relevents

A time-ordered sequence of e-mail messages between employees of a 
consultancy firm and information on the actors in the relational event 
sequence. The data is orignally analyzed by Mulder &amp; Leenders (2019), to 
find drivers of innovation-related e-mail messages exchanged between 
employees of a large consultancy firm. Originally, the data consist of 2081
e-mail messages exchanged between 70 employees over the course o fa year. 
The current data is a sample of a simulated data set, based on estimates of 
the model parameters in Mulder &amp; Leenders (2019).

datasets

Implementation of various default Bayes factors
for testing statistical hypotheses. 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,
generalized linear models, special cases of
linear mixed models, survival models, relational
event models. Parameters that can be tested are
location parameters (e.g., regression coefficients),
variances (e.g., group variances), and measures of
association (e.g,. bivariate correlations).
The statistical underpinnings are
described in
Mulder, Hoijtink, and Xin (2019) <arXiv:1904.00679>,
Mulder and Gelissen (2019) <arXiv:1807.05819>,
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 <DOI:10.1037/met0000116>,
Hoijtink, Mulder, van Lissa, and Gu, (2018) <DOI:10.31234/osf.io/v3shc>,
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>.

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 (227 rows, 3 columns)<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

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

References