# RobStatTM v1.0.2

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## Robust Statistics: Theory and Methods

Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>. This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.

# RobStatTM

This repository contains a development version of the companion package to the 2nd edition of the book Robust Statistics: Theory and Methods, by Ricardo Maronna, Doug Martin, Victor Yohai and Matias Salibian-Barrera.

• The latest official version of the package is available on CRAN. You should probably use that version.
• To install the "development" version on GitHub, use
devtools::install_github("msalibian/RobStatTM")

• The scripts reproducing the examples and figures in the book can be found in the folder inst/scripts.

#### Bug reports

We use GitHub issues to track and solve potential bugs in our package. When submitting your bug report please:

1. Check if your issue / bug has been fixed by trying to reproduce it using the latest version of the package.

2. Isolate the problem by creating a minimal reproducible example (see below)

3. Create an issue for this repository. Refer to this page for instructions on how to create a GitHub issue.

A good bug report should not require others to contact you to find more information. Please try to be as detailed as possible in your report. What is your environment? What steps will reproduce the issue? What outcome did you expect and what outcome did you get?

###### Example:

A short and descriptive bug report title

A summary of the issue and the OS environment in which it occurs. Include the steps required to reproduce the bug.

1. This is the first step
2. This is the second step
3. Further steps, etc.

Any other information you want to share that is relevant to the issue being reported. This might include the lines of code that you have identified as causing the bug, and potential solutions.

##### Minimal reproducible examples

(This section is adapted from Rob Hyndman's notes on minimal reproducible examples).

A Minimal reproducible example (MRE) is intended to reproduce an error using the smallest amount of code possible. To check that your MRE code is reproducible, try running it in a fresh R session before you submit the issue. Using minimal reproducible examples saves package developers time in wading through messy code that is not relevant to the apparent bug.

A MRE should consist of a single R script file that can be run without error in a fresh R session, and should contain the following three sections:

• The shortest amount of code that reproduces the problem.
• The output of sessionInfo() as a comment.

Please remove anything that is not necessary to reproduce the problem.

Try to use one of the built-in datasets if possible. If you need to include some data, then use dput() so the data can be included as part of the same text file. In most cases, you do not need to include all of your data, just a small subset that will allow the problem to be reproduced.

If you randomly generate some data, use set.seed(somenumber).

## Functions in RobStatTM

 Name Description covRobMM MM robust multivariate location and scatter estimator SMPY SM regression estimator using Pen~a-Yohai candidates covRob Robust multivariate location and scatter estimators ShinyUI Open the Shiny interface for the package covClassic Classical Covariance Estimation lmrobdetDCML Robust Distance Constrained Maximum Likelihood estimators for linear regression drop1.lmrobdetMM RFPE of submodels of an lmrobdetMM fit lmrobdetMM.RFPE Robust Final Prediction Error locScaleM Robust univariate location and scale M-estimators MMPY MM regression estimator using Pen~a-Yohai candidates shock Shock data INVTR2 Robust R^2 coefficient of determination initPP Robust multivariate location and scatter estimators bisquare Tuning parameter the rho loss functions pcaRobS Robust principal components biochem Biochem data lmrobM Robust estimators for linear regression with fixed designs covRobRocke Rocke's robust multivariate location and scatter estimator neuralgia Neuralgia data rho Rho functions rhoprime The first derivative of the rho function bus Bus data cov.dcml Approximate covariance matrix of the DCML regression estimator. fastmve Minimum Volume Ellipsoid covariance estimator oats Oats data lmrobM.control Tuning parameters for lmrobM alcohol Alcohol data wine Wine data opt Tuning parameter for a rho function in the (asymptotic bias-) optimal family image Image data lmrobdet.control Tuning parameters for lmrobdetMM and lmrobdetDCML prcompRob Robust Principal Components Cont'd step.lmrobdet Robust stepwise using RFPE logregBY Bianco and Yohai estimator for logistic regression algae Algae data flour Flour data skin Skin data mopt Tuning parameter for a rho function in the modified (asymptotic bias-) optimal family stackloss Stackloss data scaleM M-scale estimator rhoprime2 The second derivative of the rho function lmrobLinTest Robust likelihood ratio test for linear hypotheses waste Waste data logregWML Weighted likelihood estimator for the logistic model logregWBY Bianco and Yohai estimator for logistic regression DCML DCML regression estimator vehicle Vehicle data lmrobdetMM Robust linear regression estimators glass Glass data hearing Hearing data mineral Mineral data resex Resex data refine.sm IRWLS iterations for S- or M-estimators No Results!

## Vignettes of RobStatTM

 Name img/CovarianceChi.png img/CovarianceDistDist.png img/CovarianceEigen.png img/CovarianceEllipses.png img/CovarianceMd.png img/CovarianceSummary.png img/Covariance_PlotOpts.png img/Data1.png img/DataLibrary.png img/Data_upload.png img/Home.png img/LR_NormQQ.png img/LR_OverScat.png img/LR_PlotOpts.png img/LR_ResDens.png img/LR_ResVsDist.png img/LR_ResVsFit.png img/LR_ResVsIndex.png img/LR_RespVsFit.png img/Location-Scale.png img/PcaSummary.png img/PcaSummary2.png img/RobustLR_select.png img/RobustLR_summary.png ShinyUI.Rnw VignetteRobStatTM.pdf.asis No Results!