sars 0.5.0

Species-area relationship curve fitting in Python

sars

Species-area relationship curve fitting in Python.

A conceptual mirror of the R sars package (Matthews et al. 2019), native to the Python scientific stack.

Installation

pip install sars

Features

• 20 SAR models — power, logarithmic, asymptotic, sigmoid, and more
• Multi-model inference — fit all models at once, ranked by AICc with Akaike weights
• Model averaging — weighted-average predictions across candidate models
• Bootstrap confidence intervals — percentile-based CIs for averaged predictions
• R-validated — all models tested against R sars package reference values

Quick start

import sars

# Load the built-in Galapagos dataset (Preston 1962)
galap = sars.load_galap()

# Fit a single model
fit = sars.sar_power(galap)
print(fit)
# SARFit(model='power', c=33.1792  z=0.2832, R²=0.4912, AICc=189.03)

# Fit all 20 models and compare
multi = sars.sar_multi(galap)
print(multi.summary[["model", "AICc", "delta_AICc", "weight"]].head())

# Model-averaged predictions
avg = sars.sar_average(galap)
predictions = avg.predict([1.0, 10.0, 100.0])

# Bootstrap confidence intervals
ci = sars.bootstrap_ci(galap, n_boot=100)

Available models

Type	Models
Non-asymptotic	power, powerR, loga, linear, epm1, epm2, p1, p2
Asymptotic convex	koba, monod, negexpo, asymp, ratio
Asymptotic sigmoid	mmf, gompertz, weibull3, weibull4, chapman, betap, heleg

Each model has a dedicated function (e.g. sars.sar_power(), sars.sar_negexpo()) and returns a SARFit object with parameters, R², AIC, AICc, and BIC.

Citation

If you use this software, please cite it:

McMeen, J. (2026). sars: Species-Area Relationship curve fitting in Python.