Pattern Estimation and Identification for Dyadic Sequences Using Transition Matrices in R

Documentation for package ‘dyadicMarkov’ version 0.1.3

bivariateCase	Bivariate case identification for dyadic Markov chains
completePattern	Complete bivariate pattern identification by AIC
countEmp	Empirical transition counts for univariate dyadic sequences
countEmpBivariate	Empirical transition counts for bivariate dyadic sequences
data_complete_180	Sensitivity-analysis simulation datasets
data_complete_30	Sensitivity-analysis simulation datasets
data_complete_60	Sensitivity-analysis simulation datasets
data_complete_720	Sensitivity-analysis simulation datasets
data_complete_90	Sensitivity-analysis simulation datasets
dyadic_bivariate_example	Synthetic bivariate dyadic sequence example
dyadic_univariate_example	Synthetic univariate dyadic sequence example
mleEstimation	Maximum likelihood estimation of transition probabilities
partialPattern	Partial bivariate pattern identification by AIC
plot.dyadic_case	Plot dyadic categorical state strips
plot.dyadic_pattern	Plot dyadic categorical state strips
sensitivity_simulation_data	Sensitivity-analysis simulation datasets
univariatePattern	Univariate pattern identification for dyadic Markov chains