Description Usage Arguments Details Value See Also Examples

This function estimates the bipartite logitudinal influence network (BLIN) model *Y_t = A^T ∑_{k=1}^{lag} Y_{t-k} + ∑_{k=1}^{lag} Y_{t-k} B + X_t β + τ E_t* using maximum likelihood estimator.

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`Y` |
Response 3-mode array. |

`X` |
Optional 4-mode array of covariates, defaults to no covariates. |

`type` |
Optional string specifying BLIN model type: full, reduced_rank, or sparse. Defaults to full. |

`lag` |
Optional numeric specifying autoregressive lag in model, defaults to 1. |

`rankA` |
Optional numeric rank of influence network matrix |

`rankB` |
Optional numeric rank of influence network matrix |

`maxit` |
Optional numeric maximum number of iterations for full and reduced rank block coordinate descents, defaults to 1e3. |

`tol` |
Optional numeric convergence tolerance for full and reduced rank block coordinate descents, defaults to 1e-8. |

`init` |
Optional string specifying initialization type for full and reduced rank block coordinate descents, defaults to "I", identity for |

`sigma_init` |
Optional numeric standard deviation for random initialization of |

`verbose` |
Optional logical specifying whether progress should be printed out ( |

`calcses` |
Optional logical specifying whether standard errors should be calculated ( |

`randseed` |
Optional numeric specifying seed for random ininitialization of |

This function estimates the continuous BLIN model,

*Y_t = A^T Y_{t-1} + Y_{t-1} B + X_t β + τ E_t*

, where * \{ Y_t\}_t * is a set of *S \times L* matrices representing the bipartite relation data at each observation *t*.
The set *\{X_t \}_t* is a set of *S \times L \times p* arrays describing the influence of the
coefficient vector *beta*. Finally, each matrix *E_t* is assumed to consist of iid standard normal random variables. The matrices *A* and *B* are square matrices respesenting the influence networks among *S* senders and *L* receivers, respectively.

This function estimates the BLIN model using maximum likelihood (and related) methods. The "full" model places no restrications on the influence networks *A* and *B*, and estimates
these matrices (along with *β*) by block coordinate descent. In addition, if `calcses==TRUE`

, the standard errors for each coefficient will be estimated. Note that the standard error procedure
may require large amounts of memory to build the BLIN design matrix; a warning is produced if the estimated size of the desgn is greater than 0.5GB.

The "reduced rank" BLIN model assumes that the matrix *A* has decomposition *A = UV^T*, where each of *U* and *V* is an *S \times \code{rankA}* matrix, and
the matrix *B* has decomposition *B = WZ^T*, where each of *W* and *Z* is an *L \times \code{rankB}* matrix. This model is also estimated using block coordinate descent.

Finally, the "sparse" BLIN model assumes that *A* and *B* matrices have many entries that are small or zero. The `cv.glmnet(.)`

function from the `glmnet`

package is used
to estimate the entries in *A*, *B*, and *beta*. The object resuling from `cv.glmnet(.)`

is returned in this case.

Notice that the diagonals of *A* and *B* are not identifiable. However, the sum of each diagonal entry in *A* and *B*, i.e. *a_{ii} + b_{jj}*, is identifiable. Thus,
the diagonal sums are broken out as separate estimates under the name `diagAB`

.

If `calcses = TRUE`

and `type = full`

, then standard errors will be returned. These standard errors are based on the assumption that each *E_t* consists of iid standard normal random variables.
In this case, the full design matrix is built, which we call *W* here. Then, the variance-covariance matrix of the estimated coefficients is formed by *\hat{τ}^2 (W^T W)^{-1}*, where *\hat{τ}^2* is the usual unbiased estimator of the error variance.

`fit` |
A |

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