The R package abn
is a tool for Bayesian network analysis, a form of probabilistic graphical model. It derives a directed acyclic graph (DAG) from empirical data that describes the dependency structure between random variables. The package provides routines for structure learning and parameter estimation of additive Bayesian network models.
Installation
abn
and its installation process relies on various software that might, or might not, be present in your system.
Prior to installing
In order for abn
to work correctly on your system some dependencies need to be installed. If you are on a Linux based system (most of) these dependencies are installed automatically for you when following the pakbased installation procedure described in the Installing from GitHub section.
For MacOS and Windows based system some more preparatory steps are required.
The following paragraphs provide detailed instructions for the most common operating systems on the steps that need to be carried out prior to installing abn
.
Ubuntu
You presumably have R installed already, if not, open a terminal and type:
Note: You might need to prepend sudo
to this command.
All you need for the installation is to have the Rpackage pak installed. pak
is installed like any other Rpackage, however, it relies on curl
being present on your system, so we make sure it is there:
Now, to install pak
we start an R session and write:
install.packages('pak', repos=c(CRAN="https://cran.rproject.org"))
With that you should be ready to install abn
from GitHub.
Fedora
You presumably have R installed already, if not, open a terminal and type:
Note: You might need to prepend sudo
to this command.
For the installation you need to have the Rpackage pak installed. pak
is installed like any other Rpackage, however, it relies on curl
being installed on your system, so we make sure it is there:
Now, to install pak
we start an R session and write:
install.packages('pak', repos=c(CRAN="https://cran.rproject.org"))
There is one more thing we need to do before we can install abn
:
Install JAGS from source
JAGS, Just Another Gibbs Sampler, is a program for analyzing Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation. rjags is R’s interface to the JAGS
library. JAGS
is required in some simulations abn
can perform.
The steps needed to install JAGS 4.3.2
are:
wget O /tmp/jags.tar.gz https://sourceforge.net/projects/mcmcjags/files/JAGS/4.x/Source/JAGS4.3.2.tar.gz/download
cd /tmp
tar xf jags.tar.gz
cd /tmp/JAGS4.3.2
./configure libdir=/usr/local/lib64
make
sudo make install
Note: If you are on a 64bit system (you likely are) mind the libdir=/usr/local/lib64
argument when launching ./configure
.) Omitting this argument will lead to rjags
“not seeing” jags
.
On Fedora rjags
might need some special configuration for it to link properly to the JAGS
library. Also, it might be needed to add the path to the JAGS
library to the linker path (see rjags INSTALL file for further details).
In order to add the JAGS
library to the linker path, run the following commands:
Note: These commands might not be needed, you might first try to install the Rpackage rjags
and only run them if you encounter a configure: error: Runtime link error
.
With that you should be ready to install abn
from GitHub.
MacOS
Most likely you have R installed already but if not run:
For the installation you need to have the Rpackage pak installed. pak
is installed like any other Rpackage, we start an R session and write:
install.packages('pak', repos=c(CRAN="https://cran.rproject.org"))
We will install the system dependencies with Homebrew. Head over to their site to see the installation process or simply open a terminal and run:
To correctly link to installed libraries and to build them, we need pkgconfig
and automake
:
We will use wget
to download JAGS
later, as well as, the development headers openssl
:
Dependencies
On MacOS we need to install some system dependencies separately:

GSL
GSL, the GNU Scientific Library, is a numerical library for C/C++. It is required to compile
abn
’s C/C++ code.With Homebrew you can install the
GSL
binaries directly:brew install gsl

JAGS & rjags
JAGS, Just Another Gibbs Sampler, is a program for analyzing Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation. rjags is R’s interface to the
JAGS
library.JAGS
is required in some simulationsabn
can perform.With Homebrew you can install the
JAGS
binaries directly:brew install jags
And now to install
rjags
, open an R session and type:install.packages("rjags", type="source", repos=c(CRAN="https://cran.rproject.org")) library("rjags")

INLA
INLA is an R package that is not hosted on CRAN and thus needs to be installed separately.
abn
usesINLA
to fit some models.INLA
relies on various other R packages and C/C++ libraries. It thus needs some additional installation steps:Now, to install
INLA
itself, simply start an R session and run:install.packages("INLA", repos = c(getOption("repos"), INLA = "https://inla.rinladownload.org/R/stable"), dep = TRUE)
If you run into trouble, please see also INLA’s installation instructions for further details.
