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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:

  • Packages to be loaded.
  • 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).

Please spend time adding comments so we can understand your code quickly.

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Version

Install

install.packages('RobStatTM')

Monthly Downloads

13,754

Version

1.0.2

License

GPL (>= 3)

Maintainer

Matias Salibian-Barrera

Last Published

March 3rd, 2020

Functions in RobStatTM (1.0.2)

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

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