A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods.

A Generalized Suffix Tree for any Python iterable using Ukkonen's algorithm, with Lowest Common Ancestor retrieval.

A tool to calculate a building project's impacts on biodiversity over the entire life cycle. The tool was developed as part of the "Doughnut for Urban Development" project and manual. For more information, visit https://www.home.earth/doughnut

Sparse coding in PyTorch via the Locally Competitive Algorithm (LCA)

Appa Build is a package used to build impact model which are meant to be loaded and executed by the Appa Run package. It is part of the Appa LCA framework.

Assessment of environmental impact of energy systems by integrating data from LCA, MuSIASEM

A program for Life Cycle Assessment (LCA) calculations of supply chain waste and material footprints

Mining NCBI BLAST output

Appa Run is a package used to load and execute impact model produced by the Appa Build package. It is part of the Appa LCA framework.

PELCA - Power Electronics Life Cycle Assessment

🛢️🛩️ Calculation toolkit for aircraft fuel consumption

Data for Spatial Units

🎛️🌿 Template for a Brightway-enabled interactive Web Application based on Panel/Pyodide

Read and parse SimaPro CSV LCI and LCIA files

Converter for Life Cycle Assessment (LCA) inventory file formats

🏗️ Life Cycle Assessment (LCA) extension for Blender - Analyze environmental impact of IFC building models with KBOB & ÖKOBAUDAT databases. Calculate embodied carbon, visualize results, and optimize directly in your Blender workflow.

Clustering tools for the Lifecycle Screening of Emerging Technology (LiSET) framework

PoC Integration of Digital Product Passports (DPP) in IFC using buildingSmart Data Dictionary (bSDD)

Dashboard created in Dash (Python) with the results of audience segmentation using LCA (R).