stan

Standardize variables. For each column, return mean 0 and mean value of sum of squares = 1.

Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>,
Wang et al. (2015) <doi:10.1002/bimj.201400143>,
Wang et al. (2016) <doi:10.1177/0962280214530608>,
Wang (2021) <doi:10.1007/s11749-021-00770-2>,
Wang (2024) <doi:10.1111/anzs.12409>.

Zhu Wang

mpath

Regularized Linear Models

stan function

<dl> <dt>x</dt>
<dd>numeric variables, can be a matrix or vector</dd> <dt>weights</dt>
<dd>numeric positive vector of weights</dd></dl>

Arguments

Zhu Wang &lt;zwang145@uthsc.edu&gt;

Author

standardize variables — stan

<dl>

 <dt>x</dt>
<dd>numeric variables, can be a matrix or vector</dd>

 <dt>weights</dt>
<dd>numeric positive vector of weights</dd>

</dl>

stan: standardize variables

Description

Usage

Value

Arguments

Author