Man pages for LMest
Latent Markov Models with and without Covariates

blkdiagBuild a block diagonal matrix.
bootstrap_lm_basicParametric bootstrap for the basic LM model
bootstrap_lm_basic_contParametric bootstrap for the basic LM model for continuous...
bootstrap_lm_cov_latentParametric bootstrap for LM models with individual covariates...
bootstrap_lm_cov_latent_contParametric bootstrap for LM models for continuous outcomes...
complkComplete log-likelihood of the basic latent Markov model
complk_contComplete log-likelihood of the basic latent Markov model for...
data_criminal_simCriminal dataset
data_drugDataset about marijuana consumption
data_SRHS_longSelf-reported health status dataset
decodingPerform local and global decoding
draw_lm_basicDraw samples from the basic LM model
draw_lm_basic_contDraw samples from the basic LM model for continuous outcomes
draw_lm_cov_latentDraw samples from LM model with covariaates in the latent...
draw_lm_cov_latent_contDraw samples from LM model for continuous outcomes with...
draw_lm_mixedDraws samples from the mixed LM model
est_lm_basicEstimate basic LM model
est_lm_basic_contEstimate basic LM model for continuous outcomes
est_lm_cov_latentEstimate LM model with covariates in the latent model
est_lm_cov_latent_contEstimate LM model for continuous outcomes with covariates in...
est_lm_cov_manifestEstimate LM model with covariates in the measurement model
est_lm_mixedEstimate mixed LM model
est_mc_basicEstimate basic Markov chain (MC) model
est_mc_cov_latentEstimate Markov chain (MC) model with covariates
est_multilogitEstimate multilogit model
expitCompute the expit function.
expit1Compute the expit function with respect to a reference...
invglobInvert vector of global logits.
lk_ar_rhoCompute complete log-likelihood for AR(1) latent process
lk_comp_latentComplete log-likelihood of the latent Markov model with...
lk_comp_latent_contComplete log-likelihood of the latent Markov model for...
lk_obsCompute the observable log-likelihood of the basic LM model
lk_obs_latentCompute the observable log-likelihood of the LM model with...
lk_obs_manifestCompute the observable log-likelihood of the LM model with...
lk_obs_mixedCompute the observable log-likelihood of the mixed LM model
lk_staCompute the stationary log-likelihood
LMest-packageFit latent Markov models
logit1Compute the logit function with respect to a reference...
long2matricesFrom data in the long format to data in array format
long2wideFrom data in the long format to data in the wide format
marg_paramCompute marginal parametrization
print.LMbasicPrint the output of LMbasic object
print.LMbasiccontPrint the output of LMbasiccont object
print.LMlatentPrint the output of LMlatent object
print.LMlatentcontPrint the output of LMlatentcont object
print.LMmanifestPrint the output of LMmanifest object
print.LMmixedPrint the output of LMmixed object
print.LMsearchPrint the output of LMsearch object
print.MCbasicPrint the output of MCbasic object
print.MClatentPrint the output of MClatent object
prob_multilogitCompute multinomial probabilities
prob_post_covCompute posterior probabilities.
prob_post_cov_contCompute posterior probabilities.
prod_arrayCompute the product of array and vector
rec1Recursions used by est_lm_cov_manifest
rec3Recursions used by est_lm_cov_manifest
recursionsRecursions used by est_lm_basic
RLMSdatDataset about job satisfaction
search.model.LMSearch for the global maximum of the log-likelihood
sqCreate a matrix with the combination of vectors of (1,0)
stationaryStationary
summary.LMbasicPrint the output of LMbasic object
summary.LMbasiccontPrint the output of LMbasiccont object
summary.LMlatentPrint the output of LMlatent object
summary.LMlatentcontPrint the output of LMlatentcont object
summary.LMmanifestPrint the output of LMmanifest object
summary.LMmixedPrint the output of LMmixed object
summary.LMsearchPrint the output of LMsearch object
summary.MCbasicPrint the output of MCbasic object
summary.MClatentPrint the output of MClatent object
trans_parConvert matrix parametrization
LMest documentation built on Sept. 11, 2018, 5:05 p.m.