Table of Contents

1. Description

This is an R wrapper for which provides users capabilities to work with using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to ‘’ cloud or local API for calculations.

Note: running calculations requires a valid API license (past the initial trial period of the local API).

In the rest of this document, the R environment for is referred to as R-Agena.

2. Prerequisites and Installation

To install R-Agena from CRAN:


R-Agena requires rjson, httr, Rgraphviz, and openxlsx packages installed.

To install rjson, httr, and openxlsx from CRAN:


To install Rgraphviz from Bioconductor:

if (!require("BiocManager", quietly = TRUE))


3. Structure of R-Agena Classes

The Bayesian networks (BNs) in the R environment are represented with several objects: Node, Network, DataSet, and Model. These R objects generally follow their equivalents defined in models.

3.1 Node objects

These represent the nodes in a BN. The fields that define a Node object are as follows:

3.1.1 id

Mandatory field to create a new Node object. This is the unique identifier of model nodes.

3.1.2 name

Name of the node, optional. If not defined, id of the node will be passed onto the name field too.

3.1.3 description

Description of the node, optional. If not defined, “New Node” will be assigned to the description field.

3.1.4 type

Node type, it can be:

If it’s not specified when creating a new node, the new node is “Boolean” by default if it’s not a simulation node; and it is “ContinuousInterval” by default if it’s a simulation node.

3.1.5 parents

Other Node objects can be pointed as parents of a Node object. It is not recommended to modify this field manually, to add parents to a node, see the function addParent().

Something to keep in mind: the parent-child relationship information is stored at Node level in R environment thanks to this field, as opposed to the separate links field of a .cmpx/.json file for the models. When importing or exporting .cmpx files you do not need to think about this difference as the cmpx parser and writer functions handle the correct formats. This difference allows adding and removing Node objects as parents

3.1.6 simulated

A boolean field to indicate whether the node is a simulation node or not.

3.1.7 distr_type

The table type of the node, it can be:

3.1.8 states

States of the node (if not simulated). If states are not specified, depending on the type, sensible default states are assigned. Default states for different node types are:

And for a node with the table type (distr_type) “Expression”, the default expression is: “Normal(0,1000000)”

3.1.9 probabilities

If the table type (distr_type) of the node is “Manual”, the node will have state probabilities, values in its NPT. This field is a list of these values. The length of the list depends on the node states and the number of its parents. To see how to set probability values for a node, see setProbabilities() function.

3.1.10 expressions

If the table type (distr_type) of the node is “Expression” or “Partitioned”, the node will have expression(s) instead of the manually defined NPT values.

To see how to set the expressions for a node, see set_expressions() function.

3.1.11 partitions

If the table type (distr_type) of the node is “Partitioned”, in addition to the expressions, the node will have the partitions field. This field is a list of strings, which are ids of the parent nodes on which the node expression is partitioned.

3.1.12 variables

The node variables are called constants on Modeller. This field, if specified, sets the constant value for the node observations.

3.2 Network objects

These represent each network in a BN. Networks consist of nodes and in a BN model there might be more than one network. These networks can also be linked to each other with the use of input and output nodes. For such links, see Model$networkLinks field later in this document.

The fields that define a Network object are as follows:

3.2.1 id

Id of the Network. Mandatory field to create a new network.

3.2.2 name

Name of the network, optional. If not specified, id of the network is passed onto name field as well.

3.2.3 description

Description, optional. If not specified, the string “New Network” is assigned to description field by default.

3.2.4 nodes

A list of Node objects which are in the network. These Node objects have their own fields which define them as explained above in this document.

Note that Network objects do not have a links field unlike the models. As explained in Node$parents section above, this information is stored in Node objects in the R environment. When importing a .cmpx model, the information in links field is used to populate Node$parents fields for each node. Similarly, when exporting to a .cmpx/.json file, the parent-child information in Node$parents field is used to create the links field of the Network field of the .cmpx/.json.

3.3 DataSet objects

These represent the set of observations in a BN. A Model can have multiple DataSet objects in its dataSets field. When a new Model is created, it always comes with a default DataSet object with the id “Scenario 1” and with blank observations. It is possible to add more datasets (scenarios) with their ids. Each DataSet object under a Model can be called a new “scenario”.

3.3.1 id

Id of the dataset (scenario).

3.3.2 observations

Under each dataset (scenario), observations for all the observed nodes in all the networks of the model (in terms of their states or values) are listed. If it’s hard evidence, observation for a node will have a single value with the weight of 1. If a node in the model has a value in its variable field, this value will be passed onto the dataset (scenario) with the weight of 1.

3.3.3 results

This field is defined only for when a .cmpx model with calculations is imported. When creating a new BN in the R environment, this field is not created or filled in. The results field stores the posterior probability and inference results upon model calculation on Cloud.

