Maximum penalized likelihood estimation with extended lasso
penalty
B N Mandal <mandal.stat@gmail.com> and Jun Ma <jun.ma@mq.edu.au>
The package estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. The package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. The package also contains functions for cross validation to select a suitable lambda value given the data. The package also provides a function for estimation in fused lasso penalized linear regression.

Maximum Penalized Likelihood Estimation with Extended Lasso
Penalty
Estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. This package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. This package also contains functions for cross validation to select a suitable lambda value given the data. Also provides a function for estimation in fused lasso penalized linear regression. For more details, see Mandal, B. N.(2014). Computational methods for L1 penalized GLM model fitting, unpublished report submitted to Macquarie University, NSW, Australia.

Extra-binomial approach for pooled sequencing data
This package tests for differences in minor allele
frequency between groups and is based on an extra-binomial
variation model for pooled sequencing data. A set of simulated
pooled sequencing data can be generated using this package.

This package tests for differences in minor allele
frequency between groups and is based on an extra-binomial
variation model for pooled sequencing data.