All functions
|
|
Bernoulli()
|
Bernoulli mass (univariate, discrete, bounded space) |
|
Beta()
|
Beta density (univariate, continuous, bounded space) |
|
Binomial()
|
Binomial mass (univariate, discrete, bounded space) |
|
Categorical()
|
Categorical mass (univariate, discrete, bounded space) |
|
Cauchy()
|
Cauchy density (univariate, continuous, unbounded space) |
|
CholeskyLKJCor()
|
Cholesky LKJ Correlation density (multivariate, continuous, bounded space, prior only) |
|
Density()
|
Create a representation of a probability mass or density function for a
continuous univariate random variable. |
|
Dirichlet()
|
Dirichlet density (multivariate, continuous, unbounded space) |
|
Exponential()
|
Exponential density (univariate, continuous, bounded space) |
|
GammaDensity()
|
Gamma density (univariate, continuous, bounded space) |
|
Gaussian()
|
Gaussian density (univariate, continuous, unbounded space) |
|
ImproperUniform()
|
Improper uniform prior density (prior only) |
|
InitialFixed()
|
Fixed initial probability vector |
|
InitialSoftmax()
|
Softmax initial probability density |
|
InverseWishart()
|
Inverse Wishart density (multivariate, continuous, bounded space, prior only) |
|
MVGaussian()
|
Multivariate Gaussian density (Multivariate, continuous, unbounded space) |
|
MVGaussianCholeskyCor()
|
Multivariate Gaussian density with Cholesky decomposition of the correlation matrix (Multivariate, continuous, unbounded space) |
|
MVStudent()
|
Multivariate Student density (Multivariate, continuous, unbounded space) |
|
Multinomial()
|
Multinomial mass (multivariate, discrete, bounded space) |
|
NegativeBinomial()
|
Negative Binomial mass (univariate, discrete, bounded space) |
|
NegativeBinomialLocation()
|
Negative Binomial mass in the mean value parametrization (univariate, discrete, bounded space) |
|
Poisson()
|
Poisson mass (univariate, discrete, bounded space) |
|
RegBernoulliLogit()
|
Bernoulli regression with logistic link density (univariate, discrete, binary space) |
|
RegBinomialLogit()
|
Binomial regression with logistic link density (univariate, discrete, binary space) |
|
RegBinomialProbit()
|
Binomial regression with probit link density (univariate, discrete, bounded space) |
|
RegCategoricalSoftmax()
|
Categorical regression with softmax link density (univariate, discrete, bounded space) |
|
RegGaussian()
|
Regression with Gaussian link density (univariate, continuous, unbounded space) |
|
Student()
|
Student density (univariate, continuous, unbounded space) |
|
TransitionFixed()
|
Fixed transition probability matrix |
|
TransitionSoftmax()
|
Softmax transition density |
|
Wishart()
|
Wishart density (multivariate, continuous, bounded space, prior only) |
|
browse_model()
|
Load the underlying Stan code into an IDE or browser. |
|
classify_alpha()
|
Classify observations based on filtered probabilities. |
|
classify_gamma()
|
Classify observations based on smoothed probabilities. |
|
classify_quantity()
|
Classify observations based on latent state probabilities. |
|
classify_zstar()
|
Assign the hidden states to the most likely path (zstar). |
|
compile()
|
Compile a specified model. |
|
draw_samples()
|
Draw samples from a specification. |
|
explain()
|
Create a user-friendly text describing the model. |
|
extract
|
Extract quantities from a model fitted with BayesHMM. |
|
extract_K()
|
Extract the number of hidden states K. |
|
extract_R()
|
Extract the dimension of the observation vector R. |
|
extract_T()
|
Extract the length of the time series T. |
|
extract_alpha()
|
Extract the estimates of the filtered probability (alpha). |
|
extract_best()
|
Return the optimization object for the run with the hightest posterior log density. |
|
extract_data()
|
Extract the dataset used to fit the model. |
|
extract_date()
|
Extract the date when the model was run. |
|
extract_diagnostics()
|
Extract MCMC convergence and posterior predictive diagnostics. |
|
extract_filename()
|
Extract the path to file with the underlying Stan code. |
|
extract_gamma()
|
Extract the estimates of the smoothed probability (gamma). |
|
extract_model()
|
Extract the underlying Stan code. |
|
extract_n_chains()
|
Extract the number of chains M. |
|
extract_n_iterations()
|
Extract the number of total iterations. |
|
extract_n_thin()
|
Extract the thinning periodicity. |
|
extract_n_warmup()
|
Extract the number of warmup iterations. |
|
extract_obs_parameters()
|
Extract the estimates of the observation model parameters. |
|
extract_parameters()
|
Extract the estimates of the model parameters (observation, transition, and initial models). |
|
extract_quantity()
|
Extract estimated quantities from fit objects. |
|
extract_sample_size()
|
Extract the number of iterations kept after warmup. |
|
extract_seed()
|
Extract the seed used to fit the model. |
|
extract_spec()
|
Extract the Specification object used to object the model. |
|
extract_stanmodel()
|
Extract the stanmodel object of the fitted object. |
|
extract_time()
|
Extract the time elapsed when fitting the model. |
|
extract_y()
|
Extract the obsevation matrix used to fit the model y |
|
extract_ypred()
|
Extract the sample of the observation variable drawn from the posterior predictive density (ypred). |
|
extract_ysim()
|
Extract the simulated sample of the observation variable (ysim). |
|
extract_zpred()
|
Extract the sample of the hidden state path drawn from the posterior predictive density (zpred). |
|
extract_zsim()
|
Extract the simulated sample of the state variable (zsim). |
|
extract_zstar()
|
Extract the estimates of the most likely hidden state (zstar). |
|
fit()
|
Fit a model by MCMC |
|
get_current_theme()
|
Return the current theme. |
|
get_default_theme()
|
Return the default theme. |
|
hmm()
|
Specify a Hidden Markov Model. |
|
modify_theme_entry()
|
Modifies an entry in the current theme. |
|
optimizing()
|
Fit a model by MAP. |
|
plot_ppredictive()
|
Plot samples drawn from the posterior predictive density. |
|
plot_series()
|
Plot the observation series along with many other customizable options. |
|
plot_state_probability()
|
Plot the estimated hidden path along with many other customizable options. |
|
`+`
|
Append two Density objects. |
|
posterior_intervals()
|
Return a function that computes the posterior intervals. |
|
posterior_mode()
|
Return the posterior mode of a vector. |
|
print(<Density>)
|
Print a description of a Density object in a human friendly format. |
|
print(<Optimization>)
|
Prints the result of the model in a human friendly format. |
|
print(<Specification>)
|
Print a description of a Specification in a human friendly format. |
|
print(<stanfit>)
|
Print the result of the model in a human friendly format. |
|
sim()
|
Simulate data from the prior predictive density. |
|
specify()
|
Specify a model. |
|
theme
|
Theme for BayesHMM visualizations and printouts |
|
validate_calibration()
|
Validate a model via a procedure based on simulated data. |