beta.checked 11 Oct 2026
- Integrations
- The documented installation methods are pip, a GitHub beta install, and conda; examples also use XGBoost models and eBird survey data.checked 12 Oct 2026
- Intended users
- The introduction describes stemflow as a user-friendly Python package for modeling large spatiotemporal datasets, including ecological abundance estimation.checked 12 Oct 2026
- License
- The repository provides the software under the MIT License, which permits use, copying, modification, distribution, sublicensing, and sale subject to its stated conditions.checked 12 Oct 2026
- Memory handling
- Lazy loading and database queries are supported to reduce memory load during parallel computing.checked 12 Oct 2026
- Memory tools
- Lazy loading and database queries are described as ways to reduce memory load during parallel computing.checked 11 Oct 2026
- Product
- stemflow is a Python toolkit for Adaptive Spatio-Temporal Exploratory Models (AdaSTEM).checked 11 Oct 2026
- Purpose
- Stemflow is a Python toolkit for Adaptive Spatio-Temporal Exploratory Models (AdaSTEM), with typical use in daily abundance estimation from eBird citizen science surveys.checked 12 Oct 2026
- Regression models
- Yeschecked 25 Sep 2026
- Resource note
- The project says stemflow may consume large computational resources as data volume grows.checked 12 Oct 2026
- Scripting support
- Yeschecked 25 Sep 2026
- Spatial and temporal indexing
- It supports user-defined 2D spatial indexing, 3D spherical indexing, and user-defined temporal indexing.checked 12 Oct 2026
- Support and collaboration
- The project welcomes pull requests, application-level cooperation, and research collaboration.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 25 Sep 2026
Where it runs
- Model approach
- It splits input data into spatial or spatiotemporal cells called stixels, trains each separately, and aggregates their predictions into an ensemble.checked 12 Oct 2026
- Modeling approach
- It splits input data with Quadtree or Sphere Quadtree, trains each spatiotemporal split separately, and aggregates the ensemble for prediction.checked 11 Oct 2026
