# RobStatTM v1.0.2

Monthly downloads

## 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.

## Readme

# 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:

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

Isolate the problem by creating a

**minimal reproducible example**(see below)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.

- This is the first step
- This is the second step
- 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.

## 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

## Last month downloads

## Details

Date | 2020-03-02 |

LazyData | yes |

License | GPL (>= 3) |

RoxygenNote | 6.1.1 |

Encoding | UTF-8 |

VignetteBuilder | knitr, R.rsp |

NeedsCompilation | yes |

Packaged | 2020-03-02 20:35:25 UTC; matias |

Repository | CRAN |

Date/Publication | 2020-03-03 00:30:02 UTC |

imports | DEoptimR , DT , ggplot2 , graphics , gridExtra , methods , PerformanceAnalytics , pyinit , robustbase , rrcov , shiny , shinyjs , stats , utils , xts |

depends | fit.models , R (>= 3.5.0) |

suggests | knitr , R.rsp |

Contributors | Manuel Koller, Martin Maechler, Matias Salibian-Barrera, Victor Yohai, Ricardo Maronna, Doug Martin, Gregory Brownson, Kjell Konis, Christophe Croux, Gentiane Haesbroeck |

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