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msaenet 3.1.2

Improvements

• The coefficient profile plot now has a new default color palette (new Tableau 10). The updated palette offers a more refined and visually appealing look, while also improving accessibility for users with color-vision deficiencies. The color palette is consistency applied across multiple graphical elements in all plot types (#13).
• Added a note in the vignette about possible graphical parameters for labeling the selected variables supported by the plotting methods (thanks, @xingxingyanjing, #12).
• Simplified and optimized vignette and readme plotting chunk options (#14).
• Fixed typos and improved text style in documentation (#14).

msaenet 3.1.1

Improvements

• Used a proper, three-component version number following Semantic Versioning.
• Fixed warnings about single lambda (#11).
• Fixed “lost braces” check notes on r-devel and check notes about LazyData.
• Fixed code linting issues.
• Used GitHub Actions to build the pkgdown site.

msaenet 3.1

Improvements

• Added detailed signal-to-noise ratio (SNR) definition in msaenet.sim.gaussian().
• 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.

msaenet 3.0

New features

• Added a new argument penalty.factor.init to 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).

msaenet 2.9

Improvements

• New URL for the documentation website: https://nanx.me/msaenet/.

msaenet 2.8

New features

• 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.

msaenet 2.7

Bug fixes

• Fixed the missing arguments issue when init = "ridge".

msaenet 2.6

Improvements

• Added two arguments lower.limits and upper.limits to support coefficient constraints in aenet() and msaenet() (#1).

msaenet 2.5

Improvements

• Better code indentation style.
• Update gallery images in README.md.

msaenet 2.4

Improvements

• 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 label.pos, label.offset, label.cex, xlab, and ylab.

msaenet 2.3

Improvements

• 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.

msaenet 2.2

New features

• Added BIC, EBIC, and AIC in addition to k-fold cross-validation for model selection.
• Added new arguments tune and tune.nsteps to 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 and ebic.gamma.nsteps to control the EBIC tuning parameter, if ebic is specified by tune or tune.nsteps.
• 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 ?plot.msaenet for details.

Improvements

• Renamed previous argument gamma (scaling factor for adaptive weights) to scale to avoid possible confusion.
• Reset the default values of candidate concavity parameter gammas to be 3.7 for SCAD-net and 3 for MCP-net.
• Unified the supported model family in all model types to be "gaussian", "binomial", "poisson", and "cox".

msaenet 2.1

New features

• Added functions msaenet.sim.binomial(), msaenet.sim.poisson(), 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).

Improvements

• Speed improvements in msaenet.sim.gaussian() by more vectorization when generating correlation matrices.
• Added parameters max.iter and epsilon for MCP-net and SCAD-net related functions to have finer control over convergence criterion. By default, max.iter = 10000 and epsilon = 1e-4.

msaenet 2.0

New features

• Added amnet() to support adaptive MCP-net.
• Added asnet() to support adaptive SCAD-net.
• Added msamnet() to support multi-step adaptive MCP-net.
• Added msasnet() to support for multi-step adaptive SCAD-net.
• Added msaenet.nzv.all() for displaying the indices of non-zero variables in all adaptive estimation steps.

Improvements

• More flexible predict.msaenet method allowing users to specify prediction type.

msaenet 1.1

New features

• Added method coef for extracting model coefficients. See ?coef.msaenet for details.

Improvements

• New documentation website generated by pkgdown, with a full set of function documentation and vignettes available.
• Added Windows continuous integration support using AppVeyor.

msaenet 1.0

New features

• Initial version of the msaenet package.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.