| Type: | Package |
| Title: | Bayesian Network Learning Using the OMP Algorithm |
| Version: | 1.0 |
| Maintainer: | Michail Tsagris <mtsagris@uoc.gr> |
| Depends: | R (≥ 4.0) |
| Imports: | bnlearn, pchc, Rcpp, Rfast, stats |
| LinkingTo: | Rcpp, RcppArmadillo |
| Description: | Bayesian network learning using the orthogonal matching pursuit to perform variable selection and then application of the Hill-Climbing greedy search or the TABU search to orient the edges. Some relevant papers are Tsagris M. and Adam C. (2026) <doi:10.13140/RG.2.2.26278.28489>, Tsagris M. (2021) <doi:10.1007/s10614-020-10065-7>, Tsagris M. (2022) <doi:10.3390/math10152604>, Tsagris et al. (2022) <doi:10.1109/TCBB.2020.3029952>, Tsamardinos I., Brown L.E. and Aliferis C.F. (2006) <doi:10.1007/s10994-006-6889-7>, Mallat S. G. and Zhang Z. (1993) <doi:10.1109/78.258082>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| NeedsCompilation: | yes |
| Packaged: | 2026-10-23 17:38:33 UTC; mtsag |
| Author: | Michail Tsagris [aut, cre],
Christos Adam 
[aut] |
| Repository: | CRAN |
| Date/Publication: | 2026-11-07 15:50:02 UTC |
Bayesian Network Learning Using the OMP Algorithm
Description
Bayesian network learning using the orthogonal matching pursuit to perform variable selection and then application of the Hill-Climbing greedy search or the TABU search to orient the edges. Some relevant papers are Tsagris M. and Adam C. (2026) <doi:10.13140/RG.2.2.26278.28489>, Tsagris M. (2021) <doi:10.1007/s10614-020-10065-7>, Tsagris M. (2022) <doi:10.3390/math10152604>, Tsagris et al. (2022) <doi:10.1109/TCBB.2020.3029952>, Tsamardinos I., Brown E.L. and Aliferis F.C. (2006) <doi:10.1007/s10994-006-6889-7> and Mallat S. G. and Zhang Z. (1993) <doi:10.1109/78.258082>.
Details
| Package: | omphc |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2026-10-23 |
| License: | GPL-2 |
Maintainers
Michail Tsagris <mtsagris@uoc.gr>.
Author(s)
Michail Tsagris mtsagris@uoc.gr.
References
Spirtes P., Glymour C. and Scheines R. (2001). Causation, Prediction, and Search. The MIT Press, Cambridge, MA, USA, 3nd edition.
Adjacency matrix of a Bayesian network
Description
Adjacency matrix of a Bayesian network.
Usage
bnmat(dag)
Arguments
dag |
A BN object, an object of class "bn". |
Details
The function is called from the "bnlearn" package which invokes the "Rgraphviz" package from Bioconductor and you need to install it first.
Value
Adjacency matrix of a Bayesian network is extracted.
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
See Also
Examples
x <- matrix( rnorm(200 * 10, 1, 10), nrow = 200 )
a <- omphc(x)
bnmat(a$dag)
Plot of a Bayesian network
Description
Plot of a Bayesian network.
Usage
bnplot(dag, shape = "ellipse", main = NULL, sub = NULL, highlight = NULL)
Arguments
dag |
A BN object, an object of class "bn". |
shape |
A character string defining the shape of the nodes, "ellipse" (default value), "circle" or "rectangle". |
main |
The main title of the graph displayed on the top. |
sub |
The subtitle of the graph displayed at the bottom. |
highlight |
A list with options specifying which nodes to plot with different colours. You can also check the package bnlearn or the package Rgraphviz for more information on this, or simply check the example below. |
Details
The function is called from the "bnlearn" package which invokes the "Rgraphviz" package from Bioconductor and you need to install it first.
Value
The Bayesian network is visualised.
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
See Also
Examples
# simulate a dataset with continuous data
x <- matrix( rnorm(100 * 15, 1, 5), nrow = 100 )
a <- omphc(x)
skeleton of a Bayesian network produced by the OMP algorithm
Description
skeleton of a Bayesian network produced by the OMP algorithm.
Usage
omp.network(x, R = NULL, method = "pvalue", tol = 0.05)
Arguments
x |
A numerical matrix with the variables. If you have a data.frame (i.e. categorical data) turn them into a matrix. Note, that for the categorical case data, the numbers must start from 0. No missing data are allowed. |
R |
If the correlation matrix is available, pass it here. |
method |
The method to proceed with the OMP, p-value based ("pvalue"), BIC based ("BIC"), or adjusted R-square ("ar2") based. |
tol |
The tolerance value to terminate the OMP. The significance level, a value for the BIC difference, or a percentage for the adjusted R-square. |
Details
As in FEDHC the first phase consists of a variable selection procedure, the OMP algorithm. Then, hill-climbing or TABU search is applied to direct the edges.
