tflash: Tensor Factor Loading Adaptive SHrinkage (T-FLASH).
In kkdey/flashr: Factor Loading Adaptive SHrinkage in R

Description Usage Arguments Author(s)

Fits a rank-1 tensor mean model with a homoscedastic error term. It does this via a variational Bayesian approach with a unimodal prior on the components of the mean tensor.

tflash(Y, var_type = c("homoscedastic", "kronecker"), tol = 10^-5,
  itermax = 100, alpha = 0, beta = 0, mixcompdist = "normal",
  sig_start_itermax = 10, nullweight = 10, print_update = FALSE,
  start = c("first_sv", "random"), known_factors = NULL,
  known_modes = NULL, homo_modes = NULL)

`Y`	An array of numerics. The data.
`var_type`	A string. What variance model should we assume? Options are homoscedastic noise (`"homoscedastic"`) or Kronecker structured variance (`kronecker`).
`tol`	A positive numeric. The stopping criterion for the VEM.
`itermax`	A positive integer. The maximum number of iterations to run the VEM
`alpha`	A non-negative numeric. The prior shape parameter for the variance. Defaults to zero.
`beta`	A non-negative numeric. The prior rate parameter for the variance. Defaults to zero.
`mixcompdist`	The mixing distribution to assume. Defaults to normal. Options are those available in the `ashr` package.
`sig_start_itermax`	A positive integer. The number of iterations to run in initializing the precision before starting the VEM. Defaults to 10.
`nullweight`	A numeric greater than or equal to 1. The penalty term on the probability of zero.
`print_update`	A logical. Should we print notifications on how far along the optimization is?
`start`	How should we choose the starting values? Either using the first singular vector along each mode (`"first_sv"`) or randomly (`"random"`).
`known_factors`	A list of known factors for the modes indicated in `known_modes`. Defaults to `NULL`, where all factors are assumed to be unknown.
`known_modes`	A vector of integers. The modes that are known. Should be the same length as `known_factors`.
`homo_modes`	A vector of integers. If `var_type = "kronecker"` then `homo_modes` indicates which modes are assumed to be homoscedastic.