Changes in version 1.1.9 (2023-09-10)                  

  - Remove vignette dependency on an external download.

                 Changes in version 1.1.8 (2021-02-25)                  

  - Skip some tests on 32-bit Solaris R-patched due to numerical
    convergence issues.

                 Changes in version 1.1.7 (2021-02-23)                  

  - Add some plotting options for plot.cva.glmnet: log.x controls
    whether to plot the X-axis (lambda) on the log scale, and the legend
    can be omitted by setting either legend.x or legend.y to NULL.
  - Compatibility fixes for glmnet 4.1-1.

                 Changes in version 1.1.6 (2020-10-06)                  

  - Update maintainer email address.

                 Changes in version 1.1.5 (2020-03-12)                  

  - Fix handling of non-factor categorical predictors (from R 4.0, data
    frames will not have character columns converted to factors by
    default). The practical impact of this should be minor.

                 Changes in version 1.1.4 (2019-12-12)                  

  - Fix printout of glmnet.formula object.

                 Changes in version 1.1.3 (2019-11-18)                  

  - Support relaxed (non-regularised) fits in glmnet.formula and
    cv.glmnet.formula (requires glmnet 3.0 or later).
  - Add a legend when plotting a cva.glmnet object.

                 Changes in version 1.1.2 (2019-03-06)                  

  - Fixes a bug in the assignment of observations to crossvalidation
    folds in cva.glmnet. The impact is most serious for small datasets,
    where the number of observations per fold is relatively low. If you
    are using this function, it's highly recommended you update the
    package.

                 Changes in version 1.1.1 (2019-01-17)                  

  - Fixes bug where nfolds argument was not being passed to
    glmnet::cv.glmnet.

                  Changes in version 1.1 (2017-08-01)                   

  - Now allows interaction and expression terms without requiring
    use.model.frame=TRUE. This works in an additive fashion, ie the
    formula ~ a + b:c + d*e is treated as consisting of three terms, a,
    b:c and d*e each of which is processed independently of the others.
    A dot in the formula includes all main effect terms, ie ~ . + a:b +
    f(x) expands to ~ a + b + x + a:b + f(x) (assuming a, b and x are
    the only columns in the data). Note that a formula like ~ (a + b) +
    (c + d) will be treated as two terms, a + b and c + d.
  - The call component of a glmnet/cv.glmnet object that uses the
    original matrix/vector interface is now useful.
  - You can now explicitly specify the vector of crossvalidation folds
    (for the inner loop over lambda) when calling cva.glmnet.
  - Correctly handle non-syntactic factor variables in a formula.

                 Changes in version 1.0.2 (2017-04-24)                  

  - Initial release to CRAN.