Version 0.11
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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.

12/09/2014
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* replaced srand() with arma_rng::set_seed_random() to initialize random seed for RcppArmadillo.
* removed use of arma::sp_mat (sparse matrix) formulation for normalized CAR adjacency matrix from
  gmrfdpgrow() due to memory initialization issue in RcppArmadillo.
