|
|
-
A robust and sparse K-means clustering algorithm.
An R package (called RSKC) to compute robust and sparse clusters
based on K-means can be found in
CRAN
(a direct link is
here). This work is based on
Kondo, Y., Salibian-Barrera, M. and Zamar, R. H. (2012)
A preprint is available at arXiv:1201.6082v1.
Yumi's MSc thesis is available
here.
- An outlier robust fit for Generalized Additive Models - R
package to compute
a robust fit for Generalized Additive Models can be found in here.
Joint work with Azadeh Alimadad.
- Penalised S-regression splines - R code to compute
penalised S-regression splines can be found here.
Joint work with Tharmaratnam, K., Claeskens, G. and Croux, C. at
K. U. Leuven.
- Fast tau -
MATLAB / OCTAVE and R
code to compute the tau-regression estimator
for linear regression
can be found here.
This is joint work with Gert Willems.
- Globally robust confidence intervals for
simple linear regression models -
R
code to compute globally robust confidence intervals
for the slope of a simple linear regression model
can be found here.
This is joint work with Jorge Adrover.
- Fast S -
Plain R & S-PLUS
code to compute the Fast-S estimator
for linear regression
can be found here.
This algorithm is also implemented in
the S- and MM-estimators in the
robustbase
package for R (see below).
This is joint work
with Prof. Victor Yohai.
-
roblm -
The roblm R package for MM-regression estimators,
version 0.6.
The latest version of
this package
can be found in
CRAN. This package is not longer maintained. All its
functionality has been incorporated into the
robustbase
package (see below).
- robustbase - An R package implementing
state-of-the-art robust methods, particularly those described in the book
Robust statistics, theory and methods, by Maronna, Martin and Yohai,
Wiley, 2006.
Available in
CRAN. Currently maintained by Martin Maechler.
- R package for the Linear Grouping Algorithm
-
[Van Aelst, S., Wang, X., Zamar, R., and Zhu, R. Linear grouping using
orthogonal regression, Computational Statistics and Data Analysis (2006)].
It is able to use multiple CPUs
if available and it implements the GAP statistic
[Tibshirani, R., Walther, G. and Hastie, T. Estimating the number of clusters
in a data set via the gap statistic, JRSS B, (2001)].
It is available
here.
This is joint work with Justin Harrington.
|