norm_w_to_n

<code>norm_w_to_n</code> normalizes weights either to N or to N in treated and controls separately.

internal

Provides tools for cross-validated Lasso and Post-Lasso estimation.
Built on top of the 'glmnet' package by Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01>,
the main function plasso() extends the standard 'glmnet' output with coefficient paths
for Post-Lasso models, while cv.plasso() performs cross-validation for both Lasso
and Post-Lasso models and different ways to select the penalty parameter lambda as discussed in Knaus (2021) <doi:10.1111/rssa.12623>.

Knaus Michael C.

plasso

Cross-Validated Post-Lasso

Michael C. Knaus

Stefan Glaisner

norm_w_to_n function

<dl><dt>w</dt>
<dd>Vector or n x 1 matrix of weights that should be normalized</dd>
<dt>d</dt>
<dd>Vector of treatment indicators</dd></dl>

Arguments

Normalization of sample weights for potential sample weights — norm_w_to_n

<dl>

<dt>w</dt>
<dd>Vector or n x 1 matrix of weights that should be normalized</dd>


<dt>d</dt>
<dd>Vector of treatment indicators</dd>

</dl>

norm_w_to_n: Normalization of sample weights for potential sample weights

Description

Usage

Value

Arguments