Software:
- space (Sparse PArtial Correlation
Estimation): space is an R package for estimation and identification of
non-zero partial correlations via sparse regression techniques. It can be
downloaded from
http://cran.r-project.org/;
or by clicking
space.
It is useful in construction of large networks. For more details, see the
paper ``Peng,
J., Wang, P., Zhou, N.F., and Zhu, J. (2007). Partial
Correlation Estimation by Joint Sparse Regression Models",Journal of the American Statistical Association , Vol. 104, No. 486,
735-746 [technical report:
pdf;
arXiv:0811.4463
(stat.ME)]
- fpca
(Functional Principal Component Analysis): fpca is an R package for
estimation eigen-values and eigen-functions of the convariance kernel (fpca)
via sparsely observed functional data. It can be downloaded
from
http://cran.r-project.org/;
or by clicking fpca. It is
useful in longitudinal studies. For more details, see the paper ``Peng, J.
and
Paul, D. (2007a). A geometric approach to maximum likelihood
estimation of the functional principal components from sparse longitudinal
data",Journal
of Computational and Graphical Statistics, In Press [pdf] (arXiv:0710.5343v1
[stat.ME]; 1-29-09: updated R package
fpca
- remMap
(REgularized Multivariate regression for
identifying MAster Predictors): remMap is an R
package for fitting multivariate regression models under
high-dimension-low-sample-size setting. It can be downloaded
from
http://cran.r-project.org/;
or by clicking
remMap. It is useful in construction of
networks by using two types of high dimensional data, say CGH array and
expression array. For more details, see the paper ``Peng,
J., Zhu, J. , Bergamaschi, A., Han, W., Noh, D.Y., Pollack, J.R.,
and Wang, P. (2008) Regularized Multivariate Regression for Identifying
Master Predictors with Application to Integrative Genomics Study of Breast
Cancer",
Annals of Applied
Statistics, n Press [technical report:
pdf;
arXiv:0812.3671v1].
- dynamics: dynamics is an R package
for fitting autonomous dynamical systems using spline basis (B-splines or
cubic polynomial splines). It can be downloaded by clicking
dynamics. It is useful for fitting the
underlying common (autonomous) systems nonparametrically for a group of
random curves when only a snapshot of each sample curve is observed. For
more details, see the paper ``Paul, D., Peng, J. and Burman, P.
(2009) Semiparametric modeling of autonomous nonlinear dynamical systems
with applications", Submitted [technical report:
pdf,
arXiv:0906.3501v1]