Estimates a collection of time-indexed functions under either of Gaussian process (GP) or intrinsic Gaussian Markov random field (iGMRF) prior formulations where a Dirichlet process mixture allows sub-groupings of the functions to share the same covariance or precision parameters. The GP and iGMRF formulations both support any number of additive covariance or precision terms, respectively, expressing either or both of multiple trend and seasonality.
new release on CRAN.
performs Bayesian non-parametric modeling on a (rectangular) set of functional data observations.
The collection of functions may be modeled under Gaussian process (GP) or intrinsic Gaussian Markov
random field (iGMRF) prior formulations.
The covariance and precision parameters of the GP and iGMRF formulations, respectively, are placed under
a Dirichlet process (DP) prior to allow the data to discover dependence among the estimated functions
where co-clusters functions are drawn from distributions sharing the same covariance and precision parameters.
the GP prior formulation is invoked with gpdpgrow()
any number of additive covariance terms may be specified with gpdpgrow().
for example, if there are 4 terms, then the input variable, gp_cov = c("rq","se","sn","sn")
if the covariance functions for the 4 terms are structured as (rational quadratic, squared exponential,
seasonal, seasonal), respectively. The input variable, sn_order = c(3,12), sets the order for each seasonality
term; in this case, 3 months and 12 months (assuming the data time scale is denoted by month).
the iGMRF prior is invoked with gmrfdpgrow(), also allowing any number of additive precision terms
the input variable, q_type = c("tr","sn","sn"), denotes "tr' = trend, and "sn" = seasonality terms.
input, q_order = c(2,3,12) denotes the order for the associated term; for example, the second term
is specified as seasonal of order = 3 (e.g. months).