Overview

nemora streamlines fitting probability density functions to diameter-at-breast-height (DBH) inventories. The package packages workflows for:

  • horizontal point sampling (HPS) tallies with size-bias corrections handled through weighting, and

  • fixed-area inventories where left/right censoring implies truncated support.

The project grew out of research reproducibility stacks in the UBC FRESH Lab, and is now being generalised into a reusable toolkit with Python, CLI, and R interfaces.

Note

This documentation is pre-alpha. Expect rapid iteration while the API stabilises.

Relationship to prior work

Several R libraries—most prominently ForestFit—already support extensive parametric and mixture-based DBH modelling. nemora is positioned as a complementary, workflow-oriented toolkit:

  • Horizontal point sampling (HPS) weighting, censored workflows, and manuscript parity datasets are included out of the box.

  • The Python-first stack (Typer CLI, pandas integration, and planned nemorar bridge) enables the same pipelines to run in notebooks, batch jobs, or mixed-language projects.

  • Candidate features that originated in ForestFit (finite mixtures, JSB variations, EM initialisers) are tracked in candidate-import-from-ForestFit-features.md so upstream credit is explicit while we extend the Python implementation.

Future roadmap items include mixture and piecewise models inspired by the forestry literature cited in the README. Contributions comparing the toolkits or proposing cross-language examples are very welcome.

Current Caveats

  • Grouped Weibull currently relies on a least-squares fit (matching the manuscript parity workflow), while Johnson SB and Birnbaum-Saunders use grouped maximum-likelihood optimisation with numerical Hessian estimates. We plan to consolidate these approaches once the MLE path reproduces the legacy parity results.

  • Johnson SB grouped fits fall back to SciPy’s continuous MLE when the grouped optimiser fails, so diagnostics will report a non-converged status in those cases.

  • Generalised secant (GSM) mixtures are exposed as gsmN distributions; while the grouped estimator supports any component count N >= 2, convergence behaviour for large N still needs empirical evaluation.