In this page, several extension packages for the statistical software R
and a macro for the statistical package SAS
written in the framework of my research are presented.
R
R is a popular open source software tool for statistical analysis and graphics. More information can be
obtained from its webpage. Many extension packages
are available from the Comprehensive R Archive Network (CRAN) accessible from
the R webpage.
I am author of five R extension packages. All of them are available under the GNU license.
Packages smoothSurv
and bayesSurv
implement mainly methods for the regression analysis with time-to-event type of data (survival analysis).
Package glmmAK is devoted to generalized linear mixed models.
Package mixAK contains several methods to analyze normal mixtures.
Package vsePackage accompanies basic statistics courses I have (co-)tought.
Package smoothSurv
The package implements mainly an accelerated failure time model with an error distribution
specified as a penalized normal mixture where the parameters are estimated using a method
of penalized maximum-likelihood. Besides more common right-censored data, the package can also be
used to analyze interval-censored data.
The methodology is described and the package used in
This package is available from CRAN.
smoothSurv on CRAN
Package bayesSurv
In the package, several accelerated failure time models with random effects are implemented. The error distribution
and/or the distribution of the random effects is modelled either using a classical normal mixture with unspecified
number of components or using a penalized normal mixture where the number of components is overspecified.
The model parameters are estimated using the Markov chain Monte Carlo method in the Bayesian framework.
The methodology is described and the package used in
This package is available from CRAN.
bayesSurv on CRAN
Package glmmAK
This package implements maximum-likelihood estimation
in the logistic regression with both binary (logit model) and multinomial response (cumulative logit model),
and in the Poisson regression (log-linear model). Secondly, Bayesian estimation based on MCMC in the logistic and
Poisson regression model with random effects whose distribution is specified as a penalized normal mixture are
implemented.
The methodology is described and the package used in
This package is available from CRAN.
glmmAK on CRAN
Package mixAK
This package mainly implements MCMC analyzis of (multivariate) normal mixtures. For univariate
mixtures, the number of components may be estimated jointly with remaining parameters using
reversible jump MCMC. For multivariate mixtures, several criteria (PED, DIC) are implemented
to guide the selection of the number of components. The package allows for right-, left-, and
interval-censored data.
The package is described in
This package is available from CRAN.
mixAK on CRAN
Package vsePackage
This package accompanies basic statistics courses I have (co-)tought, originally at Faculty of Management,
University of Economics in Prague
(Fakulta managementu
Vysoké
školy
ekonomické v Praze).
Hence the title of the package which stems from the abbreviation VŠE after removal of a Czech accent.
Further, the package accompanies two study texts (in Czech) I have co-authored:
- Komárková, L., Komárek, A., Bína, V. (2007).
Základy analýzy dat a statistického úsudku s příklady v R.
Praha: Oeconomica, Nakladatelství VŠE.
ISBN 978-80-245-1227-3.
- Komárek, A., Komárková, L. (2007).
Statistická analýza závislostí s příklady v R.
Praha: Oeconomica, Nakladatelství VŠE.
ISBN 978-80-245-1226-6.
Both texts can be obtained from me upon
e-mail request.
This package is not available from CRAN.
Windows binary
Manual
SAS
SAS is a commercial statistical software. Its webpage tells you more.
In 2001, I wrote a SAS macro called hetmixed.
Macro hetmixed
The macro can be used to fit the linear mixed model with a normal mixture as a distribution for random effects.
The methodology has been described on the following places.
- Verbeke, G. and Lesaffre, E. (1997).
A linear mixed-effects model with heterogeneity in the random-effects population.
Journal of the American Statistical Association, 91, 217-221.
- Verbeke, G. and Molenberghs, G. (2000).
Linear Mixed Models for Longitudinal Data. New York: Springer-Verlag, pp. 169-188.
The macro itself is documented in my MSc. thesis (Komárek, 2001)
from which a manual has been derived.
The macro is freely available to everyone and I guess it still works. You can use it but, please, do not ask any help from me anymore.
The macro was written almost 10 years ago and that is also the time I used it actively for the last time.
Source code
Manual
Example code