Functions for fitting Bayesian dynamic factor analyses
fit_dfa()
Fit a Bayesian DFA
sim_dfa()
Simulate from a DFA
find_dfa_trends()
Find the best number of trends according to LOOIC
dfa_cv()
Apply cross validation to DFA model
Functions for evaluating convergence of DFA moddels
find_swans()
Find outlying "black swan" jumps in trends
is_converged()
Summarize Rhat convergence statistics across parameters
loo(<bayesdfa>)
LOO information criteria
predicted()
Calculate predicted value from DFA object
rotate_trends()
Rotate the trends from a DFA
trend_cor()
Estimate the correlation between a DFA trend and some other timeseries
Functions for extracting common outputs
dfa_fitted()
Get the fitted values from a DFA as a data frame
dfa_loadings()
Get the loadings from a DFA as a data frame
dfa_trends()
Get the trends from a DFA as a data frame
Functions for evaluating regimes with univariate HMMs
hmm_init()
Create initial values for the HMM model.
find_regimes()
Fit multiple models with differing numbers of regimes to trend data
fit_regimes()
Fit models with differing numbers of regimes to trend data
Functions for plotting DFA and HMM models
plot_fitted()
Plot the fitted values from a DFA
plot_loadings()
Plot the loadings from a DFA
plot_regime_model()
Plot the state probabilities from find_regimes()
plot_trends()
Plot the trends from a DFA