**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**

**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",*Annals of Applied Statistics,***4 (1): 53-77**

**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,**Peng**and Burman (2011) Semiparametric modeling of autonomous nonlinear dynamical systems with applications",*Annals of Applied Statistics*, 5(3): 2078-2108

**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", 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(2014) Learning directed acyclic graphs via bootstrap aggregating", http://arxiv.org/abs/1406.2098