LatentVariableRegression: Fit a latent variable linar multi-task model (Zhang et al....
In tohein/linearMTL: Linear Models for Multi-Task Learning

A Bayesian linear multi-task model where the regression matrix is assumed to be composed of latent factors.

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LatentVariableRegression(X = NULL, task.specific.features = list(), Y, S,
  max.iter = 10000, epsilon = 1e-05, XTX = NULL, XTY = NULL,
  verbose = 1)

`X`	N by J1 matrix of features common to all tasks.
`task.specific.features`	List of features which are specific to each task. Each entry contains an N by J2 matrix for one particular task (where columns are features). List has to be ordered according to the columns of Y.
`Y`	N by K output matrix for every task.
`S`	H by K loading matrix.
`max.iter`	(Optional) Maximum number of iterations.
`epsilon`	(Optional) Desired accuracy. If error change drops below epsilon, the algorithm terminates.
`XTX`	(Optional) Precomputed matrices t(X)*X as for example produced by PrepareMatrices.
`XTY`	(Optional) Precomputed matrices t(X)*Y as for example produced by PrepareMatrices
`verbose`	(Optional) Integer in 0,1,2. verbose = 0: No output. verbose = 1: Print summary at the end of the optimization. verbose = 2: Print progress during optimization.

List containing

`Gamma`	Estimated mixing matrix.
`sigma2`	Estimated sigma^2.
`Psi`	Estimated Psi.
`S`	Loading matrix used.
`B`	MAP estimate of the regression coefficients.
`early.termination`	Boolean indicating whether the algorithm exceeded max.iter iterations.