ran_refine: Random Effects Refinement for Latent Class Mixed Effects...
In wfmueller29/trajpkg: Latent Class Trajectory Functions

This function applies lcmem across a vector of random effects candidates. Each element is applied for the four comibination of nwg = T/F and idiag = T/F. It also allows the option to parallelize with the futures package

1	ran_refine(df, fixed, mixture, random_vect, subject, k)

`df`	data frame object with data for models
`fixed`	a string that represents a two-side linear formual object for the fixed effects in a linear mixed model. By default, an intercept is included. If no intercept, -1 should be the first term included on the right of ~.
`mixture`	a string that represents one-sided formula object for the class-specific fixed effects in the linear mixed model (to specify only for a number of latent classes greater than 1). Among the list of covariates included in fixed, the covariates with class-specific regression parameters are entered in mixture separated by +. By default, an intercept is included. If no intercept, -1 should be the first term included.
`random_vect`	a character vector where each element represents an optional one-sided formula for the random-effects in the linear mixed model. Covariates with a random-effect are separated by +. By default, an intercept is included. If no intercept, -1 should be the first term included.
`subject`	name of the covariate representing the grouping structure specified with ”.
`k`	integer specfiyin number of classes