- 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, Wang, Zhou and Zhu (2009).
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 and Paul
(2009). A geometric approach to maximum likelihood estimation of the
functional principal components from sparse longitudinal data, Journal of
Computational and Graphical Statistics, 18(4): 995 - 1015 [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, Zhu, Bergamaschi, Han, Noh, Pollack and Wang (2010)
Regularized Multivariate Regression for Identifying Master Predictors with
Application to Integrative Genomics Study of Breast Cancer, The Annals
of Applied Statistics, 4
(1): 53-77 [technical report: pdf; arXiv:0812.3671v1].
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. dynamics 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, Peng and Burman (2011) Semiparametric
modeling of autonomous nonlinear dynamical systems with applications, The Annals of Applied Statistics, 5(3): 2078-2108 [technical report: pdf,
- BINCO: BINCO
is an R package to calculate the optimal threshold of selection
frequencies for variable/feature selection through directly controlling
the false discovery rate. It can be downloaded from http://cran.r-project.org/.
is useful for determining the amount of regularization in high-dimension
regularization problems, particularly, unsupervised learning such as
network structure learning. For more details, see the paper
: Li, Hsu, Peng and Wang (2013).
Bootstrap inference for network construction, The Annals
of Applied Statistics, 7(1): 391-417. [Full Text].
- dagbag: dagbag is an R package to conduct DAG learning
by hill climbing algorithm and bootstrap aggregating. It can be downloaded from
http://cran.r-project.org/. dagbag is useful for
high-dimensional directed acyclic graph (DAG) model learning. For more
details, see the paper: R. Wang and J. Peng. Learning directed
acyclic graphs via bootstrap aggregating (2014). [arXiv:1406.2098]
- spaceMap: spaceMap is an
integrative –omics analysis pipeline for
constructing regulatory networks and conducting network analysis. It is
maintained on https://topherconley.github.io/spacemap/. spaceMap is useful for high-dimensional
conditional graphical model learning. For more details, see the paper: Conley C., Umut Ozbek, Wang P., and J. Peng. Characterizing functional
consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap
(2017). [bioRxiv: 10.1101/248229]
- PLNet: PLNet is an R package to infer graphical models
and build regulatory networks based on RNA-Seq
data. It can be downloaded from https://github.com/jie108/PLNet/. For more details, see the paper: Choi Y., Coram M., J. Peng, and Tang H. A Poisson Log-Normal model for
constructing gene covariation network using RNA-seq data (2017).
Journal of Computational Biology, 24(7):721-731.