Mstep_kernel: M step for kernel
In ppsbm: Clustering in Longitudinal Networks
Description
Usage
Arguments
References
Description
M step for kernel estimator
Usage
1
Mstep_kernel(data, VE, directed)
Arguments
data
Data same of mainVEM
VE
Results of the previous VE for iterative computation
directed
Boolean for directed (TRUE) or undirected (FALSE) case
References
GR<c3><89>GOIRE , G. (1993). Least squares cross-validation for counting process intensities. Scand. J. Statist. 20, pp. 343<e2><80><93>360.
MATIAS, C., REBAFKA, T. & VILLERS, F. (2018). A semiparametric extension of the stochastic block model for longitudinal networks. Biometrika.
RAMLAU-HANSEN, H. (1983). Smoothing counting process intensities by means of kernel functions. Ann. Statist. 11, pp. 453<e2><80><93>466.
ppsbm documentation built on May 1, 2019, 11:26 p.m.
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Description Usage Arguments References
Description
M step for kernel estimator
Usage
1 | Mstep_kernel(data, VE, directed)
|
Arguments
data |
Data same of mainVEM |
VE |
Results of the previous VE for iterative computation |
directed |
Boolean for directed (TRUE) or undirected (FALSE) case |
References
GR<c3><89>GOIRE , G. (1993). Least squares cross-validation for counting process intensities. Scand. J. Statist. 20, pp. 343<e2><80><93>360.
MATIAS, C., REBAFKA, T. & VILLERS, F. (2018). A semiparametric extension of the stochastic block model for longitudinal networks. Biometrika.
RAMLAU-HANSEN, H. (1983). Smoothing counting process intensities by means of kernel functions. Ann. Statist. 11, pp. 453<e2><80><93>466.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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