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RANSAC
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
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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 07 Mar. 2026.
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