Windows
For the installation you need to have the Rpackage pak installed. pak
is installed like any other Rpackage, we start an R session and write:
install.packages('pak', repos=c(CRAN="https://cran.rproject.org"))
Dependencies
On Windows we need to install some system dependencies separately:

GSL
GSL, the GNU Scientific Library, is a numerical library for C/C++. It is required to compile
abn
’s C/C++ code.In Windows
GSL
is available a.o. through cygwin, which has a straight forward installation process. Either head over to the website, download and install thesetupx86_64.exe
file or use PowerShell:ImportModule bitstransfer NewItem ItemType Directory Force Path "C:\Program Files\cygwin" startbitstransfer source https://cygwin.com/setupx86_64.exe "C:\Program Files\cygwin\setupx86_64.exe" StartProcess Wait FilePath "C:\Program Files\cygwin\setupx86_64.exe" ArgumentList "/S" PassThru

JAGS & rjags
JAGS, Just Another Gibbs Sampler, is a program for analyzing Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation. rjags is R’s interface to the
JAGS
library.JAGS
is required in some simulationsabn
can perform.You can either head over to the JAGS download page, download and execute the installable, or use PowerShell. The following instructions will download and install
JAGS 4.3.1
in PowerShell:ImportModule bitstransfer NewItem ItemType Directory Force Path "C:\Program Files\JAGS\JAGS4.3.1" startbitstransfer source https://sourceforge.net/projects/mcmcjags/files/JAGS/4.x/Windows/JAGS4.3.1.exe/download "C:\Program Files\JAGS\JAGS4.3.1\JAGS4.3.1.exe" StartProcess Wait FilePath "C:\Program Files\JAGS\JAGS4.3.1\JAGS4.3.1.exe" ArgumentList "/S" PassThru
In order to make sure
rjags
findsJAGS
we set the environment variableJAGS_HOME
before installingrjags
. To do so, open your R session and type:Sys.setenv(JAGS_HOME="C:/Program Files/JAGS/JAGS4.3.1") install.packages("rjags", repos=c(CRAN="https://cran.rproject.org")) library("rjags")

INLA
INLA is an R package that is not hosted on CRAN and thus needs to be installed separately.
abn
usesINLA
to fit some models.The installation is straight forward, simply start an R session and run:
install.packages("INLA", repos = c(getOption("repos"), INLA = "https://inla.rinladownload.org/R/stable"), dep = TRUE)
If you run into trouble, please see also INLA’s installation instructions for further details.
Click on your operating system to see the specific installation instructions
Installing from GitHub (recommended)
[!WARNING] Until the R package
ragg
releases version1.3.3
it might not build correctly. Sinceabn
depends onragg
the installation ofabn
might fail. Therefore, we recommend installing the developer version as a temporal fix:pak::pak("rlib/ragg")
Simply run this command before installing
abn
(see below) and you should be good to go.
From GitHub you can install any version and/or state of the abn
repository you want. We recommend to not directly install main
, but to a specific version. Head over to our version list to see which one is the latest version. Here we assume the version is 3.1.2
.
pak
should already be installed.
If not, install it first.
Open an R session and type:
install.packages('pak', repos=c(CRAN="https://cran.rproject.org"))
To install abn
run in your R session:
pak::repo_add(INLA = "https://inla.rinladownload.org/R/stable/")
pak::pkg_install("furrerlab/abn@3.1.2", dependencies=TRUE)
Note: The first command can be skipped on MacOS or Windows.
Installing from CRAN
[!NOTE] When installing from CRAN you might not get the latest version of
abn
. If you want the latest version follow the instructions from Installing from GitHub.
In order to install the abn
version on CRAN, open an R session and type:
pak::repo_add(INLA = "https://inla.rinladownload.org/R/stable/")
pak::pkg_install("abn", dependencies=TRUE)
Note: The first command can be skipped on MacOS or Windows.
abn
has several dependencies that are not available on CRAN. This is why we rely on pak for the installation and the Prior to installing section should be followed through before installing abn
from CRAN. ^{1}
Installing from source
It is also possible to clone this repository and install abn
from source.
[!NOTE] Also in this case you need to first prepare your system by following the Prior to installing section.
Installing from source is done with the following steps:

Clone the repository and go to the root directory of the repo:

Deactivate
abn
’s development environment (a renv virtual environment):renv::deactivate()

Build and install the local content with dependencies:
pak::repo_add(INLA = "https://inla.rinladownload.org/R/stable/") pak::local_install(dependencies=TRUE)
Note: The first command can be skipped on MacOS or Windows.