Value
A list including:
ini.stat |
The test statistics of the univariate associations. |
ini.pvalue |
The initial p-values univariate associations. |
pvalue |
A matrix with the logarithm of the p-values of the updated associations. This final p-value is the maximum p-value among the two p-values in the end. |
runtime |
The duration of the algorithm. |
ntests |
The number of tests conducted during each k. |
G |
The adjancency matrix. A value of 1 in G[i, j] appears in G[j, i] also, indicating that i and j have an edge between them. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsamardinos I., Brown E.L. and Aliferis F.C. (2006). The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learning, 65(1): 31–78.
See Also
Examples
m <- rbn2(1000, p = 50, nei = 5)
G <- m$G
x <- m$x
a <- omp.network(x, tol = 0.1)
The OMP-based Bayesian network learning algorithm
Description
The OMP-based Bayesian network learning algorithm.
Usage
omphc(x, skel = NULL, R = NULL, method = "pvalue", tol = 0.05, algo = "hc",
tabu = 10, restart = 10, score = "bic-g", blacklist = NULL, whitelist = NULL)
Arguments
x |
A numerical matrix with the variables. No missing data are allowed. |
skel |
If you have the output of the skeleton phase, the output from the function
|
R |
If the correlation matrix is available, pass it here. |
method |
If the skeleton is not available, the method to proceed with the OMP, p-value based ("pvalue"), BIC based ("BIC"), or adjusted R-square ("ar2") based. |
tol |
The tolerance value to terminate the OMP. The significance level, a value for the BIC difference, or a percentage for the adjusted R-square. |
algo |
The scoring algorithm, either "hc" or "tabu" |
tabu |
An integer, the length of the tabu list used in the tabu function. |
restart |
An integer, the number of random restarts. |
score |
A character string, the label of the network score to be used in the algorithm. If none is specified, the default score is the Bayesian Information Criterion for continuous data sets. The available score for continuous variables are: "bic-g" (default), "loglik-g", "aic-g", "bic-g" or "bge". |
blacklist |
A data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs not to be included in the graph. |
whitelist |
A data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph. |
Details
The OMPHC or OMPTABU algorithm is implemented. The OMP algorithm (Tsagris et al., 2002, Mallat S. G. and Zhang Z. (1993)), is implemented during the skeleton identification phase. Next, the Hill Climbing greedy search or the Tabu search is employed to score the network.
Value
A list including:
ini |
A list including the output of the |
dag |
A "bn" class output. A list including the outcome of the Hill-Climbing or the Tabu search phase. See the package "bnlearn" for more details. |
scoring |
The score value. |
runtime |
The duration of the algorithm. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2022). The \gamma-OMP
Algorithm for Feature Selection With Application to Gene Expression Data.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(2): 1214–1224.
Tsamardinos I., Brown E.L. and Aliferis F.C. (2006). The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learning, 65(1): 31–78.
Mallat S. G. and Zhang Z. (1993). Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41(12): 3397–3415.
See Also
Examples
m <- rbn2(1000, p = 50, nei = 5)
G <- m$G
x <- m$x
a <- omphc(x, tol = 0.01)
Random values simulation from a Bayesian network
Description
Random values simulation from a Bayesian network.
Usage
rbn(n, dag, x)
Arguments
n |
The number of observations to generate. |
dag |
A "bn" object. See the examples for more information. |
x |
The data used to fit the Bayesian network in a data.frame format. |
Details
This information is taken directly from the R package "bnlearn". This function implements forward/logic sampling: values for the root nodes are sampled from their (un-conditional) distribution, then those of their children conditional on the respective parent sets. Thisis done iteratively until values have been sampled for all nodes.If "dagobj" contains NA parameter estimates (because of unobserved discrete parents configurations in the data the parameters were learned from), rbn will produce observations that contain NAs when thoseparents configurations appear in the simulated samples.
Value
A data frame with the same structure (column names and data types) of the argument "data".
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Korb K. and Nicholson A.E. (2010).Bayesian Artificial Intelligence. Chapman & Hall/CRC, 2nd edition.
See Also
Examples
# simulate a dataset with continuous data
x <- matrix( rnorm(200 * 20, 1, 10), nrow = 200 )
a <- omphc(x)
sim <- rbn(100, dag = a$dag, x = x)
Continuous data simulation from a DAG.
Description
Contunuous data simulation from a DAG.