3.4 Model objects

These represent the overall BN. A single .cmpx file corresponds to a singe Model. A BN model can have multiple networks with their own nodes, links between these networks, and datasets.

3.4.1 id

Id of the Model, optional. If not specified, the id of the first Network in the model’s networks field is used to create a Model$id.

3.4.2 networks

A list of all the Network objects that make up the model. This field is mandatory for creating a new Model object.

3.4.3 dataSets

Optional field for DataSet objects. When creating a new Model, it is possible to use predefined scenarios as long as their DataSet$observations field has matching ids with the nodes in the model. If none is specified, by default a new Model object will come with an empty dataset called “Scenario 1”.

If the Model has multiple networks, it is possible to have links between these networks, following the model networkLinks format.

To see how to create these links, see add_network_link() function later in this document.

3.4.5 settings

Model settings for calculations. It includes the following fields (the values in parantheses are the defaults if settings are not specified for a model):

Model settings can be provided when creating a new model, if not provided the model will come with the default settings. Default settings can be changed later on (with the method $change_settings()), or model settings can be reset back to default values (with the method $default_settings()). See the correct input parameter format for these functions in the following section. Individual fields in model setting can be adjusted by directly accessing the field too.

4. Class Methods

The Node, Network, and Model objects have their own respective methods to help their definition and manipulate their fields. The R class methods are used with the $ sign following an instance of the class. For example,






4.1 Node methods

Some Node fields can be modified with a direct access to the field. For example, to update the name or a description information of a Node, simply use:

example_node$name <- "new node name"


example_node$description <- "new node description"

Because changing the name or description of a Node does not cause any compatibility issues. However, some fields such as table type or parents will have implications for other fields. Changing the node parents will change the size of its NPT, changing the node’s table type from “Manual” to “Expression” will mean the state probabilities are now defined in a different way. Therefore, to modify such fields of a Node, use the corresponding method described below. These methods will ensure all the sensible adjustments are made when a field of a Node has been changed.

These are the methods Node objects can call for various purposes with their input parameters shown in parantheses:

4.1.1 add_parent(newParent)

The method to add a new parent to a node. Equivalent of adding an arc between two nodes on Modeller. The input parameter newParent is another Node object. If newParent is already a parent for the node, the function does not update the parents field of the node.

When a new parent is added to a node, its NPT values and expressions are reset/resized accordingly.

There is also a method called addParent_byID(newParentID, varList), however, this is only used in the cmpx parser. To add a new parent to a Node, it is recommended to use add_parent() function with a Node object as the input.

4.1.2 remove_parent(oldParent)

The method to remove one of the existing parents of a node. Equivalent of removing the arc between two nodes on Modeller. The input parameter oldParent is a Node object which has already been added to the parents field of the node.

When an existing parent is removed from a node, its NPT values and expressions are reset/resized accordingly.

4.1.3 get_parents()

A method to list all the existing parent nodes of a Node.

4.1.4 set_distribution_type(new_distr_type)

A method to set the table type (distr_type) of a node. If a Node is simulated, its table type can be “Expression” or “Partitioned” - the latter is only if the node has parent nodes. If a Node is not simulated, its table type can be “Manual”, “Expression”, or “Partitioned Expression (if the node has parent nodes)”.

4.1.5 set_probabilities(new_probs, by_rows = TRUE)

The method to set the probability values if the table type (distr_type) of a Node is “Manual”. new_probs is a list of numerical values, and the length of the input list depends on the number of the states of the node and of its parents.

You can format the input list in two different orders. If the parameter by_rows is set to true, the method will read the input list to fill in the NPT row by row; if set to false, the method will read the input list to fill in the NPT column by columnn. This behaviour is illustrated with use case examples later in this document.

4.1.6 set_expressions(new_expr, partition_parents = NULL)

The method to set the probability values if the table type (distr_type) of a Node is “Expression” or “Partitioned”. If the table type is “Expression”, new_expr is a single string and partition_parents is left NULL. If the table type is “Partitioned”, new_expr is a list of expressions for each parent state, and partition_parents is a list of strings for each partitioned parent node’s id.

4.1.7 set_variable(variable_name, variable_value)

A method to set variables (constants) for a node. Takes the variable_name and variable_value inputs which define a new variable (constant) for the node.

4.1.8 remove_variable(variable_name)

A method to remove one of the existing variables (constants) from a node, using the variable_name.

4.2 Network methods

As described above, Node objects can be created and manipulated outside a network in the R environment. Once they are defined, they can be added to a Network object. Alternatively, a Network object can be created first and then its nodes can be specified. The R environment gives the user freedom, which is different from Modeller where it is not possible to have a node completely outside any network. Once a Network object is created, with or without nodes, the following methods can be used to modify and manipulate the object.

4.2.1 add_node(newNode)

A method to add a new Node object to the nodes field of a Network object. The input newNode is a Node object and it is added to the network if it’s not already in it.

