print.tna_bootstrap

Provides tools for performing Transition Network Analysis (TNA) to
study relational dynamics, including functions for building and plotting TNA
models, calculating centrality measures, and identifying dominant events and
patterns. TNA statistical techniques (e.g., bootstrapping and permutation
tests) ensure the reliability of observed insights and confirm that
identified dynamics are meaningful. See (Saqr et al., 2025)
<doi:10.1145/3706468.3706513> for more details on TNA.

Sonsoles López-Pernas

Transition Network Analysis (TNA)

print.tna_bootstrap function

<dl><dt>x</dt>
<dd>A <code>tna_bootstrap</code> object.</dd>
<dt>digits</dt>
<dd>An <code>integer</code> giving the minimal number of
significant digits to print.</dd>
<dt>type</dt>
<dd>A <code>character</code> vector giving the type of edges to print.
The default option <code>"both"</code> prints both statistically significant and
non-significant edges, <code>"sig"</code> prints only significant edges, and <code>"nonsig"</code>
prints only the non-significant edges.</dd>
<dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

Print Bootstrap Results — print.tna_bootstrap

<dl>

<dt>x</dt>
<dd>A <code>tna_bootstrap</code> object.</dd>


<dt>digits</dt>
<dd>An <code>integer</code> giving the minimal number of
significant digits to print.</dd>


<dt>type</dt>
<dd>A <code>character</code> vector giving the type of edges to print.
The default option <code>"both"</code> prints both statistically significant and
non-significant edges, <code>"sig"</code> prints only significant edges, and <code>"nonsig"</code>
prints only the non-significant edges.</dd>


<dt>...</dt>
<dd>Ignored.</dd>

</dl>

print.tna_bootstrap: Print Bootstrap Results

Description

Usage

Value

Arguments

Examples