Cox model regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty. In addition, it efficiently solves an approximate L0 variable selection based on truncated likelihood function. Moreover, it can also handle the adaptive version of these regularization forms, such as adaptive lasso and net adjusting for signs of linked coefficients. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.

Cox model regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty, and their adaptive forms, such as adaptive lasso and net adjusting for signs of linked coefficients. Moreover, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter \code{lambda}. In addition, it fits a varying coefficient Cox model by kernel smoothing, incorporated with the aforementioned penalties. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.

Change Point Detection in Dynamic Graphs
Marco Corneli, Pierre Latouche, Fabrice Rossi, Margot Selosse
Implementation of a model-based approach for clustering and multiple change points in dynamic graphs (Marco Corneli, Pierre Latouche and Fabrice Rossi (2018) <doi.org/10.1007/s11222-017-9775-1>).
Rcpp (>= 0.12.17), mclust, RColorBrewer, igraph

CpG Filtering Method Based on Intra-class Correlation
Coefficients
Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.

CpG Filtering Method Based on Intra-Class Correlation
Coefficients

Association between Methylation and a phenotype of interest
CpGassoc is designed to test for association between
methylation at CpG sites across the genome and a phenotype of
interest, adjusting for any relevant covariates. The package
can perform standard analyses of large datasets very quickly
with no need to impute the data. It can also handle mixed
effects models with chip or batch entering the model as a
random intercept.  CpGassoc also includes tools to apply
quality control filters, perform permutation tests, and create
QQ plots, manhattan plots, and scatterplots for individual CpG
sites.