Note that adding a new Node to the network does not automatically add its parents to the network. If the node has parents already defined, you need to add all the parent Nodes separately to the network, too.

4.2.2 remove_node(oldNode)

A method to remove an existing Node object from the network. Note that removing a Node from a network doesn’t automatically remove it from its previous parent-child relationships in the network. You need to adjust such relationships separately on Node level.

4.2.3 get_nodes()

A method to see ids of all the nodes in a network.

4.2.4 plot()

A method to plot the graphical structure of a BN network.

4.3 Model methods

A Model object consists of networks, network links, datasets, and settings. A new Model object can be created with a network (or multiple networks). By default, it is created with a single empty dataset (scenario) called “Scenario 1”. Following methods can be used to modify Model objects:

4.3.1 add_network(newNetwork)

A method to add a new Network object to the networks field of a Model object. The input newNetwork is a Network object and it is added to the model if it’s not already in it.

4.3.2 remove_network(oldNetwork)

A method to remove an existing Network object from the model. Note that removing a Node from a network doesn’t automatically remove its possible network links to other networks in the model. networkLinks field of a Model should be adjusted accordingly if needed.

4.3.3 get_networks()

A method to see ids of all the networks in a model.

This is the method to add links to a model between its networks. These links start from a “source node” in a network and go to a “target node” in another network. To create the link, the source and target nodes in the networks need to be specified together with the network they belong to (by the Node and Network ids). The input parameters are as follows:

Note that links between networks are allowed only when the source and target nodes fit certain criteria. Network links are allowed if:

4.3.5 remove_network_link(source_network, source_node,target_network, target_node)

A method to remove network links, given the ids of the source and target nodes (and the networks they belong to).

A method to remove all existing network links in a model.

4.3.7 create_dataSet(id)

It is possible to add multiple scenarios to a model. These scenarios are new DataSet objects added to the dataSets field of a model. Initially these scenarios have no observations and are only defined by their ids. The scenarios are populated with the enter_observation() function.

4.3.8 remove_dataSet(olddataSet)

A method to remove an existing scenario from the model. Input parameter olddataSet is the string which is the id of a dataset (scenario).

4.3.9 get_dataSets()

A method to list the ids of all existing scenarios in a model.

4.3.10 enter_observation(dataSet = NULL, node, network, value, variable_input = FALSE, soft_evidence = FALSE)

A method to enter observation to a model. To enter the observation to a specific dataset (scenario), the dataset id must be given as the input parameter dateSet. If dataSet is left NULL, the entered observation will by default go to “Scenario 1”. This means that if there is no extra datasets created for a model (which by default comes with “Scenario 1”), any observation entered will be set for this dataset (mimicking the behaviour of entering observation to Modeller).

The observation is defined with the mandatory input parameters: * node = Node$id of the observed node * network = Network$id of the network the observed node belongs to * value = this parameter can be: * the value or state of the observation for the observed node (if variable_input and soft_evidence are FALSE) * the id of a variable (constant) defined for the node (if variable_input is TRUE) * the array of multiple values and their weights (if soft_evidence is TRUE) * variable_input = a boolean parameter, set to TRUE if the entered observation is a variable (constant) id for the node instead of an observed value * soft_evidence = a boolean parameter, set to TRUE if the entered observation is not hard evidence. Then the value parameter should follow c(value_one, value_one_weight, value_two, value_two_weight, ..., value_n, value_n_weight)

4.3.11 remove_observation(dataSet = NULL, node, network)

A method to remove a specific observation from the model. It requires the id of the node which has the observation to be removed and the id of the network the node belongs to.

4.3.12 clear_dataSet_observations(dataSet)

A method to clear all observations in a specific dataset (scenario) in the model.

4.3.13 clear_all_observations()

A method to clear all observations defined in a model. This function removes all observations from all datasets (scenarios).

4.3.14 import_results(results_file)

A method to import results of a calculated dataSet from a json file. This correct format for the results json file for this method is the file generated with the local developer API calculation (see Section 9).

Note that when you use local API calculation, the results are imported to the model automatically.

4.3.15 change_settings(settings)

A method to change model settings. The input parameter settings must be a list with the correctly named elements, for example:

new_settings <- list(parameterLearningLogging = TRUE, 
                    discreteTails = FALSE, 
                    sampleSizeRanked = 10, 
                    convergence = 0.05, 
                    simulationLogging = TRUE, 
                    iterations = 100, 
                    tolerance = 1)


If you prefer to adjust only one of the setting fields, you can directly access the field, for example:

example_model$settings$convergence <- 0.01

4.3.16 default_settings()

A method to reset model settings back to default values. The default values for model settings are:

4.3.17 to_cmpx(filename = NULL)

A method to export the Model to a .cmpx file. This method passes on all the information about the model, its datasets, its networks, their nodes, and model settings to a .cmpx file in the correct format readable by

If the input parameter filename is not specified, it will use the Model$id for the filename.

