CRAN release: 2019-05-17
- Added detailed signal-to-noise ratio (SNR) definition in
- Updated the example code in the vignette to make it work better with the most recent version of glmnet (2.0-16).
- Updated GitHub repository links due to the handle change.
- Updated the vignette style.
CRAN release: 2018-12-14
- Added a new argument
penalty.factor.initto support customized penalty factor applied to each coefficient in the initial estimation step. This is useful for incorporating prior information about variable weights, for example, emphasizing specific clinical variables. We thank Xin Wang from University of Michigan for this feedback [#4].
CRAN release: 2018-05-14
- New URL for the documentation website: https://nanx.me/msaenet/.
CRAN release: 2018-01-05
- Added a Cleveland dot plot option
type = "dotplot"in
plot.msaenet(). This plot offers a direct visualization of the model coefficients at the optimal step.
CRAN release: 2017-09-24
CRAN release: 2017-04-24
CRAN release: 2017-03-25
CRAN release: 2017-02-18
- Improved graphical details for coefficient path plots, following the general graphic style in the ESL (The Elements of Statistical Learning) book.
- More options available in
plot.msaenet()for extra flexibility: it is now possible to set important properties of the label appearance such as position, offset, font size, and axis titles via the new arguments
CRAN release: 2017-02-10
- Reduced model saturation cases and improved speed at the initialization step for MCP-net and SCAD-net based models when
init = "ridge", by using the ridge estimation implementation from
glmnet. As a benefit, we now have a more aligned baseline for the comparison between elastic-net based models and MCP-net/SCAD-net based models when
init = "ridge".
- Style improvements in code and examples: reduced whitespace with a new formatting scheme.
CRAN release: 2017-02-02
- Added BIC, EBIC, and AIC in addition to k-fold cross-validation for model selection.
- Added new arguments
tune.nstepsto controls this for selecting the optimal model for each step, and the optimal model among all steps (i.e. the optimal step).
- Added arguments
ebic.gamma.nstepsto control the EBIC tuning parameter, if
ebicis specified by
- Redesigned plot function: now supports two types of plots (coefficient path, screeplot of the optimal step selection criterion), optimal step highlighting, variable labeling, and color palette customization. See
- Renamed previous argument
gamma(scaling factor for adaptive weights) to
scaleto avoid possible confusion.
- Reset the default values of candidate concavity parameter
gammasto be 3.7 for SCAD-net and 3 for MCP-net.
- Unified the supported model
familyin all model types to be
CRAN release: 2017-01-15
- Added functions
msaenet.sim.cox()to generate simulation data for logistic, Poisson, and Cox regression models.
- Added function
msaenet.fn()for computing the number of false negative selections in msaenet models.
- Added function
msaenet.mse()for computing mean squared error (MSE).
- Speed improvements in
msaenet.sim.gaussian()by more vectorization when generating correlation matrices.
- Added parameters
epsilonfor MCP-net and SCAD-net related functions to have finer control over convergence criterion. By default,
max.iter = 10000and
epsilon = 1e-4.
CRAN release: 2017-01-05
- Added support for adaptive MCP-net. See
- Added support for adaptive SCAD-net. See
- Added support for multi-step adaptive MCP-net (MSAMNet). See
- Added support for multi-step adaptive SCAD-net (MSASNet). See
msaenet.nzv.all()for displaying the indices of non-zero variables in all adaptive estimation steps.
CRAN release: 2016-12-29
- Added method
coeffor extracting model coefficients. See