Title: Data Manipulation Extensions of 'Dplyr' and 'Tidyr'

Title: Additional Families for Generalized Linear Models
Description: Creates family objects identical to stats
Imports: stats (>= 3.5.3), assertthat (>= 0.2.1)

Imports: stats (>= 3.5.3), assertthat (>= 0.2.1), numDeriv (>=

Title: Constructs the Exterior Match from Two Matched Control Groups
Description: If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups.  The exterior match is used to compare the two control groups.

Title: Additional Funnel Plot Augmentations
Author: Dean Langan, Alex Sutton, Julian Higgins, Walter Gregory
Description: This is a package containing the function extfunnel()
which produces a funnel plot including additional augmentations
such as the statistical significance contours and the

Author: Dean Langan, Alexander Sutton, Julian PT Higgins, Walter
such as statistical significance contours and heterogeneity

Title: Maximum penalized likelihood estimation with extended lasso
Author: B N Mandal <mandal.stat@gmail.com> and Jun Ma <jun.ma@mq.edu.au>
Maintainer: B N Mandal <mandal.stat@gmail.com>
Description: 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.

Title: Maximum Penalized Likelihood Estimation with Extended Lasso
Description: 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.