lcmem: More Generalized hlme
In wfmueller29/trajpkg: Latent Class Trajectory Functions

This function runs the hlme function from the lcmm package, with a few tweaks to make hlme more flexible for looping purposes. Fixed, mixture, and random parameters are strings. Ng (number of groups in hlme) is k in this function. If k > 1 and B is not supplied, lcmem will run hlme with k = 1, then use this model as B for the provided k. df is a rlang symbol instead of a dataframe object.

lcmem(
  data,
  fixed,
  mixture,
  random,
  subject,
  k,
  B = NULL,
  idiag = FALSE,
  nwg = FALSE,
  df_sym = NULL,
  silent = TRUE
)

`data`	the dataframe that we are using to create the model
`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`	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 ”.
`k`	optional number of latent classes considered. If k=1 (by default) no mixture nor classmb should be specified. If k>1, mixture is required.
`B`	optional specification for the initial values for the parameters. Three options are allowed: (1) a vector of initial values is entered (the order in which the parameters are included is detailed in details section). (2) nothing is specified. A preliminary analysis involving the estimation of a standard linear mixed model is performed to choose initial values. (3) when ng>1, a hlme object is entered. It should correspond to the exact same structure of model but with ng=1. The program will automatically generate initial values from this model. This specification avoids the preliminary analysis indicated in (2). Note that due to possible local maxima, the B vector should be specified and several different starting points should be tried.
`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).
`df_sym`	the symbol of that dtaframe given by the data param in the global env.