4.3.18 to_json(filename = NULL)

A method to export the Model to a .json file instead of .cmpx. See to_cmpx() description above for all the details.

4.3.19 get_results()

A method to generate a .csv file based on the calculation results a Model contains. See Section 8 for details.

4.4 Other R-Agena Functions

R-Agena environment provides certain other functions outside the class methods.

4.4.1 from_cmpx(modelPath = "/path/to/model/file.cmpx")

This is the cmpx parser function to import a .cmpx file and create R objects based on the model in the file. To see its use, see Section 5 and Section 9.

4.4.2 create_batch_cases(inputModel, inputData)

This function takes an R Model object (inputModel) and an input CSV file (inputData) with observations defined in the correct format and creates a batch of datasets (scenarios) for each row in the input data and generates a .json file. To see its use and the correct format of the CSV file for a model’s data, see Section 7.

4.4.3 create_csv_template(inputModel)

This function creates an empty CSV file with the correct format so that it can be filled in and used for create_batch_bases().

4.4.4 create_sensitivity_config(...)

A function to create a sensitivity configuration object if a sensitivity analysis request will be sent to Cloud servers. Its parameters are:

For the use of the function, see Section 8.

R-Agena environment allows users to send their models to Cloud servers for calculation. The functions around the server capabilities (including authentication) are described in Section 8.

R-Agena environment allows users to connect to the local developer API for calculation. The functions about the local developer API communication are descibed in Section 9.

5. Importing a Model from .cmpx

To import an existing model (from a .cmpx file), use the from_cmpx() function:


new_model <- from_cmpx("/path/to/model/file.cmpx")

This creates an R Model object with all the information taken from the .cmpx file. All fields and sub-fields of the Model object (as per Section 3) are accessible now. For example, you can see the networks in this model with:


Each network in the model is a Network object, therefore you can access its fields with the same logic, for example to see the id of the first network and all the nodes in the first network in the BN, use respectively:


Similarly, each node in a network itself is a Node object. You can display all the fields of a node. Example uses for the second node in the first network of a model:


Once the R model is created from the imported .cmpx file, the Model object as well as all of its Network, DataSet, and Node objects can be manipulated using R methods.

6. Creating and Modifying a Model in R

It is possible to create an model entirely in R, without a .cmpx file to begin with. Once all the networks and nodes of a model are created and defined in R, you can export the model to a .cmpx or .json file to be used with calculations and inference, locally or on Cloud. In this section, creating a model is shown step by step, starting with nodes.

Import the installed R code with


6.1 Creating Nodes

In the R environment, Node objects represent the nodes in BNs, and you can create Node objects before creating and defining any network. To create a new node, only its id (unique identifier) is mandatory, you can define some other optional fields upon creation if desired. A new node creation function takes the following parameters where id is the only mandatory one and all others are optional:

new("Node", id, name, description, type, simulated, states)

# id parameter is mandatory
# the rest is optional

If the optional fields are not specified, the nodes will be created with the defaults. The default values for the fields, if they are not specified, are:

Once a new node is created, depending on the type and number of states, other fields are given sensible default values too. These fields are distr_type (table type), probabilities or expressions. To specify values in these fields, you need to use the relevant set functions (explained in Section and shown later in this section). The default values for these fields are:

Look at the following new node creation examples:

node_one <- new("Node", id = "node_one")
node_two <- new("Node", id = "node_two", name = "Second Node")
node_three <- new("Node", id = "node_three", type = "Ranked")
node_four <- new("Node", id = "node_four", type = "Ranked", states = c("Very low", "Low", "Medium", "High", "Very high"))

Looking up some example values in the fields that define these nodes:

6.2 Modifying Nodes

To update node information, some fields can be simply overwritten with direct access to the field if it does not affect other fields. These fields are node name, description, or state names (without changing the number of states). For example:

node_one$states <- c("Negative","Positive")
node_one$description <- "first node we have created"

Other fields can be specified with the relevant set functions. To set probability values for a node with a manual table (distr_type), you can use set_probabilities() function:


Note that the set_probabilities() function takes a list as input, even when the node has no parents and its NPT has only one row of probabilities. If the node has parents, the NPT will have multiple rows which should be in the input list.