Quickstart
Explore the basics of data analysis using additive Bayesian networks with the abn
package through our simple example. The datasets required for these examples are included within the abn
package.
For a deeper understanding, refer to the manual pages on the abn
homepage, which include numerous examples. Key pages to visit are fitAbn()
, buildScoreCache()
, mostProbable()
, and searchHillClimber()
. Also, see the examples below for a quick overview of the package’s capabilities.
Features
The R package abn
provides routines for determining optimal additive Bayesian network models for a given data set. The core functionality is concerned with model selection  determining the most likely model of data from interdependent variables. The model selection process can incorporate expert knowledge by specifying structural constraints, such as which arcs are banned or retained.
The general workflow with abn
follows a threestep process:
Determine the model search space: The function
buildScoreCache()
builds a cache of precomputed scores for each possible DAG. For this, it’s required to specify the data types of the variables in the data set and the structural constraints of the model (e.g. which arcs are banned or retained and the maximum number of parents per node).
Structure learning:
abn
offers different structure learning algorithms: The exact structure learning algorithm from Koivisto and Sood (2004) is implemented in
C
and can be called with the functionmostProbable()
, which finds the most probable DAG for a given data set. The functionsearchHeuristic()
provides a set of heuristic search algorithms. These include the hillclimber, tabu search, and simulated annealing algorithms implemented inR
.searchHillClimber()
searches for highscoring DAGs using a random restart greedy hillclimber heuristic search and is implemented inC
. It slightly deviates from the method initially presented by Heckerman et al. 1995 (for details consult the respective help page?abn::searchHillClimber()
).
 The exact structure learning algorithm from Koivisto and Sood (2004) is implemented in
Parameter estimation: The function
fitAbn()
estimates the model’s parameters based on the DAG from the previous step.
abn
allows for two different model formulations, specified with the argument method
:
method = "mle"
fits a model under the frequentist paradigm using informationtheoretic criteria to select the best model.method = "bayes"
estimates the posterior distribution of the model parameters based on two Laplace approximation methods, that is, a method for Bayesian inference and an alternative to Markov Chain Monte Carlo (MCMC): A standard Laplace approximation is implemented in theabn
source code but switches in specific cases (see help page?fitAbn
) to the Integrated Nested Laplace Approximation from the INLA package requiring the installation thereof.
To generate new observations from a fitted ABN model, the function simulateAbn()
simulates data based on the DAG and the estimated parameters from the previous step. simulateAbn()
is available for both method = "mle"
and method = "bayes"
and requires the installation of the JAGS package.
Supported Data types
The abn
package supports the following distributions for the variables in the network:
Gaussian distribution for continuous variables.
Binomial distribution for binary variables.
Poisson distribution for variables with count data.
Multinomial distribution for categorical variables (only available with
method = "mle"
).
Unlike other packages, abn
does not restrict the combination of parentchild distributions.
Multilevel Models for Grouped Data Structures
The analysis of “hierarchical” or “grouped” data, in which observations are nested within higherlevel units, requires statistical models with parameters that vary across groups (e.g. mixedeffect models).
abn
allows to control for onelayer clustering, where observations are grouped into a single layer of clusters that are themself assumed to be independent, but observations within the clusters may be correlated (e.g. students nested within schools, measurements over time for each patient, etc). The argument group.var
specifies the discrete variable that defines the group structure. The model is then fitted separately for each group, and the results are combined.
For example, studying student test scores across different schools, a varying intercept model would allow for the possibility that average test scores (the intercept) might be higher in one school than another due to factors specific to each school. This can be modeled in abn
by setting the argument group.var
to the variable containing the school names. The model is then fitted as a varying intercept model, where the intercept is allowed to vary across schools, but the slope is assumed to be the same for all schools.
Under the frequentist paradigm (method = "mle"
), abn
relies on the lme4
package to fit generalized linear mixed models (GLMMs) for Binomial, Poisson, and Gaussian distributed variables. For multinomial distributed variables, abn
fits a multinomial baseline category logit model with random effects using the mclogit
package. Currently, only onelayer clustering is supported (e.g., for method = "mle"
, this corresponds to a random intercept model).
With a Bayesian approach (method = "bayes"
), abn
relies on its own implementation of the Laplace approximation and the package INLA
to fit a singlelevel hierarchical model for Binomial, Poisson, and Gaussian distributed variables. Multinomial distributed variables in general (see Section Supported Data Types) are not yet implemented with method = "bayes"
.
