Robust Mixture Discriminant Analysis
Overview
Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 doi:10.1016/j.patcog.2009.03.027, allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.
Install
Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Charles Bouveyron <charles.bouveyron@gmail.com>’
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Authors@R: c(person(given = c("Charles", "Bouveyron", "&", "Stephane"),
family = "Girard",
role = "aut"),
person(given = "Charles",
family = "Bouveyron",
role = "cre",
email = "charles.bouveyron@gmail.com"))
as necessary.- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 3includes a package overview page
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
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- Documented parameters
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Downloads
Dependencies
Installing robustDA adds 12 packages (7 with compiled code, 6.9 MB of source tarballs). Heaviest: mclust 2.7 MB, Rcpp 1.8 MB, RcppArmadillo 1.0 MB.
Code & Tests
People & History
Maintainer since 1.0 (2014)
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.2Latest
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- RR 3.3.0 released · 2016-05-03
- RR 3.2.0 released · 2015-04-16
- 1.12015-01-14 · diff ↗
- 1.02014-09-29
- RR 3.1.0 released · 2014-04-10
Package metadata
- First published
- 2014-09-29
- Total releases
- 3 / 12 yrs
- License
- GPL-2 OSI
- Download size
- 3.2 KB
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