BNRC is a network score based on B-spline nonparametric regression model. The score is developed by Prof. Seiya Imoto at University of Tokyo. For more details, see their paper: Imoto et al. (2002) Pac. Symp. Biocomput. 7, 175-186.

Parameters

M=n

The number of B-splines for nonparametric regression. By default, n=20.

max_parents=n

mp=n

Maximum parents

prop=n

Property output type. This changes properties to be stored in edges and nodes after the estimation.
0: standard.
1: less information (simple) for the contitous or discrete only variable data set.

hyper_bg=x

hb=x

hb value for the hyperparameter range. The hyperparameter β is determined by the grid search. The grid search starts from β = 10^hb and then decreases the value such as β = 10^{hb − (i × hi )}, where i = 1, 2, ...., hn. (default: x = 2.0)

hyper_inc=x

hi=x

hi value for hyperparameter range. See explanation of hyper_bg. (default: x = 0.4)

hyper_n=n

hn=n

hn value for hyperparameter range. See explanation of hyper_bg. (default: n = 21)

linear

Linear mode. This is short for "hb=2.0,hi=1.0,hn=2".

max_loops=n

The maximum number of loops for parameter estimation by the back fitting algorithm. (default: n = 100)

stop

If specified, the algorithm stops if the parameter estimation is not converged.

verbose=n

v=n

Verbose level. By defalt, n=0.

The following keys are used internally.

xl: DoubleArray instance that defines the minimum (left-most) values of the value ranges for modeling with B-splines.
xr: DoubleArray instance that defines the maximum (right-most) values of the value ranges for modeling with B-splines.

Network Scores | INGOR Manual