LMest3.2.8 package

Parametric bootstrap for the basic LM model for continuous outcomes

Parametric bootstrap for the basic LM model

Parametric bootstrap for LM models for continuous outcomes with indivi...

Parametric bootstrap for LM models with individual covariates in the l...

Parametric bootstrap

Perform local and global decoding

Draw samples from the basic LM model for continuous outcomes

Draw samples from the basic LM model

Draw samples from LM model for continuous outcomes with covariaates in...

Draw samples from LM model with covariaates in the latent model

Draws samples from the mixed LM model

Draw simulated sample from a Generalized Latent Markov Model

Estimate basic LM model for continuous outcomes

Estimate basic LM model

Estimate LM model for continuous outcomes with covariates in the laten...

Estimate LM model with covariates in the latent model

Estimate LM model with covariates in the measurement model

Estimate mixed LM model

Estimate basic Markov chain (MC) model

Estimate Markov chain (MC) model with covariates

Class 'LMbasic'

Class 'LMbasiccont'

Overview of the Package LMest

Estimate Latent Markov models for categorical responses

Estimate Latent Markov models for continuous responses

Data for LMest functions

Perform local and global decoding

Formulas for LMest functions

Estimate Markov Chain models

Estimate mixed Latent Markov models

Search for the global maximum of the log-likelihood

Class 'LMlatent'

Class 'LMlatentcont'

Class 'LMmanifest'

Class 'LMmanifestcont'

Class 'LMmixed'

From data in the long format to data in array format

From data in the long format to data in the wide format

From data in array format to data in long format

Class 'MCbasic'

Class 'MCcov'

Plots for Generalized Latent Markov Models

Print the output

Standard errors

Search for the global maximum of the log-likelihood

Summary and plot of lmestData

Summary of LM fits