k_can: Class Number Candidates for Latent Class Mixed Effects Models
In wfmueller29/trajpkg: Latent Class Trajectory Functions

This function applies lcmem across a vector of class candidates defined by (1:max_k) and also allows the option to parallelize with the futures package

1	k_can(df, fixed, mixture, random, subject, max_k, idiag = FALSE, nwg = FALSE)

`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. 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`	a string that 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 ”.
`max_k`	the number of classes to apply the model structure
`idiag`	optional logical for the structure of the variance-covariance matrix of the random-effects. If FALSE, a non structured matrix of variance-covariance is considered (by default). If TRUE a diagonal matrix of variance-covariance is considered.
`nwg`	optional logical indicating if the variance-covariance of the random-effects is class-specific. If FALSE the variance-covariance matrix is common over latent classes (by default). If TRUE a class-specific proportional parameter multiplies the variance-covariance matrix in each class (the proportional parameter in the last latent class equals 1 to ensure identifiability).