additive_regression

<p>Fit sparse BLIN model
D is matrix of lagged Y (covariates)
Y is oservations
X is covariates
for multiple time periods
Returns A, B, beta, Yhat</p>

internal

Estimate influence networks from longitudinal bipartite relational data, where the longitudinal relations are continuous. The outputs are estimates of weighted influence networks among each actor type in the data set. The generative model is the Bipartite Longitudinal Influence Network (BLIN) model, a linear autoregressive model for these type of data. The supporting paper is ``Inferring Influence Networks from Longitudinal Bipartite Relational Data'', which is in preparation by the same authors. The model may be estimated using maximum likelihood methods and Bayesian methods. For more detail on methods, see Marrs et. al. <arXiv:1809.03439>.

Frank W. Marrs 

blin

Bipartite Longitudinal Influence Network (BLIN) Estimation

Benjamin W. Campbell 

Bailey K. Fosdick 

Skyler J. Cranmer 

Tobias B{"o}hmelt 

additive_regression function

Fit sparse BLIN model
D is matrix of lagged Y (covariates)
Y is oservations
X is covariates
for multiple time periods
Returns A, B, beta, Yhat — additive_regression

Fit sparse BLIN model
D is matrix of lagged Y (covariates)
Y is oservations
X is covariates
for multiple time periods
Returns A, B, beta, Yhat

additive_regression: Fit sparse BLIN model D is matrix of lagged Y (covariates) Y is oservations X is covariates for multiple time periods Returns A, B, beta, Yhat

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Arguments