[JSS-Announce] 4 new issues for JSS

[Jan de Leeuw]

Generalized Additive Models for Location Scale and Shape (GAMLSS) in R
D Mikis Stasinopoulos, Robert A. Rigby
Dec 31, '07, 12:00 AM
Vol. 23, Issue 7, Dec 2007

Abstract:

GAMLSS is a general framework for fitting regression type models where  
the distribution of the response variable does not have to belong to  
the exponential family and includes highly skew and kurtotic  
continuous and discrete distribution. GAMLSS allows all the parameters  
of the distribution of the response variable to be modelled as linear/ 
non-linear or smooth functions of the explanatory variables. This  
paper starts by defining the statistical framework of GAMLSS, then  
describes the current implementation of GAMLSS in R and finally gives  
four different data examples to demonstrate how GAMLSS can be used for  
statistical modelling.

  Read more…
Inference in Graphical Gaussian Models with Edge and Vertex Symmetries  
with the gRc Package for R
Søren Højsgaard, Steffen L. Lauritzen
Dec 31, '07, 12:00 AM
Vol. 23, Issue 6, Dec 2007

Abstract:

In this paper we present the R package gRc for statistical inference  
in graphical Gaussian models in which symmetry restrictions have been  
imposed on the concentration or partial correlation matrix. The models  
are represented by coloured graphs where parameters associated with  
edges or vertices of same colour are restricted to being identical. We  
describe algorithms for maximum likelihood estimation and discuss  
model selection issues. The paper illustrates the practical use of the  
gRc package.

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Algorithms for Linear Time Series Analysis: With R Package
A. Ian McLeod, Hao Yu, Zinovi L. Krougly
Dec 31, '07, 12:00 AM
Vol. 23, Issue 5, Dec 2007

Abstract:

Our ltsa package implements the Durbin-Levinson and Trench algorithms  
and provides a general approach to the problems of fitting,  
forecasting and simulating linear time series models as well as  
fitting regression models with linear time series errors. For  
computational efficiency both algorithms are implemented in C and  
interfaced to R. Examples are given which illustrate the efficiency  
and accuracy of the algorithms. We provide a second package FGN which  
illustrates the use of the ltsa package with fractional Gaussian noise  
(FGN). It is hoped that the ltsa will provide a base for further time  
series software.

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systemfit: A Package for Estimating Systems of Simultaneous Equations  
in R
Arne Henningsen, Jeff D. Hamann
Dec 31, '07, 12:00 AM
Vol. 23, Issue 4, Dec 2007

Abstract:

Many statistical analyses (e.g., in econometrics, biostatistics and  
experimental design) are based on models containing systems of  
structurally related equations. The systemfit package provides the  
capability to estimate systems of linear equations within the R  
programming environment. For instance, this package can be used for  
"ordinary least squares'' (OLS), ""seemingly unrelated  
regression'' (SUR), and the instrumental variable (IV) methods "two- 
stage least squares'' (2SLS) and "three-stage least squares'' (3SLS),  
where SUR and 3SLS estimations can optionally be iterated.  
Furthermore, the systemfit package provides tools for several  
statistical tests. It has been tested on a variety of datasets and its  
reliability is demonstrated.

  Read more…
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