cmm_init: Initialize an LRMoE model based on Clustered Method of...
In UofTActuarial/LRMoE: Logit-Weighted Reduced Mixture-of-Experts

Initialize an LRMoE model based on Clustered Method of Moments

1	cmm_init(Y, X, n_comp, type, exact_Y = FALSE)

`Y`	A N by d (`exact_Y=T`) or N by 4d (`exact_Y=F`) matrix of numerics, where N is sample size and d is the dimension of each obsevation. If the size is N by 4d, Each block of four columns should be organized as `(tl, yl, yu, tu)`, representing the truncation lower bound, censoring lower bound, censoring upper bound and truncation upper bound.
`X`	X A N*P matrix of numerics, where P is the number of covariates. The first column of `X` should be 1, which is the intercept.
`n_comp`	Numeric, representing how many latent classes/groups to use.
`type`	A vector of strings of either "continuous" or "exact", representing whether each dimension of `Y` is continuous or discrete.
`exact_Y`	TRUE/FALSE: whether `Y` is observed exactly, or with censoring and/or truncation.

A list where zero_y, mean_y_pos, var_y_pos, skewness_y_pos and kurtosis_y_pos represent the summary statistics of Y by dimension and by component. alpha_init and experts_init represents the parameter initializations. ll_init contains the loglikelihood of each experts fitted to Y by dimension and by component. ll_best suggests an initialization of expert functions based on the best loglikelihood.