Usage
rbn2(n, G = NULL, p, nei, low = 0.1, up = 1)
rbn3(n, p, s, a = 0, m, G = NULL, seed = FALSE)
Arguments
n |
A number indicating the sample size. |
p |
A number indicating the number of nodes (or vectices, or variables). |
nei |
The average number of neighbours. |
s |
A number in |
a |
A number in |
m |
A vector equal to the number of nodes. This is the mean vector of the normal distribution from which the data are to be generated. This is used only when |
G |
If you already have an an adjacency matrix in mind, plug it in here, otherwise, leave it NULL. |
seed |
If seed is TRUE, the simulated data will always be the same. |
low |
Every child will be a function of some parents. The beta coefficients of the parents will be drawn uniformly from two numbers, low and up. See details for more information on this. |
up |
Every child will be a function of some parents. The beta coefficients of the parents will be drawn uniformly from two numbers, low and up. See details for more information on this. |
Details
In the case where no adjacency matrix is given, an p \times p matrix with zeros everywhere is created.
Every element below the diagonal is is replaced by random values from a Bernoulli distribution with probability of success equal to s. This is the matrix B. Every value of 1 is replaced by a uniform value in 0.1, 1. This final matrix is called A. The data are generated from a multivariate normal distribution with a zero mean vector and covariance matrix equal to \left({\bf I}_p- A\right)^{-1}\left({\bf I}_p- A\right), where {\bf I}_p is the p \times p identiy matrix. If a is greater than zero, the outliers are generated from a multivariate normal with the same covariance matrix and mean vector the one specified by the user, the argument "m". The flexibility of the outliers is that you cna specifiy outliers in some variables only or in all of them. For example, m = c(0,0,5) introduces outliers in the third variable only, whereas m = c(5,5,5) introduces outliers in all variables. The user is free to decide on the type of outliers to include in the data.
For the "rdag2", this is a different way of simulating data from DAGs. The first variable is normally generated. Every other variable can be a function of some previous ones. Suppose now that the i-th variable is a child of 4 previous variables. We need for coefficients b_j to multiply the 4 variables and then generate the i-th variable from a normal with mean \sum_{j=1}b_j X_j and variance 1. The b_j will be either positive or negative values with equal probability. Their absolute values ranges between "low" and "up". The code is accessible and you can see in detail what is going on. In addition, every generated data, are standardised to avoid numerical overflow.
Value
A list including:
nout |
The number of outliers. |
G |
The adcacency matrix used. For the "rdag" if G[i, j] = 2, then G[j, i] = 3 and this means that there is an arrow from j to i. For the "rdag2" the entries are either G[i, j] = G[j, i] = 0 (no edge) or G[i, j] = 1 and G[j, i] = 0 (indicating i -> j). |
A |
The matrix with the with the uniform values in the interval |
x |
The simulated data. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris M. (2019). Bayesian network learning with the PC algorithm: an improved and correct variation. Applied Artificial Intelligence, 33(2): 101–123.
Tsagris M., Borboudakis G., Lagani V. and Tsamardinos I. (2018). Constraint-based Causal Discovery with Mixed Data. International Journal of Data Science and Analytics, 6: 19–30.
Spirtes P., Glymour C. and Scheines R. (2001). Causation, Prediction, and Search. The MIT Press, Cambridge, MA, USA, 3nd edition.
Colombo Diego, and Marloes H. Maathuis (2014). Order-independent constraint-based causal structure learning. Journal of Machine Learning Research, 15(1): 3741–3782.
See Also
Examples
x <- rbn3(100, 20, 0.2)$x
a <- omphc(x)
Update the skeleton of the Bayesian network
Description
The OMP-based Bayesian network learning algorithm.
Usage
update.skel(skel, method = "pvalue", tol = 0.01)
Arguments
skel |
The output of the function |
method |
The method used to construct the skeleton, p-value based ("pvalue"), BIC based ("BIC"), or adjusted R-square ("ar2") based. |
tol |
The tolerance value to update the skeleton. The significance level, a value for the BIC difference, or a percentage for the adjusted R-square. It has to be smaller than the tolerance value initially used. |
Details
Suppose you have run the skeleton of the OMPHC with a tolerance value equal to 0.05 using the "pvalue"
method. Now, you wish to update the skeleton for tol=0.01. Instead of running the omp.network
again with tol=0.01, you can update the G matrix with this function. The function will change the G
matrix, and the Rho list.
Value
The same output as omp.network .
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
See Also
Examples
m <- rbn2(1000, p = 50, nei = 5)
G <- m$G
x <- m$x
a1 <- omp.network(x, tol = 0.1)
a2 <- omp.network(x, tol = 0.01)
b <- update.skel(a1, method = "pvalue", tol = 0.01)