robust_summary

Computes mean, standard deviation, median, MAD, trimmed mean, and
bootstrap confidence intervals for a numeric vector.

Provides tools for analyzing the relationship between direct
prices (based on labor values) and prices of production using Bayesian
generalized linear models, panel data methods, partial least squares
regression, canonical correlation analysis, and panel vector
autoregression. Includes functions for model comparison, out-of-sample
validation, and structural break detection. Here, methods use raw accounting data with explicit temporal structure, following Gomez Julian (2023) <doi:10.17605/OSF.IO/7J8KF>
and standard econometric techniques for panel data analysis.

José Mauricio Gómez Julián

valueprhr

Value-Price Analysis with Bayesian and Panel Data Methods

Jose Mauricio Gomez Julian

robust_summary function

<dl><dt>x</dt>
<dd>Numeric vector.</dd>
<dt>bootstrap_reps</dt>
<dd>Number of bootstrap replications for CI. Default 200.</dd>
<dt>trim_proportion</dt>
<dd>Proportion to trim for trimmed mean. Default 0.10.</dd></dl>

Arguments

Robust Summary Statistics with Bootstrap Confidence Intervals — robust_summary

<dl>

<dt>x</dt>
<dd>Numeric vector.</dd>


<dt>bootstrap_reps</dt>
<dd>Number of bootstrap replications for CI. Default 200.</dd>


<dt>trim_proportion</dt>
<dd>Proportion to trim for trimmed mean. Default 0.10.</dd>

</dl>

robust_summary: Robust Summary Statistics with Bootstrap Confidence Intervals

Description

Usage

Value

Arguments

Examples