Package: proclhmm 1.0.0
proclhmm: Latent Hidden Markov Models for Response Process Data
Provides functions for simulating from and fitting the latent hidden Markov models for response process data (Tang, 2024) <doi:10.1007/s11336-023-09938-1>. It also includes functions for simulating from and fitting ordinary hidden Markov models.
Authors:
proclhmm_1.0.0.tar.gz
proclhmm_1.0.0.zip(r-4.5)proclhmm_1.0.0.zip(r-4.4)proclhmm_1.0.0.zip(r-4.3)
proclhmm_1.0.0.tgz(r-4.5-x86_64)proclhmm_1.0.0.tgz(r-4.5-arm64)proclhmm_1.0.0.tgz(r-4.4-x86_64)proclhmm_1.0.0.tgz(r-4.4-arm64)proclhmm_1.0.0.tgz(r-4.3-x86_64)proclhmm_1.0.0.tgz(r-4.3-arm64)
proclhmm_1.0.0.tar.gz(r-4.5-noble)proclhmm_1.0.0.tar.gz(r-4.4-noble)
proclhmm_1.0.0.tgz(r-4.4-emscripten)proclhmm_1.0.0.tgz(r-4.3-emscripten)
proclhmm.pdf |proclhmm.html✨
proclhmm/json (API)
NEWS
| # Install 'proclhmm' in R: |
| install.packages('proclhmm', repos = c('https://xytangtang.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/xytangtang/proclhmm/issues
Last updated 9 months agofrom:b7dff6b900. Checks:7 OK, 4 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 03 2025 |
| R-4.5-win-x86_64 | NOTE | Mar 03 2025 |
| R-4.5-mac-x86_64 | NOTE | Mar 03 2025 |
| R-4.5-mac-aarch64 | NOTE | Mar 03 2025 |
| R-4.5-linux-x86_64 | NOTE | Mar 03 2025 |
| R-4.4-win-x86_64 | OK | Mar 03 2025 |
| R-4.4-mac-x86_64 | OK | Mar 03 2025 |
| R-4.4-mac-aarch64 | OK | Mar 03 2025 |
| R-4.3-win-x86_64 | OK | Mar 03 2025 |
| R-4.3-mac-x86_64 | OK | Mar 03 2025 |
| R-4.3-mac-aarch64 | OK | Mar 03 2025 |
Exports:compute_P1_lhmmcompute_paras_hmmcompute_PQ_lhmmcompute_thetafind_state_seqhmmlhmmsim_hmmsim_hmm_parassim_lhmmsim_lhmm_paras
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute LHMM probabilities from parameters | compute_P1_lhmm |
| Compute probabilities from logit scale parameters in HMM | compute_paras_hmm |
| Compute LHMM probabilities from parameters | compute_PQ_lhmm |
| Estimate latent traits in LHMM | compute_theta |
| Viterbi algorithm for HMM | find_state_seq |
| MMLE of HMM | hmm |
| MMLE of LHMM | lhmm |
| proclhmm: Latent Hidden Markov Models for Response Process Data | proclhmm |
| Simulating action sequences using HMM | sim_hmm |
| generate HMM parameters | sim_hmm_paras |
| Simulating action sequences using LHMM | sim_lhmm |
| generate LHMM parameters | sim_lhmm_paras |