Basic Background
Bayesian network modeling is a data analysis technique ideally suited to messy, highly correlated and complex datasets. This methodology is rather distinct from other forms of statistical modeling in that its focus is on structure discovery—determining an optimal graphical model that describes the interrelationships in the underlying processes that generated the data. It is a multivariate technique and can be used for one or many dependent variables. This is a datadriven approach, as opposed to relying only on subjective expert opinion to determine how variables of interest are interrelated (for example, structural equation modeling).
Below and on the package’s website, we provide some cookbooktype examples of how to perform Bayesian network structure discovery analyses with observational data. The particular type of Bayesian network models considered here are additive Bayesian networks. These are rather different, mathematically speaking, from the standard form of Bayesian network models (for binary or categorical data) presented in the academic literature, which typically use an analytically elegant but arguably interpretationwise opaque contingency table parametrization. An additive Bayesian network model is simply a multidimensional regression model, e.g., directly analogous to generalized linear modeling but with all variables potentially dependent.
An example can be found in the American Journal of Epidemiology, where this approach was used to investigate risk factors for child diarrhea. A special issue of Preventive Veterinary Medicine on graphical modeling features several articles that use abn to fit epidemiological data. Introductions to this methodology can be found in Emerging Themes in Epidemiology and in Computers in Biology and Medicine where it is compared to other approaches.
What is an additive Bayesian network?
Additive Bayesian network (ABN) models are statistical models that use the principles of Bayesian statistics and graph theory. They provide a framework for representing data with multiple variables, known as multivariate data.
ABN models are a graphical representation of (Bayesian) multivariate regression. This form of statistical analysis enables the prediction of multiple outcomes from a given set of predictors while simultaneously accounting for the relationships between these outcomes.
In other words, additive Bayesian network models extend the concept of generalized linear models (GLMs), which are typically used to predict a single outcome, to scenarios with multiple dependent variables. This makes them a powerful tool for understanding complex, multivariate datasets.
The term Bayesian network is interpreted differently across various fields.
Bayesian network models often involve binary nodes, arguably the most frequently used type of Bayesian network. These models typically use a contingency table instead of an additive parameter formulation. This approach allows for mathematical elegance and enables key metrics like model goodness of fit and marginal posterior parameters to be estimated analytically (i.e., from a formula) rather than numerically (an approximation). However, this parametrization may not be parsimonious, and the interpretation of the model parameters is less straightforward than the usual Generalized Linear Model (GLM) type models, which are prevalent across all scientific disciplines.
While this is a crucial practical distinction, it’s a relatively lowlevel technical one, as the primary aspect of BN modeling is that it’s a form of graphical modeling – a model of the data’s joint probability distribution. This joint – multidimensional – aspect makes this methodology highly attractive for complex data analysis and sets it apart from more standard regression techniques, such as GLMs, GLMMs, etc., which are only onedimensional as they assume all covariates are independent. While this assumption is entirely reasonable in a classical experimental design scenario, it’s unrealistic for many observational studies in fields like medicine, veterinary science, ecology, and biology.
Examples
 Example 1: Basic usage
 Example 2: Restrict model search space
 Example 3: Grouped Data Structures
 Example 4: Using INLA vs internal Laplace approximation
Example 1: Basic Usage
This is a basic example which shows the basic workflow:
library(abn)
# Builtin toy dataset with two Gaussian variables G1 and G2, two Binomial variables B1 and B2, and one multinomial variable C
str(g2b2c_data)
# Define the distributions of the variables
dists < list(G1 = "gaussian",
B1 = "binomial",
B2 = "binomial",
C = "multinomial",
G2 = "gaussian")
# Build the score cache
cacheMLE < buildScoreCache(data.df = g2b2c_data,
data.dists = dists,
method = "mle",
max.parents = 2)
# Find the most probable DAG
dagMP < mostProbable(score.cache = cacheMLE)
# Print the most probable DAG
print(dagMP)
# Plot the most probable DAG
plot(dagMP)
# Fit the most probable DAG
myfit < fitAbn(object = dagMP,
method = "mle")
# Print the fitted DAG
print(myfit)
Example 2: Restrict Model Search Space
Based on example 1, we may know that the arc G1>G2 is not possible and that the arc from C > G2 must be present. This “expert knowledge” can be included in the model by banning the arc from G1 to G2 and retaining the arc from C to G2.
