summarise_placebo_top3_posthoc

Summarises the three strongest temporal placebo results (based on
the difference between original and permuted ELPD) from a temporal
permutation test.

Provides tools for fitting bivariate hurdle negative binomial
models with horseshoe priors, Bayesian Model Averaging (BMA) via stacking,
and comprehensive causal inference methods including G-computation,
transfer entropy, Threshold Vector Autoregressive (TVAR) and Smooth
Transition Autoregressive (STAR) models, Dynamic Bayesian Networks (DBN),
Hidden Markov Models (HMM), and sensitivity analysis.

José Mauricio Gómez Julián

bivarhr

Bivariate Hurdle Regression with Bayesian Model Averaging

summarise_placebo_top3_posthoc function

<dl><dt>placebo_tab</dt>
<dd>Optional data frame with placebo results, typically
containing columns <code>perm</code>, <code>elpd_orig</code>, <code>elpd_perm</code>,
and <code>diff</code>. If <code>NULL</code> or empty, the function attempts to
read <code>"placebo_temporal.csv"</code> from <code>dir_csv</code>.</dd>
<dt>dir_csv</dt>
<dd>Character scalar; directory where the placebo CSV file
is stored.</dd></dl>

Arguments

Summarise top-3 temporal placebo results — summarise_placebo_top3_posthoc

<dl>

<dt>placebo_tab</dt>
<dd>Optional data frame with placebo results, typically
containing columns <code>perm</code>, <code>elpd_orig</code>, <code>elpd_perm</code>,
and <code>diff</code>. If <code>NULL</code> or empty, the function attempts to
read <code>"placebo_temporal.csv"</code> from <code>dir_csv</code>.</dd>


<dt>dir_csv</dt>
<dd>Character scalar; directory where the placebo CSV file
is stored.</dd>

</dl>

summarise_placebo_top3_posthoc: Summarise top-3 temporal placebo results

Description

Usage

Value

Arguments

Details