Assume that node_one and node_two are the parents of node_three (how to add parent nodes is illustrated later in this section). Now assume that you want node_three to have the following NPT:

node_one Negative Positive
node_two False True False True
Low 0.1 0.2 0.3 0.4
Medium 0.4 0.45 0.6 0.55
High 0.5 0.35 0.1 0.05

There are two ways to order the values in this table for the set_probabilities() function, using the boolean by_rows parameter. If you want to enter the values following the rows in Modeller NPT rather than ordering them by the combination of parent states (columns), you can use by_rows = TRUE where each element of the list is a row of the Modeller NPT:

node_three$set_probabilities(list(c(0.1, 0.2, 0.3, 0.4), c(0.4, 0.45, 0.6, 0.55), c(0.5, 0.35, 0.1, 0.05)), by_rows = TRUE)

If, instead, you want to define the NPT with the probabilities that add up to 1 (conditioned on the each possible combination of parent states), you can set by_rows = FALSE as the following example:

node_three$set_probabilities(list(c(0.1, 0.4, 0.5), c(0.2, 0.45, 0.35), c(0.3, 0.6, 0.1), c(0.4, 0.55, 0.05)), by_rows = FALSE)

Similarly, you can use set_expressions() function to define and update expressions for the nodes without Manual NPT tables. If the node has no parents, you can add a single expression:


Or if the node has parents and the expression is partitioned on the parents:

example_node$set_expressions(c("Normal(90,10)", "Normal(110,15)", "Normal(120,30)"), partition_parents = "parent_node")

Here you can see the expression is an array with three elements and the second parameter (partition_parameters) contains the ids of the parent nodes. Expression input has three elements based on the number of states of the parent node(s) on which the expression is partitioned.

6.3 Adding and Removing Parent Nodes

To add parents to a node, you can use addParent() function. For example:


This adds node_one to the parents list of node_three, and resizes the NPT of node_three (and resets the values to a discrete uniform distribution).

To remove an already existing parent, you can use:


This removes node_one from the parents list of node_three, and resizes the NPT of node_three (and resets the values to a discrete uniform distribution).

Below we follow the steps from creation of node_three to the parent modifications and see how the NPT of node_three changes after each step.

node_three <- new("Node", id = "node_three", type = "Ranked")
[1] 0.3333333

[1] 0.3333333

[1] 0.3333333

#discrete uniform with three states (default of Ranked node)
node_three$setProbabilities(list(0.7, 0.2, 0.1))
[1] 0.7

[1] 0.2

[1] 0.1
[1] "node_one"

# node_one has been added to the parents list of node_three
[1] 0.3333333 0.3333333

[1] 0.3333333 0.3333333

[1] 0.3333333 0.3333333

#  NPT of node_three has been resized based on the number of parent node_one states
# NPT values for node_three are reset to discrete uniform
[1] "node_one" "node_two"

# node_two has been added to the parents list of node_three
[1] 0.3333333 0.3333333 0.3333333 0.3333333

[1] 0.3333333 0.3333333 0.3333333 0.3333333

[1] 0.3333333 0.3333333 0.3333333 0.3333333

#  NPT of node_three has been resized based on the number of parent node_one and node_two states
# NPT values for node_three are reset to discrete uniform

6.4 Creating and Modifying Networks

BN Models contain networks, at least one or optionally multiple. If there are multiple networks in a model, they can be linked to each other with the use of input and output nodes. A Network object in R represents a network in a BN model. To create a new Network object, you need to specify its id (mandatory parameter), and you can also fill in the optional parameters:

new("Network", id, name, description, nodes)

# id parameter is mandatory
# the rest is optional

Here clearly nodes field is the most important information for a network but you do not need to specify these on creation. You can choose to create an empty network and fill it in with the nodes afterwards with the use of add_node() function. Alternatively, if all (or some) of the nodes you will have in the network are already defined, you can pass them to the new Network object on creation.

Below is an example of network creation with the nodes added later:

network_one <- new("Network", id = "network_one")


Notice that when node_three is added to the network, its parents are not automatically included. So if a node has parents, you need to separately add them to the network, so that later on your model will not have discrepancies.

The order in which nodes are added to a network is not important as long as all parent-child nodes are eventually in the network.

Alternatively, you can create a new network with its nodes:

network_two <- new("Network", id = "network_two", nodes = c(node_one, node_two, node_three))

Or you can create the network with some nodes and add more nodes later on:

network_three <- new("Network", id = "network_three", nodes = c(node_one, node_three))


To remove a node from a network, you can use remove_node() function. Again keep in mind that removing a node does not automatically remove all of its parents from the network. For example,


To plot a network and see its graphical structure, you can use


6.5 Creating and Modifying the Model

BN models consist of networks, the links between networks, and datasets (scenarios). Only the networks information is mandatory to create a new Model object in R. The other fields can be filled in afterwards. The new model creation function is:

new("Model", id, networks, dataSets, networkLinks)

# networks parameter is mandatory
# the rest is optional

For example, you can create a model with the networks defined above:

example_model <- new("Model", networks = list(network_one))

Note that even when there is only one network in the model, the input has to be a list. Networks in a model can be modified with add_network() and remove_network() functions:


Network links between networks of the model can be added with the add_network_link() function. For example:

example_model$add_network_link(source_network = network_one, source_node = node_three, target_network = network_two, target_node = node_three, link_type = "Marginals")

For link_type options and allowed network link rules, see add_network_link() section.

