**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**, 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***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**: 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, arXiv:0906.3501v1]

**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/.*It 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. [pdf]