This function specifies the value of the `u_smooth,v_smooth`

arguments in the
`moma_*pca`

series of functions, and the `x_smooth,y_smooth`

arguments
in the `moma_*cca`

and `moma_*lda`

series of functions.

moma_smoothness(Omega = NULL, ..., alpha = 0, select_scheme = "g")

Omega | A matrix of appropriate size. A common choice is the second difference matrix.
See |
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... | Force users to specify arguments by names. |

alpha | A vector containing penalty values |

select_scheme | A char being either "b" (nested BIC search) or "g" (grid search). MoMA provides a flexible framework for regularized multivariate analysis
with several tuning parameters for different forms of regularization.
To assist the user in selecting these parameters ( To explain nested BIC search, we need to look into how the algorithm runs.
To find an (approximate) solution to a penalized SVD (Singular Value Decomposition) problem is to solve two
penalized regression problems iteratively. Let's call them problem u and problem v, which give
improving estimates of the right singular vector, The nested BIC search is essentially two 2-D searches. We start from SVD solutions, and then find the optimal
parameters for problem u, given current estimate of Users are welcome to refer to section 3.1: Selection of Regularization Parameters in the paper cited below. |