Version 3.0 of the NLME library, software for fitting and analyzing
mixed-effects models in S-PLUS, is being released after extensive beta
testing.
The NLME software has been completely redesigned to emphasize a
modular, object-oriented design that facilitates users' incorporating
new methods for existing generic functions (e.g. new classes of
correlation structures and variance functions). Some of the new
features of NLME 3.0 are:
- Linear and nonlinear multilevel models for data with multiple nested
grouping levels. These use new computational methods described in
http://nlme.stat.wisc.edu/pub/NLME/CompMulti.pdf.
- a new class, called groupedData, for representing data grouped
according to one or more nested factors. Methods for plotting,
summarizing, and fitting groupedData objects are also included.
- extensive use of Trellis plots for exploring grouped data and
checking models fitted to such data. The plot methods for fitted
objects include a formula argument which gives them unlimited
flexibility for generating diagnostic plots.
- new classes of correlation structures including ARMA(p,q) models,
spatial correlation structures (Gaussian, exponential, etc.) with and
without nugget effects, and general correlation with no particular
structure.
- new and redesigned classes of variance functions using arbitrary
covariates and grouping of parameters (the previous version, 2.1, used
the fitted values as the covariate for the built-in variance
functions). Because the correlation structure and the variance
function are independently specified, heterogeneous AR1, ARMA, etc.
models are handled naturally.
- a gls function for fitting linear models with correlation structures
and/or variance functions. You can think of it as lme without random
effects, or lm with correlation structures and variance functions.
- a gnls function for fitting nonlinear models with correlation
structures and/or variance functions. You can think of it as
nlme without random effects, or nls with correlation structures and
variance functions.
- an extensive set of examples. A separate directory contains
groupedData objects for the datasets used in the book "SAS System for
Mixed Models", by Littel, Milliken, Stroup, and Wolfinger. Sample lme
analyses that parallel the PROC MIXED analyses from that book are also
given. We hope this will enable people who are familiar with PROC
MIXED to learn lme more quickly.
The distribution files for Unix (S-PLUS 3.4) and Windows (S-PLUS 3.3
and higher) can be obtained from the U. Wisconsin-Madison site
http://nlme.stat.wisc.edu
ftp://nlme.stat.wisc.edu/pub/NLME
or from the Bell Labs site
http://cm.bell-labs.com/stat/NLME
ftp://cm.bell.labs.com/cm/ms/departments/sia/project/nlme
The Unix version is available as a gzip'd tar file
(nlme3_0.tar.gz). The Windows version (courtesy of Prof. Brian
D. Ripley) is available as a zip'd file (nlme30.zip). If you use ftp
to transfer one of these files, remember to set binary mode for the
transfer.
If you decide to down load the code, we strongly recommend you
subscribe to nlme-help@stat.wisc.edu. Send a message with the word
"subscribe" in the body to nlme-help-request@stat.wisc.edu. This is
the recommended list for users to ask questions, report bugs, and make
suggestions about the code.
Regards,
Jose' Pinheiro
Bell Laboratories jcp@research.bell-labs.com
600 Mountain Avenue, Room 2C-258 office: (908) 582-2390
Murray Hill, NJ 07974 fax: (908) 582-3340
and
Douglas Bates bates@stat.wisc.edu
Statistics Department 608/262-2598
University of Wisconsin - Madison http://www.stat.wisc.edu/~bates/
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