Bayesian Dynamic Factor Analysis (DFA) with 'Stan'


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Documentation for package ‘bayesdfa’ version 1.2.0

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bayesdfa-package The 'bayesdfa' package.
bayesdfa The 'bayesdfa' package.
dfa_cv Apply cross validation to DFA model
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
find_dfa_trends Find the best number of trends according to LOOIC
find_inverted_chains Find which chains to invert
find_regimes Fit multiple models with differing numbers of regimes to trend data
find_swans Find outlying "black swan" jumps in trends
fit_dfa Fit a Bayesian DFA
fit_regimes Fit models with differing numbers of regimes to trend data
hmm_init Create initial values for the HMM model.
invert_chains Invert chains
is_converged Summarize Rhat convergence statistics across parameters
loo LOO information criteria
loo.bayesdfa LOO information criteria
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
predicted Calculate predicted value from DFA object
rotate_trends Rotate the trends from a DFA
sim_dfa Simulate from a DFA
trend_cor Estimate the correlation between a DFA trend and some other timeseries