leaf_stats.default

A default leaf_stats for forests classes without a leaf_stats method
that always returns NULL.

internal

Forest-based statistical estimation and inference.
GRF provides non-parametric methods for heterogeneous treatment effects estimation
(optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables),
as well as least-squares regression, quantile regression, and survival regression,
all with support for missing covariates.

Erik Sverdrup

Generalized Random Forests

Julie Tibshirani

Susan Athey

Rina Friedberg

Vitor Hadad

David Hirshberg

Luke Miner

Stefan Wager

Marvin Wright

leaf_stats.default function

<dl><dt>forest</dt>
<dd>Any forest</dd>
<dt>samples</dt>
<dd>The samples to include in the calculations.</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

A default leaf_stats for forests classes without a leaf_stats method
that always returns NULL. — leaf_stats.default

<dl>

<dt>forest</dt>
<dd>Any forest</dd>


<dt>samples</dt>
<dd>The samples to include in the calculations.</dd>


<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd>

</dl>

A default leaf_stats for forests classes without a leaf_stats method
that always returns NULL.

leaf_stats.default: A default leaf_stats for forests classes without a leaf_stats method that always returns NULL.

Description

Usage

Arguments