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LazyData.msaenet.sim.gaussian().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).type = "dotplot" in plot.msaenet(). This plot offers a direct visualization of the model coefficients at the optimal step.init = "ridge".lower.limits and upper.limits to support coefficient constraints in aenet() and msaenet() (#1).README.md.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.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".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).ebic.gamma and ebic.gamma.nsteps to control the EBIC tuning parameter, if ebic is specified by tune or tune.nsteps.?plot.msaenet for details.gamma (scaling factor for adaptive weights) to scale to avoid possible confusion.gammas to be 3.7 for SCAD-net and 3 for MCP-net.family in all model types to be "gaussian", "binomial", "poisson", and "cox".msaenet.sim.binomial(), msaenet.sim.poisson(), msaenet.sim.cox() to generate simulation data for logistic, Poisson, and Cox regression models.msaenet.fn() for computing the number of false negative selections in msaenet models.msaenet.mse() for computing mean squared error (MSE).msaenet.sim.gaussian() by more vectorization when generating correlation matrices.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.amnet() to support adaptive MCP-net.asnet() to support adaptive SCAD-net.msamnet() to support multi-step adaptive MCP-net.msasnet() to support for multi-step adaptive SCAD-net.msaenet.nzv.all() for displaying the indices of non-zero variables in all adaptive estimation steps.predict.msaenet method allowing users to specify prediction type.coef for extracting model coefficients. See ?coef.msaenet for details.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.
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