When a new model is created, it comes with a single dataset (scenario) by default. See next section to see how to add observations to this dataset (scenario) or add new datasets (scenarios).

6.6 Creating Datasets (Scenarios) and Entering Observation

To enter observations to a Model (which by default has one single scenario), use the enter_observation() function. You need to specify the node (and the network it belongs to) and give the value (one of the states if it’s a discrete node, a sensible numerical value if it’s a continuous node):

example_model$enter_observation(node = node_three, network = network_one, value = "High")

Note that this function did not specify any dataset (scenario). If this is the case, observation is always entered to the first (default) scenario.

You may choose to add more datasets (scenarios) to the model with the create_dataSet() function:

example_model$create_dataSet("Scenario 2")

Once added, you can enter observation to the new dataset (scenario) if you specify the dataSet parameter in the enter_observation() function:

example_model$enter_observation(dataSet = "Scenario 2", node = node_three, network = network_one, value = "Medium")

6.7. Exporting a Model to .cmpx or .json

Once an R model is defined fully and it is ready, you can export it to a .cpmx or a .json file. The function to create these files convert the information to the correct format for to understand. You can use either of the functions:




If left blank, these functions will create a file named after the Model$id with the correct extension. You may choose to name the file at the creation:


7. Creating Batch Cases for a Model in R

R-Agena environment allows creation of batch cases based on a single model and multiple observation sets. Observations should be provided in a CSV file with the correct format for the model. In this CSV file, each row of the data is a single case (dataset) with a set of observed values for nodes in the model. First column of the CSV file is the dataset (scenario) ids which will be used to create a new risk scenario for each data row. All other columns are possible evidence variables whose headers follow the “node_id.network_id” format. Thus, each column represents a node in the BN and is defined by the node id and the id of the network to which it belongs.

An example CSV format is as below:

Case node_one.network_one node_two.network_one cont_node.network_one node_one.network_two node_two.network_two
1 Negative True 20 Negative False
2 Positive
True Negative True
3 Positive False 18 Positive

Once the model is defined in R-Agena and the CSV file with the observations is prepared, you can use the create_batch_cases() function to generate scenarios for the BN:

create_batch_cases(inputModel, inputData)

where inputModel is a Model object and inputData is the path to the CSV file with the correct format. For example,

create_batch_cases(example_model, "example_dataset.csv")

This will create new datasets (scenarios) for each row of the dataset in the model, fill these datasets (scenarios) in with the observations using the values given in the dataset, create a new .json file for the model with all the datasets (scenarios). If there are NA values in the dataset, it will not fill in any observation for that specific node in that specific dataset (scenario).

Important note: Once the function has generated the .json file with all the new datasets (scenarios), it will remove the new datasets (scenarios) from the model. This function does not permanently update the model with the datasets (scenarios), it generates a .json model output with the observed datasets (scenarios) for the BN. It also does not alter already existing datasets (scenarios) in the Model object if there are any.

Assume that you use a model in R with two already existing datasets: an empty default “Scenario 1” which was created with the model, and a dataset (scenario) you have added “Test patient” with some observations. And you have a CSV file with 10 rows of data, whose Case column reads: “Patient 1, Patient 2, …, Patient 10”, with the set of observations for 10 patients. Once create_batch_cases() is used, it’s going to generate a .json file for this model with all 12 datasets (scenarios), but after the use of the function, the model will still have only “Scenario 1” and “Test patient” datasets (scenarios) in its $dataSets field.

8. Cloud with R-Agena

You can use R-Agena environment to authenticate with Cloud (using your existing account) and send your model files to Cloud for calculations. The connection between your local R-Agena environment and Cloud servers is based on the httr package in R.

8.1 Authentication

login() function is used to authenticate the user. To create an account, visit Once created, you can use your credentials in R-Agena to access the servers.

example_login <- login(username, password)

This will send a POST request to authentication server, and will return the login object (including access and refresh tokens) which will be used to authenticate further operations.

8.2 Model Calculation

calculate() function is used to send an R model object to Cloud servers for calculation. The function takes the following parameters:

Currently servers accept a single set of observations for each calculation, if the R model has multiple datasets (scenarios), you need to specify which dataset is to be used.

For example,

calculate(example_model, example_login)


calculate(example_model, example_login, dataSet_id)

If calculation is successful, this function will update the R model (the relevant dataSets$results field in the model) with results of the calculation.

The model calculation computation supports asynchronous (polling) request if the computation job takes longer than 10 seconds. The R client will periodically recheck the servers and obtain the results once the computation is finished (or timed out, whichever comes first).

