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RANSAC  

Robust Model Fitting Using the RANSAC Algorithm
View on CRAN: Click here


Download and install RANSAC package within the R console
Install from CRAN:
install.packages("RANSAC")

Install from Github:
library("remotes")
install_github("cran/RANSAC")

Install by package version:
library("remotes")
install_version("RANSAC", "0.1.0")



Attach the package and use:
library("RANSAC")
Maintained by
Jadson Abreu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-05-07
Latest Update: 2025-05-07
Description:
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) <doi:10.1145/358669.358692>.
How to cite:
Jadson Abreu (2025). RANSAC: Robust Model Fitting Using the RANSAC Algorithm. R package version 0.1.0, https://cran.r-project.org/web/packages/RANSAC. Accessed 04 Oct. 2026.
Previous versions and publish date:
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Complete documentation for RANSAC
Functions, R codes and Examples using the RANSAC R package
Full RANSAC package functions and examples
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