fit_sequence_hmm: Fit a categorical hidden Markov model

In gp3sequences: Transparent Analysis of Ordered Categorical Sequences

Fit a categorical hidden Markov model

Description

Fits a finite-state, time-homogeneous categorical HMM by Baum-Welch EM. Latent states are statistical model states only; they are not psychological, diagnostic, or causal constructs.

Usage

fit_sequence_hmm(
  data,
  n_states,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state",
  symbol_levels = NULL,
  state_names = NULL,
  initial_probs = NULL,
  transition_probs = NULL,
  emission_probs = NULL,
  max_iter = 200L,
  tolerance = 1e-06,
  pseudocount = 1e-06,
  seed = 1L,
  keep_posteriors = FALSE
)

Arguments

data	Long-format sequence data.
n_states	Number of latent states.
sequence_id_col, order_col, state_col	Sequence columns.
symbol_levels	Optional observed-symbol ordering.
state_names	Optional latent-state names.
initial_probs, transition_probs, emission_probs	Optional starting values.
max_iter	Maximum EM iterations.
tolerance	Relative log-likelihood tolerance.
pseudocount	Non-negative smoothing count.
seed	Reproducibility seed.
keep_posteriors	Retain final forward-backward results.

Value

An object of class gp3_sequence_hmm containing fitted parameters, log likelihood, convergence diagnostics, symbol coding, and optional posteriors.

Examples

sequences <- data.frame(
  sequence_id = rep(c("s1", "s2", "s3", "s4"), each = 4L),
  sequence_order = rep(1:4, times = 4L),
  state = c("A", "B", "C", "D", "A", "B", "C", "C",
            "D", "C", "B", "A", "D", "C", "A", "A"),
  group = rep(c("g1", "g2"), each = 8L),
  stringsAsFactors = FALSE
)
fit_sequence_hmm(sequences, n_states = 2L, max_iter = 5L, seed = 1L)

gp3sequences documentation built on Aug. 23, 2026, 5:10 p.m.