If you would like to see the calculation results in a .csv format, you can use the Model method get_results() to generate the output file.

get_results() is a method for the R Model objects, and it creates a .csv output with all calculated marginal posterior probabilities in the model. To use the function,


or with a custom file name:


This will generate a .csv file with the following format:

Scenario Network Node State Probability
Scenario 1 Network 1 Node 1 State 1 0.2
Scenario 1 Network 1 Node 1 State 2 0.3
Scenario 1 Network 1 Node 1 State 3 0.5
Scenario 1 Network 1 Node 2 State 1 0.3
Scenario 1 Network 1 Node 2 State 2 0.7
Scenario 1 Network 1 Node 3 State 1 0.1
Scenario 1 Network 1 Node 3 State 2 0.8
Scenario 1 Network 1 Node 3 State 3 0.1

8.3 Sensitivity Analysis

For the sensitivity analysis, first you need to crate a sensivity configuration object, using the create_sensitivity_config(...) function. For example,

example_sens_config <- create_sensitivity_config(
                      target = "node_one",
                      sensitivity_nodes = c("node_two","node_three"),
                      report_settings = list(summaryStats = c("mean", "variance")),
                      dataset = "dataSet_id",
                      network = "network_one")

Using this config object, now you can use the sensitivity_analysis() function to send the request to the server. For example,

sensitivity_analysis(example_model, test_login, example_sens_config)

This will return a spreadsheet of tables and a json file for the results. The spreadsheet contains sensitivity analysis results and probability values for each sensitivity node defined in the configuration. The results json file contains raw results data for all analysis report options defined, such as tables, tornado graphs, and curve graphs.

The sensitivity analysis computation supports asynchronous (polling) request if the computation job takes longer than 10 seconds. The R client will periodically recheck the servers and obtain the results once the computation is finished (or timed out, whichever comes first).

9. Local API with R-Agena has a Java based API to be used with developer license. If you have the developer license, you can use the local API for calculations in addition to modeller. The local API has Java and maven dependencies, which you can see on its github page in full detail. R-Agena has communication with the local agena developer API.

To manually set up the local agena developer API, follow the instructions on the github page for the API:

For the API setup, in the R environment you can use


to clone the git repository of the API in your working directory.

Once the API is cloned, you can compile maven environment with:


and if needed, activate your developer license with


passing on your developer license key as the parameter.

!! Note that when there is a new version of the agena developer API, you need to re-run local_api_compile() function to update the local repository.

Once the local API is compiled and developer license is activated, you can use the local API directly with your models defined in R. To use the local API for calculations of a model created in R:

local_api_calculate(model, dataSet, output)

where the parameter model is an R Model object, dataSet is the id of one of the dataSets existing in the Model object, and output is the desired name of the output file to be generated with the result values. Note that output is just the file name and not the absolute path. For example,

local_api_calculate(model = example_model,
                    dataSet = example_dataset_id,
                    output = "exampe_results.json")

This function will create the .cmpx file for the model and the separate .json file required for the dataSet, and send them to the local API (cloned and compiled within the working directory), obtain the calculation result values and create the output file in the working directory, and remove the model and dataSet files used for calculation from the directory. The function also updates the R Model object with the calculation results (in addition to creating the separate results.json file in the directory).

If you’d like to run multiple dataSets in the same model in batch, you can use local_api_batch_calculate() instead. This function takes an R Model object as input and runs the calculation for each dataSet in it, and fills in all the relevant result fields under each dataSet. You can use this function as

local_api_batch_calculate(model = example_model)

where example_model is an R Model object with multiple dataSets.

You can also run a sensitivity analysis in the local API, using

local_api_sensitivity(model, sens_config, output)

Here the sens_config is created by the use of create_sensitivity_config(...). For example:

local_api_sensitivity(model = example_model,
                      sens_config = example_sensitivity_config,
                      output = "example_sa_results.json")

This function will create the .cmpx file for the model and the separate .json files required for the dataSet and sensitivity analysis configuration file, and send them to the local API (cloned and compiled within the working directory), obtain the sensitivity analysis result values and create the output file in the working directory, and remove the model, dataSet and config files used for sensitivity analysis from the directory. local_api_sensitivity() looks at the dataSet field of sens_config to determine which dataSet to use, if the field doesn’t exist, the default behaviour is to create a new dataSet without any observations for the sensitivity analysis.

10. R-Agena Use Case Examples

In this section, some use case examples of R-Agena environment are shown.

101. Asia Model

This is a BN which calculates the risk of certain medical conditions such as tuberculosis, lung cancer, and bronchitis from two casual factors - smoking and whether the patient has been to Asia recently. Additionally two other pieces of evidence are available: whether the patient is suffering from dyspnoea (shortness of breath) and whether a positive or negative X-ray test result is available.

We can start with creating all the nodes in the model:

A <- new("Node", id="A", name="Visit to Asia?")
S <- new("Node", id="S", name="Smoker?")

TB <- new("Node", id="T", name="Has tuberculosis")
L <- new("Node", id="L", name="Has lung cancer")
B <- new("Node", id="B", name="Has bronchitis")

TBoC <- new("Node", id="TBoC", name="Tuberculosis or cancer")

X <- new("Node", id="X", name="Positive X-ray?")
D <- new("Node", id="D", name="Dyspnoea?")

