relevents

A time-ordered sequence of 247 communication messages between 25 actors.

datasets

Implementation of default Bayes factors
for testing statistical hypotheses under various statistical models. The package is
intended for applied quantitative researchers in the
social and behavioral sciences, medical research,
and related fields. The Bayes factor tests can be
executed for statistical models such as
univariate and multivariate normal linear models,
correlation analysis, generalized linear models, special cases of
linear mixed models, survival models, relational
event models. Parameters that can be tested are
location parameters (e.g., group means, regression coefficients),
variances (e.g., group variances), and measures of
association (e.g,. polychoric/polyserial/biserial/tetrachoric/product
moments correlations), among others.
The statistical underpinnings are
described in
O'Hagan (1995) <DOI:10.1111/j.2517-6161.1995.tb02017.x>,
De Santis and Spezzaferri (2001) <DOI:10.1016/S0378-3758(00)00240-8>,
Mulder and Xin (2022) <DOI:10.1080/00273171.2021.1904809>,
Mulder and Gelissen (2019) <DOI:10.1080/02664763.2021.1992360>,
Mulder (2016) <DOI:10.1016/j.jmp.2014.09.004>,
Mulder and Fox (2019) <DOI:10.1214/18-BA1115>,
Mulder and Fox (2013) <DOI:10.1007/s11222-011-9295-3>,
Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) <DOI:10.1037/met0000116>,
Hoijtink, Mulder, van Lissa, and Gu (2018) <DOI:10.1037/met0000201>,
Gu, Mulder, and Hoijtink (2018) <DOI:10.1111/bmsp.12110>,
Hoijtink, Gu, and Mulder (2018) <DOI:10.1111/bmsp.12145>, and
Hoijtink, Gu, Mulder, and Rosseel (2018) <DOI:10.1037/met0000187>. When using the
packages, please refer to the package Mulder et al. (2021) <DOI:10.18637/jss.v100.i18>
and the relevant methodological papers.

Joris Mulder

BFpack

Flexible Bayes Factor Testing of Scientific Expectations

Caspar van Lissa

Donald R. Williams

Xin Gu

Anton Olsson-Collentine

Florian Boeing-Messing

Jean-Paul Fox

Janosch Menke

Robbie van Aert

Barry Brown

James Lovato

Kathy Russell

Lapack 3.8 

Jack Dongarra

Jim Bunch

Cleve Moler

Gilbert Stewart

John Burkandt

Ashwith Rego

Alexander Godunov

Alan Miller

Jean-Pierre Moreau

The R Core Team 

relevents function

dataframe (247 rows, 3 columns)
<table class="table">
<tr><td>relevents$time</td><td><code>numeric</code></td><td>Time of the e-mail message,
 in seconds since onset of the observation</td></tr>
<tr><td>relevents$sender</td><td><code>integer</code></td><td>ID of the sender, corresponding to
 the employee IDs in the actors dataframe</td></tr>
<tr><td>relevents$receiver</td><td><code>integer</code></td><td>ID of the receiver</td></tr>
</table>

Format

A sequence of innovation-related e-mail messages — relevents

dataframe (247 rows, 3 columns)
<table class='table'>
<tr><td>relevents$time</td><td><code>numeric</code></td><td>Time of the e-mail message,
 in seconds since onset of the observation</td></tr>
<tr><td>relevents$sender</td><td><code>integer</code></td><td>ID of the sender, corresponding to
 the employee IDs in the actors dataframe</td></tr>
<tr><td>relevents$receiver</td><td><code>integer</code></td><td>ID of the receiver</td></tr>
</table>

A sequence of innovation-related e-mail messages

relevents$time	`numeric`	Time of the e-mail message, in seconds since onset of the observation
relevents$sender	`integer`	ID of the sender, corresponding to the employee IDs in the actors dataframe
relevents$receiver	`integer`	ID of the receiver

relevents: A sequence of innovation-related e-mail messages

Description

Usage

Arguments

Format

Details