Version 0.14
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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.
02/20/2015
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* removed use of arma_rng::set_seed_random() to avoid warning about setting R seed from C++.
10/16/2015
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* Minor changes in anticipation of ggplot2 1.1.0
07/27/2016
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* return pop_plot and samp_plot ggplot2 objects in gen_informative_sample() that plot generated synthetic functions in the population
and sample, respectively.
* employ uniform(0,1) starting values in the MCMC sampler when co-sampling the GP functions, bb, in gpdpgrow() to prevent numerical instability
producing NaNs in auxclusterstep() for sampling cluster assignments under Debian Linux.
04/05/2017
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* added new option to gmrfdpgrow() to allow input of N x R predictor matrix, ksi, that is used to update the prior probability of cluster assignments,
s. This dependent product partition model is enabled by placing a distribution on the ksi, though, we don't believe they're random to esablish a coherence
function multiplicative input to the prior distribution on cluster assignment.
* Note that we had to switch the sampling algorithm for cluster assignment from a conjugate multinomial to the non=conjugate Neal's algorithm 8 since the
inclusion of ksi produces a non-conjugate mixture.
* added new option to gmrfdpgrow() to support count data response if user inputs non-NULL N x T offset matrix, E.