x*********************************
* *
* Changes in islasso *
* *
*********************************
===============
version 1.4.2
===============
* Some bugs for binomial family fixed.
===============
version 1.4.1
===============
* Some bugs fixed.
===============
version 1.4.0
===============
* New optimization algorithm for the 'islasso' method. The algorithm is now stable for all the implemented distributions.
* In aic.islasso function the available methods are "AIC", "BIC", "AICc", "eBIC", "GCV", "GIC".
* New class of functions named 'islasso.path' have been created. The main function 'islasso.path()' allows to build the coefficient profile for a fixed sequence of lambda values.
* A new function GoF.islasso.path() allows to extract the optimal value of the tuning parameter which minimizes a fixed criterion. Available criteria are the same as in aic.islasso function.
* Some bugs fixed.
===============
version 1.3.1
===============
* Some bugs fixed.
===============
version 1.3.0
===============
* Vignette added to the package 'islasso'
* Some bugs fixed.
===============
version 1.2.3
===============
* Some bugs fixed.
===============
version 1.2.2
===============
* Some bugs fixed.
===============
version 1.2.1
===============
* Some bugs fixed.
===============
version 1.2.0
===============
* New implementation of the estimating algorithm. Now islasso is much stabler and faster.
* New function: general linear hypotheses for linear combinations of the regression coefficients, including confidence intervals.
* New changes:
- prediction function includes confidence intervals for the fitted values
- step Halving with Armijo's rule has been improved.
- convergence criterion has been improved
* Some bugs fixed.
===============
version 1.1.0
===============
* New implementation of the estimating algorithm. Now islasso is much stabler and faster reducing the number of iterations to reach convergence.
* New changes:
- step Halving with Armijo's rule has been implemented.
- the algorithm includes the possibility to use the elastic-net approach defining an alpha parameter in the objective function as in glmnet package.
- the summary method includes now the degree of freedom for each covariate, and in addition it is possible to choose between t-test or z-test (only for gaussian family).
- optim.islasso has been renamed as aic.islasso the interval specification is not required now, the new select the best tuning parameter based on the minimization of the AIC or the BIC.
- the function islasso.control has been renamed as is.control and some control parameters have been modified.
- two trace version has been implemented in the function is.control a compact version (trace=1) and a long version (trace=2).
* Some bugs fixed.