Version 0.1
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FEATURES
* 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).
CHANGES
08/10/2014
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* version 0.1 launched on CRAN.