Bayesian Time Series Modeling with Stan
Computes posterior sample of the point wise AIC method from a `varstan...
Computes posterior sample of the point wise corrected AIC method from ...
Convert to a stanfit object.
Automatic estimate of a Seasonal ARIMA model
Automatically create a ggplot for time series objects.
autoplot methods for varstan models.
Bayes Factors from Marginal Likelihoods.
Bayesian Time Series Modeling with Stan.
Define a beta prior distribution
Computes posterior sample of the pointwise BIC method from a varstan o...
Log Marginal Likelihood via Bridge Sampling.
Define a Cauchy prior distribution
Visual check of residuals in a varstan object.
Define a chi square prior distribution
Define an exponential prior distribution
Extract chains of an stanfit object implemented in rstan package
Expected Values of the Posterior Predictive Distribution
Forecasting varstan objects.
Fourier terms for modeling seasonality.
Define a gamma prior distribution
A constructor for a GARCH(s,k,h) model.
Get parameters of a varstan object.
Get the prior distribution of a model parameter
acf plot
Histogram with optional normal density functions
qqplot with normal qqline
pacf plot.
A constructor for a Holt trend state-space model.
A constructor for a Holt-Winters state-space model.
Define an inverse gamma prior distribution
Define an inverse gamma prior distribution
Define a non informative Jeffrey's prior for the degree freedom hyper ...
Define a Laplace prior distribution
Define a LKJ matrix prior distribution
A constructor for local level state-space model.
Extract posterior sample of the point wise log-likelihood from a `vars...
Extract posterior sample of the accumulated log-likelihood from a `var...
Leave-one-out cross-validation
MCMC Plots Implemented in bayesplot
Print the defined model of a varstan object.
Naive and Random Walk models.
Define a normal prior distribution
plot methods for varstan models.
Expected Values of the Posterior Predictive Distribution
Posterior uncertainty intervals
Draw from posterior predictive h steps ahead distribution
Out-of-sample predictive errors
Print a garch model
Prints a Holt model.
Print a Holt-Winter model
Print a Local Level model
Print a naive model
Print a Sarima model.
Print a state-space model.
Print a Stochastic Volatility model
Print a varstan object
Generic function for extracting information about prior distributions
Objects exported from other packages
Print a full report of the time series model in a varstan object.
Generic function and method for extract the residual of a varstan ob...
Constructor a Multiplicative Seasonal ARIMA model.
Set a prior distribution to a model parameter.
A constructor for a Additive linear State space model.
Fitting for a GARCH(s,k,h) model.
Fitting an Holt state-space model.
Fitting a Holt-Winters state-space model.
Fitting a Local level state-space model.
Naive and Random Walk models.
Fitting a Multiplicative Seasonal ARIMA model.
Fitting an Additive linear State space model.
Fitting a Stochastic volatility model.
Define a t student prior distribution
Summary method for a varstan object
Constructor of an Stochastic volatility model object
Define a uniform prior distribution
Constructor of a varstan object.
Widely Applicable Information Criterion (WAIC)
Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.