All the nodes are binary so we do not need to specify the type or states. Then we can add the edges between nodes, by adding relevant nodes as parents to the child nodes:


Now we can set the NPT values for all the nodes:

A$set_probabilities(list(0.99, 0.01))
TB$set_probabilities(list(c(0.99,0.01),c(0.95,0.05)),by_rows = FALSE)
L$set_probabilities(list(c(0.9,0.1),c(0.99,0.01)),by_rows = FALSE)
B$set_probabilities(list(c(0.7,0.3), c(0.4,0.6)),by_rows = FALSE)
TBoC$set_probabilities(list(c(1,0),c(0,1),c(0,1),c(0,1)),by_rows = FALSE)
X$set_probabilities(list(c(0.95,0.05), c(0.02,0.98)),by_rows = FALSE)
D$set_probabilities(list(c(0.9,0.1),c(0.2,0.8),c(0.3,0.7),c(0.1,0.9)),by_rows = FALSE)

Now we create a network with all the nodes, and a model with the network:

asia_net = new("Network", id="asia_net", nodes=c(A,S,TB,L,B,TBoC,X,D))
asia_model = new("Model", networks = list(asia_net))

Now we can choose to use the model in any possible way: exporting to a .cmpx file for modeller, sending it to cloud, or sending it to the local developer API for calculations. For example:


10.2 Diet Experiment Model

This is a BN which uses experiment observations to estimate the parameters of a distribution. In the model structure, there are nodes for the parameters which are the underlying parameters for all the experiments and the observed values inform us about the values for these parameters. The model in Modeller is given below:

Diet Experiment Image

In this section we will create this model entirely in RAgena environment. We can start with creating first four nodes.

Mean and variance nodes:


#First we create the "mean" and "variance" nodes

mean <- new("Node", id = "mean", simulated = TRUE)

variance <- new("Node", id = "variance", simulated = TRUE)

Common variance and tau nodes:

#Now we create the "common variance" and its "tau" parameter nodes

tau <- new("Node", id = "tau", simulated = TRUE)

common_var <- new("Node", id = "common_var", name = "common variance", simulated = TRUE)

Now we can create the four mean nodes, using a for loop and list of Nodes:

#Creating a list of four mean nodes, "mean A", "mean B", "mean C", and "mean D"

mean_names <- c("A", "B", "C", "D")
means_list <- vector(mode = "list", length = length(mean_names))

for (i in seq_along(mean_names)) {
  node_id <- paste0("mean",mean_names[i])
  node_name <- paste("mean",mean_names[[i]])
  means_list[[i]] <- new("Node", id = node_id, name = node_name, simulated = TRUE)

Now we can create the experiment nodes, based on the number of observations which will be entered:

# Defining the list of observations for the experiment nodes
# and creating the experiment nodes y11, y12, ..., y47, y48

observations <- list(c(62, 60, 63, 59),
                     c(63, 67, 71, 64, 65, 66),
                     c(68, 66, 71, 67, 68, 68),
                     c(56, 62, 60, 61, 63, 64, 63, 59))

obs_nodes_list <- vector(mode = "list", length = length(mean_names))
for (i in seq_along(obs_nodes_list)) {
  obs_nodes_list[[i]] <- vector(mode = "list", length = length(observations[[i]]))
  this_mean_id <- means_list[[i]]$id
  for (j in seq_along(obs_nodes_list[[i]])) {
    node_id <- paste0("y",i,j)
    obs_nodes_list[[i]][[j]] <- new("Node", id = node_id, simulated = TRUE)
    this_expression <- paste0("Normal(",this_mean_id,",common_var)")

We can create a network for all the nodes:

#Creating the network for all the nodes

diet_network <- new("Network", id = "Hierarchical_Normal_Model_1",
                            name = "Hierarchical Normal Model")

And add all the nodes to this network. First eight nodes:

# Adding first eight nodes to the network

for (nd in c(mean, variance, tau, common_var, means_list)) {

Then adding all the experiment nodes:

# Adding all the experiment nodes to the network

for (nds in obs_nodes_list) {
  for (nd in nds) {

Now we can create a model with this network:

# Creating a model with the network

diet_model <- new("Model", networks = list(diet_network),
                        id = "Diet_Experiment_Model")

We enter all the observation values to the nodes:

# Entering all the observations

for (i in seq_along(observations)) {
  for (j in seq_along(observations[[i]])) {
    this_node_id <- paste0("y",i,j)
    this_value <- observations[[i]][j]
    diet_model$enter_observation(node = this_node_id,
                                 network = diet_model$networks[[1]]$id,
                                 value = this_value)

Now the model is ready with all the information, we can export it to either a .json or a .cmpx file for calculations, either locally or on Cloud:

# Creating json or cmpx file for the model