gam_hist_ci: Plot histograms with confidence intervals
In simonecollier/lizardHMM: Fitting Lizard Movement Data with HMMs

This function plots the histograms for each subject and variable with the fitted state dependent distributions overlayed and their corresponding confidence intervals.

gam_hist_ci(
  x,
  viterbi,
  num_states,
  num_subjects,
  num_variables,
  hmm,
  state_dep_dist_pooled = FALSE,
  variable_names = c("Var 1", "Var 2", "Var 3"),
  subject_names = c("Subject 1", "Subject 2", "Subject 3", "Subject 4"),
  width = 1,
  n = 100,
  level = 0.975,
  x_step = 0.2
)

`x`	The data to be fit with an HMM in the form of a 3D array. The first index (row) corresponds to time, the second (column) to the variable number, and the third (matrix number) to the subject number.
`viterbi`	A matrix with each column indicating the sequence of states decoded by `gam_viterbi()` that is supposed to have generated the data of the subject in the corresponding column.
`num_states`	The number of states in the desired HMM.
`num_subjects`	The number of subjects/trials that generated the data.
`num_variables`	The number of variables in the data.
`hmm`	A list of parameters that specify the gamma HMM, including `num_states`, `num_variables`, `num_subjects`, `alpha`, `theta`, `gamma`, `delta`.
`state_dep_dist_pooled`	A logical variable indiacting whether the state dependent distribution parameters `alpha` and `theta` should be treated as equal for all subjects.
`variable_names`	A vector containing the names of the variables in the data `x`.
`subject_names`	A vector containing the names of the subjects generating the data `x`.
`width`	The width of the histogram bins.
`n`	The number of samples in the Monte Carlo fitting.
`level`	A number indicating the level of confidence for the desired interval.
`x_step`	A value indicating the step length for the range of observation values.