The retain and ban matrices are specified as an adjacency matrix of 0 and 1 entries, where 1 indicates that the arc is banned or retained, respectively. Row and column names must match the variable names in the data set. The corresponding column is a parent of the variable in the row. Each column represents the parents, and the row is the child. For example, the first row of the ban matrix indicates that G1 is banned as a parent of G2.
Further, we can restrict the maximum number of parents per node to 2.
# Ban the edge G1 > G2
banmat < matrix(0, nrow = 5, ncol = 5, dimnames = list(names(dists), names(dists)))
banmat[1, 5] < 1
# retain always the edge C > G2
retainmat < matrix(0, nrow = 5, ncol = 5, dimnames = list(names(dists), names(dists)))
retainmat[5, 4] < 1
# Limit the maximum number of parents to 2
max.par < 2
# Build the score cache
cacheMLE_small < buildScoreCache(data.df = g2b2c_data,
data.dists = dists,
method = "mle",
dag.banned = banmat,
dag.retained = retainmat,
max.parents = max.par)
print(paste("Without restrictions from example 1: ", nrow(cacheMLE$node.defn)))
print(paste("With restrictions as in example 2: ", nrow(cacheMLE_small$node.defn)))
Example 3: Grouped Data Structures
Depending on the data structure, we may want to control for onelayer clustering, where observations are grouped into a single layer of clusters that are themselves assumed to be independent, but observations within the clusters may be correlated (e.g., students nested within schools, measurements over time for each patient, etc.).
Currently, abn
supports only one layer clustering.
# Builtin toy data set
str(g2pbcgrp)
# Define the distributions of the variables
dists < list(G1 = "gaussian",
P = "poisson",
B = "binomial",
C = "multinomial",
G2 = "gaussian") # group is not among the list of variable distributions
# Ban arcs such that C has only B and P as parents
ban.mat < matrix(0, nrow = 5, ncol = 5, dimnames = list(names(dists), names(dists)))
ban.mat[4, 1] < 1
ban.mat[4, 4] < 1
ban.mat[4, 5] < 1
# Build the score cache
cache < buildScoreCache(data.df = g2pbcgrp,
data.dists = dists,
group.var = "group",
dag.banned = ban.mat,
method = "mle",
max.parents = 2)
# Find the most probable DAG
dag < mostProbable(score.cache = cache)
# Plot the most probable DAG
plot(dag)
# Fit the most probable DAG
fit < fitAbn(object = dag,
method = "mle")
# Plot the fitted DAG
plot(fit)
# Print the fitted DAG
print(fit)
Example 4: Using INLA vs internal Laplace approximation
Under a Bayesian approach, abn
automatically switches to the Integrated Nested Laplace Approximation from the INLA package if the internal Laplace approximation fails to converge. However, we can also force the use of INLA by setting the argument control=list(max.mode.error=100)
.
The following example shows that the results are very similar. It also shows how to constrain arcs as formula objects and how to specify different parent limits for each node separately.
library(abn)
# Subset of the buildin dataset, see ?ex0.dag.data
mydat < ex0.dag.data[,c("b1","b2","g1","g2","b3","g3")] ## take a subset of cols
# setup distribution list for each node
mydists < list(b1="binomial", b2="binomial", g1="gaussian",
g2="gaussian", b3="binomial", g3="gaussian")
# Structural constraints
## ban arc from b2 to b1
## always retain arc from g2 to g1
## parent limits  can be specified for each node separately
max.par < list("b1"=2, "b2"=2, "g1"=2, "g2"=2, "b3"=2, "g3"=2)
# now build the cache of precomputed scores according to the structural constraints
res.c < buildScoreCache(data.df=mydat, data.dists=mydists,
dag.banned= ~b1b2,
dag.retained= ~g1g2,
max.parents=max.par)
# repeat but using RINLA. The mlik's should be virtually identical.
if(requireNamespace("INLA", quietly = TRUE)){
res.inla < buildScoreCache(data.df=mydat, data.dists=mydists,
dag.banned= ~b1b2, # ban arc from b2 to b1
dag.retained= ~g1g2, # always retain arc from g2 to g1
max.parents=max.par,
control=list(max.mode.error=100)) # force using of INLA
## comparison  very similar
difference < res.c$mlik  res.inla$mlik
summary(difference)
}
Contributing
We greatly appreciate contributions from the community and are excited to welcome you to the development process of the abn
package. Here are some guidelines to help you get started:
Seeking Support: If you need help with using the
abn
package, you can seek support by creating a new issue on our GitHub repository. Please describe your problem in detail and include a minimal reproducible example if possible.Reporting Issues or Problems: If you encounter any issues or problems with the software, please report them by creating a new issue on our GitHub repository. When reporting an issue, try to include as much detail as possible, including steps to reproduce the issue, your operating system and R version, and any error messages you received.
