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proclhmm: Latent Hidden Markov Models for Response Process Data

Description

This package provides functions for simulating from and fitting the latent hidden Markov models for response process data (Tang, 2024). It also includes functions for simulating from and fitting ordinary hidden Markov models.

Data Simulation Functions

• sim_hmm_paras generates parameters of HMM

• sim_hmm generates actions sequences from HMM.

• sim_lhmm_paras generates parameters of LHMM

• sim_lhmm generates actions sequences from LHMM.

Model Fitting Functions

• hmm fits HMM models. Parameters are estimated through marginalized maximum likelihood estimation.

• lhmm fits LHMM models. Parameters are estimated through marginalized maximum likelihood estimation.

• compute_theta compute MAP estimates of latent traits in LHMM.

• find_state_seq compute the most likely hidden state sequence.

Acknowledgment

The development of this package is supported by National Science Foundation grant DMS-2310664.

References

Tang, X. (2024) Latent Hidden Markov Models for Response Process Data. Psychometrika 89, 205-240. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11336-023-09938-1")}

proclhmm documentation built on June 22, 2024, 10:02 a.m.