# cnNew: New catNetwork In sdnet: Soft-Discretization-Based Bayesian Network Inference

## Description

Creates a new `catNetwork` with specified nodes, categories, parent sets and probability structure.

## Usage

 `1` ```cnNew(nodes, cats, pars, probs=NULL, p.delta1=0.01, p.delta2=0.01, dagonly=FALSE) ```

## Arguments

 `nodes` a `vector` of nodes names `cats` a `list` of node categories `pars` a `list` of node pars `probs` a `list` of probabilities `p.delta1` a `numeric` `p.delta2` a `numeric` `dagonly` a `logical`, selects between catNetwork and DAG

## Details

If `probs` is not specified, then a random probability model is assigned with conditional probability values in the union of the intervals [p.delta1, 0.5-p.delta2] and [0.5+p.delta2, 1-p.delta1]. Because of the nested list hierarchy of the probability structure, specifying the probability argument explicitly can be very elaborated task for large networks. In the following example we create a small network with only three nodes. The first node has no pars and only its marginal distribution is given, `c(0.2,0.8)`. Note that all inner most vectors in the `probs` argument, such as `(0.4,0.6)`, represent conditional distributions and thus sum to 1.

## Value

A `catNetwork` object.

## Author(s)

N. Balov

`catNetwork-class`, `cnRandomCatnet`
 ```1 2 3 4 5 6 7 8 9``` ```cnet <- cnNew( nodes = c("a", "b", "c"), cats = list(c("1","2"), c("1","2"), c("1","2")), pars = list(NULL, c(1), c(1,2)), probs = list( c(0.2,0.8), list(c(0.6,0.4),c(0.4,0.6)), list(list(c(0.3,0.7),c(0.7,0.3)), list(c(0.9,0.1),c(0.1,0.9)))) ) ```