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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "RANSAC" in publications use:'
type: software
license: MIT
title: 'RANSAC: Robust Model Fitting Using the RANSAC Algorithm'
version: 0.1.0
doi: 10.32614/CRAN.package.RANSAC
abstract: Provides tools for robust regression model fitting using the RANSAC (Random
  Sample Consensus) algorithm. RANSAC is an iterative method to estimate parameters
  of a model from a dataset that contains outliers. This package allows fitting both
  linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable
  models in the presence of noisy or corrupted data. The methods are particularly
  useful in contexts where traditional least squares regression fails due to the influence
  of outliers. Implementations include support for performance metrics such as RMSE,
  MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles
  (1981) <https://doi.org/10.1145/358669.358692>.
authors:
- family-names: Abreu
  given-names: Jadson
  email: jadson.ap@gmail.com
repository: https://jadson-abreuv.r-universe.dev
commit: c5a4bc3b4b477d247a70e8e4d4f8263bd531a6b6
date-released: '2025-05-07'
contact:
- family-names: Abreu
  given-names: Jadson
  email: jadson.ap@gmail.com