% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sequence-latent-models.R
\name{fit_sequence_hmm}
\alias{fit_sequence_hmm}
\title{Fit a categorical hidden Markov model}
\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{
\item{data}{Long-format sequence data.}

\item{n_states}{Number of latent states.}

\item{sequence_id_col, order_col, state_col}{Sequence columns.}

\item{symbol_levels}{Optional observed-symbol ordering.}

\item{state_names}{Optional latent-state names.}

\item{initial_probs, transition_probs, emission_probs}{Optional starting values.}

\item{max_iter}{Maximum EM iterations.}

\item{tolerance}{Relative log-likelihood tolerance.}

\item{pseudocount}{Non-negative smoothing count.}

\item{seed}{Reproducibility seed.}

\item{keep_posteriors}{Retain final forward-backward results.}
}
\value{
An object of class \code{gp3_sequence_hmm} containing fitted parameters,
log likelihood, convergence diagnostics, symbol coding, and optional
posteriors.
}
\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.
}
\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)

}
