Network-based regularization has achieved success in variable selection for
high-dimensional biological data due to its ability to incorporate correlations among
genomic features. This package provides procedures of network-based variable selection
for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and
Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response
are supported. Robust network-based methods are available for continuous and survival
responses.

Automatic Time Series Forecasting and Missing Value Imputation
c(
person("Davide", "Altomare", email = "info@channelattribution.net", role = c("cre", "aut")),
person("David", "Loris", email = "david.loris@gmail.com", role = "aut")
)
Machine learning algorithm for predicting and imputing time series. It can automatically set all the parameters needed, thus in the minimal configuration it only requires the target variable and the dependent variables if present. It can address large problems with hundreds or thousands of dependent variables and problems in which the number of dependent variables is greater than the number of observations. Moreover it can be used not only for time series but also for any other real valued target variable. The algorithm implemented includes a Bayesian stochastic search methodology for model selection and a robust estimation based on bootstrapping. 'rego' is fast because all the code is C++.

person("Roger", "Marshall", role = c("aut", "cre"),
email = "rj.marshall@auckland.ac.nz")
A function to plot a regression nomogram of coxph, lm and glm regression
objects. Covariate distributions are superimposed on nomogram scales and the plot
is animated to allow on the fly changes to distribution representation and to
enable outcome calculation.
vioplot, sm, beanplot, survival, graphics, stats

A function to plot a regression nomogram of regression
objects. Covariate distributions are superimposed on nomogram scales and the plot
can be animated to allow on-the-fly changes to distribution representation and to
enable outcome calculation.
vioplot, sm, beanplot, survival, graphics, stats, lme4