importance_weights

<a href="https://lifecycle.r-lib.org/articles/stages.html#experimental"><img src="figures/lifecycle-experimental.svg?package=hardhat&version=1.3.1" alt="[Experimental]"></a>
<code>importance_weights()</code> creates a vector of importance weights which allow you
to apply a context dependent weight to your observations. Importance weights
are supplied as a non-negative double vector, where fractional values are
allowed.

Building modeling packages is hard. A large amount of effort
generally goes into providing an implementation for a new method that
is efficient, fast, and correct, but often less emphasis is put on the
user interface. A good interface requires specialized knowledge about
S3 methods and formulas, which the average package developer might not
have. The goal of 'hardhat' is to reduce the burden around building
new modeling packages by providing functionality for preprocessing,
predicting, and validating input.

Davis Vaughan

hardhat

Construct Modeling Packages

Max Kuhn

Posit Software, PBC 

importance_weights function

<dl><dt>x</dt>
<dd>A double vector.</dd></dl>

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
<code>importance_weights()</code> creates a vector of importance weights which allow you
to apply a context dependent weight to your observations. Importance weights
are supplied as a non-negative double vector, where fractional values are
allowed.

Importance weights — importance_weights

<dl>

<dt>x</dt>
<dd>A double vector.</dd>

</dl>

importance_weights: Importance weights

Description

Usage

Value

Arguments

Details

See Also

Examples