| LMest-package | Fit latent Markov models |
| blkdiag | Build a block diagonal matrix. |
| bootstrap_lm_basic | Parametric bootstrap for the basic LM model |
| bootstrap_lm_cov_latent | Parametric bootstrap for LM models with individual covariates in the latent model |
| complk | Complete log-likelihood of the basic latent Markov model |
| data_criminal_sim | Criminal dataset |
| data_drug | Dataset about marijuana consumption |
| data_SRHS_long | Self-reported health status dataset |
| decoding | Perform local and global decoding |
| draw_lm_basic | Draw samples from the basic LM model |
| draw_lm_cov_latent | Draw samples from LM model with covariaates in the latent model |
| draw_lm_mixed | Draws samples from the mixed LM model |
| est_lm_basic | Estimate basic LM model |
| est_lm_cov_latent | Estimate LM model with covariates in the latent model |
| est_lm_cov_manifest | Estimate LM model with covariates in the measurement model |
| est_lm_mixed | Estimate mixed LM model |
| est_multilogit | Estimate multilogit model |
| expit | Compute the expit function. |
| expit1 | Compute the expit function with respect to a reference category. |
| invglob | Invert vector of global logits. |
| lk_ar_rho | Compute complete log-likelihood for AR(1) latent process |
| lk_comp_latent | Complete log-likelihood of the latent Markov model with covariates |
| lk_obs | Compute the observable log-likelihood of the basic LM model |
| lk_obs_latent | Compute the observable log-likelihood of the LM model with covariates in the latent model |
| lk_obs_manifest | Compute the observable log-likelihood of the LM model with covariates in the measurement model |
| lk_obs_mixed | Compute the observable log-likelihood of the mixed LM model |
| lk_sta | Compute the stationary log-likelihood |
| LMest | Fit latent Markov models |
| logit1 | Compute the logit function with respect to a reference category. |
| long2matrices | From data in the long format to data in array format |
| long2wide | From data in the long format to data in the wide format |
| marg_param | Compute marginal parametrization |
| print.LMbasic | Print the output of LMbasic object |
| print.LMlatent | Print the output of LMlatent object |
| print.LMmanifest | Print the output of LMmanifest object |
| print.LMmixed | Print the output of LMmixed object |
| print.LMsearch | Print the output of LMsearch object |
| prob_multilogit | Compute multinomial probabilities |
| prob_post_cov | Compute posterior probabilities. |
| prod_array | Compute the product of array and vector |
| rec1 | Recursions used by est_lm_cov_manifest |
| rec3 | Recursions used by est_lm_cov_manifest |
| recursions | Recursions used by est_lm_basic |
| RLMSdat | Dataset about job satisfaction |
| search.model.LM | Search for the global maximum of the log-likelihood |
| sq | Create a matrix with the combination of vectors of (1,0) |
| stationary | Stationary |
| summary.LMbasic | Print the output of LMbasic object |
| summary.LMlatent | Print the output of LMlatent object |
| summary.LMmanifest | Print the output of LMmanifest object |
| summary.LMmixed | Print the output of LMmixed object |
| summary.LMsearch | Print the output of LMsearch object |
| trans_par | Convert matrix parametrization |