Software Contributions: We encourage contributions directly via pull requests on our GitHub repository. Before starting your work, please first create an issue describing the contribution you wish to make. This allows us to discuss and agree on the best way to integrate your contribution into the package.
By participating in this project, you agree to abide by our code of conduct. We are committed to making participation in this project a respectful and harassmentfree experience for everyone.
Citation
If you use abn
in your research, please cite it as follows:
> citation("abn")
To cite the methodology of the R package 'abn' use:
Kratzer G, Lewis F, Comin A, Pittavino M, Furrer R (2023). “Additive Bayesian Network Modeling with the R Package abn.” _Journal of Statistical Software_,
*105*(8), 141. doi:10.18637/jss.v105.i08 <https://doi.org/10.18637/jss.v105.i08>.
To cite an example of a typical ABN analysis use:
Kratzer, G., Lewis, F.I., Willi, B., Meli, M.L., Boretti, F.S., HofmannLehmann, R., Torgerson, P., Furrer, R. and Hartnack, S. (2020). Bayesian Network
Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland. Frontiers in Veterinary Science, 7, 73
To cite the software implementation of the R package 'abn' use:
Furrer, R., Kratzer, G. and Lewis, F.I. (2023). abn: Modelling Multivariate Data with Additive Bayesian Networks. R package version 2.72.
https://CRAN.Rproject.org/package=abn
License
The abn
package is licensed under the GNU General Public License v3.0.
Code of Conduct
Please note that the abn
project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Applications
The abn website provides a comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation.
Technical articles
Kratzer et al. (2023): Additive Bayesian Network Modeling with the R Package abn
Kratzer et al. (2020) Bayesian Networks modeling applied to Feline Calicivirus infection among cats in Switzerland
Kratzer et al. (2018): Comparison between Suitable Priors for Additive Bayesian Networks
Koivisto et al. (2004): Exact Bayesian structure discovery in Bayesian networks
Friedman et al. (2003): Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
Friedman et al. (1999): Data analysis with Bayesian networks: A bootstrap approach
Heckerman et al. (1995): Learning Bayesian Networks – The Combination of Knowledge And StatisticalData
Application articles
Delucchi et al. (2022): Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors
Guinat et al. (2020) Biosecurity risk factors for highly pathogenic avian influenza (H5N8) virus infection in duck farms, France
Hartnack et al. (2019) Additive Bayesian networks for antimicrobial resistance and potential risk factors in nontyphoidal Salmonella isolates from layer hens in Uganda
Ruchti et al. (2019): Progression and risk factors of pododermatitis in parttime group housed rabbit does in Switzerland
Comin et al. (2019) Revealing the structure of the associations between housing system, facilities, management and welfare of commercial laying hens using Additive Bayesian Networks
Ruchti et al. (2018): Pododermatitis in group housed rabbit does in Switzerland – prevalence, severity and risk factors
Pittavino et al. (2017): Comparison between generalised linear modelling and additive Bayesian network; identification of factors associated with the incidence of antibodies against Leptospira interrogans sv Pomona in meat workers in New Zealand
Hartnack et al. (2017): Attitudes of Austrian veterinarians towards euthanasia in small animal practice: impacts of age and gender on views on euthanasia
Lewis et al. (2012): Revealing the Complexity of Health Determinants in Resourcepoor Settings
Lewis et al. (2011): Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data
Workshops
Causality:
 4 December 2018, Beate Sick & Gilles Kratzer of the 1st Causality workshop talk, Bayesian Networks meet Observational data. (UZH, Switzerland)
ABN modeling
07 July 2021, workshop at the UseR! Conference on Additive Bayesian Networks Modeling. (Online)
29 March 2019, workshop at the SVEPM conference on Multivariate analysis using Additive Bayesian Networks. (Utrecht, Netherland)
Presentations
4 October 2018, talk in Nutricia (Danone). Multivariable analysis: variable and model selection in system epidemiology. (Utrecht, Netherland)
30 May 2018. Brown Bag Seminar in ZHAW. Presentation: Bayesian Networks Learning in a Nutshell. (Winterthur, Switzerland)