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pcaPP - Robust PCA by Projection Pursuit

Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>, Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.

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openblascpp

11.02 score 2 stars 372 dependents 299 scripts 39k downloads

rrcov - Scalable Robust Estimators with High Breakdown Point

Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point: principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>), linear and quadratic discriminant analysis (Todorov and Pires (2007)), multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>), outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>). See also Todorov and Filzmoser (2009) <urn:isbn:978-3838108148>, Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.

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openblas

10.62 score 2 stars 97 dependents 680 scripts 21k downloads

tclust - Robust Trimmed Clustering

Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, Fritz et al. (2012) <doi:10.18637/jss.v047.i12>, Garcia-Escudero et al. (2011) <doi:10.1007/s11222-010-9194-z> and others.

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openblascppopenmp

8.50 score 4 stars 4 dependents 85 scripts 3.1k downloads

spls - Sparse Partial Least Squares (SPLS) Regression and Classification

Provides functions for fitting a sparse partial least squares (SPLS) regression and classification (Chun and Keles (2010) <doi:10.1111/j.1467-9868.2009.00723.x>).

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8.39 score 9 dependents 180 scripts 10k downloads

robust - Port of the S+ "Robust Library"

Methods for robust statistics, a state of the art in the early 2000s, notably for robust regression and robust multivariate analysis.

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openblasglibc

8.04 score 9 dependents 780 scripts 10k downloads

fsdaR - Robust Data Analysis Through Monitoring and Dynamic Visualization

Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis (FSDA)' which is comprehensive and computationally efficient software package for robust statistics in regression, multivariate and categorical data analysis. The current R version implements tools for regression: (forward search, S- and MM-estimation, least trimmed squares (LTS) and least median of squares (LMS)), for multivariate analysis (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression. The distinctive feature of our package is the possibility of monitoring the statistics of interest as a function of breakdown point, efficiency or subset size, depending on the estimator. This is accompanied by a rich set of graphical features, such as dynamic brushing, linking, particularly useful for exploratory data analysis.

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openjdk

5.41 score 5 stars 103 scripts 345 downloads

rrcov3way - Robust Methods for Multiway Data Analysis, Applicable also for Compositional Data

Provides methods for multiway data analysis by means of Parafac and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (2018) <doi:10.1080/02664763.2017.1381669>). Several optimization methods alternative to ALS are available (Simonacci and Gallo (2019) <doi:10.1016/j.chemolab.2019.103822>, Simonacci and Gallo (2020) <doi:10.1007/s00500-019-04320-9>).

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4.39 score 49 scripts 336 downloads

rrcovNA - Scalable Robust Estimators with High Breakdown Point for Incomplete Data

Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point for Incomplete Data (missing values) (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).

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3.81 score 1 stars 129 scripts 743 downloads

rda - Shrunken Centroids Regularized Discriminant Analysis

Provides functions implementing the shrunken centroids regularized discriminant analysis for classification purpose in high dimensional data. The method is described in Guo at al. (2013) <doi:10.1093/biostatistics/kxj035>.

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3.64 score 1 dependents 29 scripts 380 downloads

trimcluster - Cluster Analysis with Trimming

Trimmed k-means clustering. The method is described in Cuesta-Albertos et al. (1997) <doi:10.1214/aos/1031833664>.

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3.62 score 1 dependents 28 scripts 1000 downloads

rrcovHD - Robust Multivariate Methods for High Dimensional Data

Robust multivariate methods for high dimensional data including outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).

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cpp

3.50 score 63 scripts 441 downloads

FRB - Fast and Robust Bootstrap

Perform robust inference based on applying Fast and Robust Bootstrap on robust estimators (Van Aelst and Willems (2013) <doi:10.18637/jss.v053.i03>). This method constitutes an alternative to ordinary bootstrap or asymptotic inference. procedures when using robust estimators such as S-, MM- or GS-estimators. The available methods are multivariate regression, principal component analysis and one-sample and two-sample Hotelling tests. It provides both the robust point estimates and uncertainty measures based on the fast and robust bootstrap.

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1.00 score 4 scripts 245 downloads