================
Version 0.7-10
================
CHANGES:

  * Fix issues related to USE_FC_LEN_T

================
Version 0.7-9
================
CHANGES:

  * Remove zcpglm
  * Allow compatibility with changes in statmod

================
Version 0.7-5
================
CHANGES:

  * ‘gini’ now can handle a single score
  * Fixed bug when supplying initial values


================
Version 0.7-4
================

CHANGES:

  * 'predict' method failed when 'cpglm' is rank deficient. Fixed this. 
	Now returns the prediction but with a warning. 
  * 'predict' method for 'cpglmm' defaults to type = 'response'.
  * Fix issues when specifying initial values for 'bcplm' 
  * Add author info for the 'amer' functions
  * Update vignettes
 

================
Version 0.7-3
================

CHANGES:

  * update to be compatible with R 3.x (Prior versions failed)


================
Version 0.7-2
================

CHANGES:

  * fix bug related to sp2d

================
Version 0.7-1
================

CHANGES:

  * remove dependency on lme4 which is now completely re-written 

================
Version 0.6-4
================

CHANGES:

  * remove dependency on amer which now is withdrawn from CRAN

================
Version 0.6-1
================
NEW FEATURES

  * Add vignettes 
  * Add function 'zcpglm' which implements a zero-inflated compound Poisson 
	generalized linear model 
  * Add function 'gini' which computes the Gini indices that enable robust 
	model comparison involving compound Poisson distributions

CHANGES:

  * Add a new data set 'AutoClaim'
  * Set the max number of terms in computing the compound Poisson density using 
	series evaluation method. 
  * Correct bugs in predicting 'cpglm'. When the new data set has fewer factor 
	levels, the old method produces wrong predictions.
  * 'cpglm' has an argument 'optimizer' that allows users to select optimization routines.
  * Function 'bcplm' now implements MCMC methods for both GLM and mixed models. 
	This is a combination of the old functions 'bcpglm' and 'bcpglmm'
  * Produce model summary for Bayesian estimates (from 'bcplm')
  * Provide methods 'fixef' and 'VarCorr' for class 'bcplm'
  * The tuning procedure in MCMC is now based a method described in  Browne and Draper (2005)
  * MCMC now only implements univariate M-H within Gibbs sampling. Block update for the fixed 
	effects are removed. 
  * Remove the latent variable approach for Bayesian estimation
  * Remove method 'mcmcsamp'
  * Functions 'getF' and 'plotF' (copied from 'amer') now works for additive models
	to extract and plot fitted smoothing effects
  * Fix bugs in quadrature estimation of mixed models due to code inherited from lme4. This 
	will only affect models with multiple random effects per level. 
  * Fix the underflow issue in the quadrature estimation.
  * Implement the PQL method to generate initial values in 'cpglmm'.



================
Version 0.5-1
================
NEW FEATURES

  * Methods 'mcmcsamp' now is not available for 'cpglm' and 'cpglmm' objects
	as a convenient way to perform MCMC simulations.
  * Function 'cpglmm' handles additive models in a similar way as the package
	'amer'.
  * Big data capability is added to function 'cpglm', which uses the bounded
	memory regression facility from the package 'biglm'.
  * Function 'cpglmm' has an additional argument 'optimizer' that allows the 
	users to choose which optimization routine to be used. The package 
	'minqa' is now imported for the use 'bobyqa'.
  * Function 'cpglmm' now implements adaptive Gauss-Hermite quadrature method
	for models with a single grouping factor. 
  * Function 'bcpglmm' now implements an additional latent variable approach. 

CHANGES:
  * The MCEM method is now completely removed from 'cpglm'
  * In 'cpglmm', the Laplace approximated loglikelihood seems to have left out 
	the dispersion parameter for one term, resulting a larger than expected 
	variance component estimate. This is now fixed and it is more consistent 
	with the quadrature estimate. 
  * Add method 'predict' for 'cpglm' and 'cpglmm', which computes the predicted
	values for a new data set, but not the prediction errors. 
  * In 'cpglmm', fix a bug in specifying 'offset'
  * In 'cpglmm', 'sigmaML' is updated after fitting the model so that the 
	'postVar' option in 'ranef' in 'lme4' can be used now. 
  * 'weights' was not reflected in the update of the deviance. This is fixed now. 
  * In 'cpglmm', 'vcov' now computes variances for 'phi' and 'p'
  * register native routines in initialization

================
Version 0.4-1
================
NEW FEATURES

  * Function 'bcpglmm' is added that handles Bayesian mixed-effect models 
	using MCMC simulations.

CHANGES:
  * create the class 'cplm' as a fundamental structure in the package, 
	and define utility methods for it
  * replace the 'pstart', 'phistart' and 'betastart' arguments by a single
	argument 'inits' in most functions
  * combine the documentation for all classes and methods 

================
Version 0.3-1
================
NEW FEATURES

  * Function 'cpglmm' is added that handles mixed-effect models using 
	Laplace approximations. This is based on the R package 'lme4'.       
  * Function 'bcpglm' now has a second method to fit Bayesian compound
	Poisson GLM using direct Tweedie density approximation. 
  * Function 'bcpglm' also has a tuning phase that automatically updates
	the scale parameter in the proposal distribution. 
  * The profile likelihood method in 'cpglm' is now automated

CHANGES:
  * Prior distribution of the dispersion parameter in 'bcpglm' is
	changed to be Uniform, specified in the argument 'bound.phi'
  * 'bcpglm' has another argument 'method' that allows users to choose
	from the latent variable approach or direct density evaluation
  * An insurance example 'insLoss' is added in 'bcpglm' 
  * Remove the 'digits' parameter in control in 'cpglm' as the profile 
	likelihood method is automated now 
  * MCEM method in 'cpglm' simplifies the process of increase sample 
	size. The old time-consuming method of estimating approximate 
 	covariance matrix is removed. So the 'alpha' parameter in control
	is removed. 
  * The default method in 'cpglm' is now set to be 'profile'
  * Remove the 'summary' slot in 'bcpglm'
  * The profile method in 'cpglm' now returns covariance estimate for the 
	dispersion and index parameter 
  * 'bcpglm' replaces ARMS with M-H update. Now the dependency on the 
	ARMS functions is eliminated
  * 'bcpglm' now generates starting values using 'cpglm'
  * Simplify rejection sampling of latent variables (now twice faster)


================
Version 0.2-1
================
NEW FEATURES

  * The package now implements MCMC methods for Bayesian compound 
	Poisson GLM in the function "bcpglm" with the use of 
	latent variables.      
  * The R package "coda" is imported so that a large number of functions 
	and methods defined there are now directly applicable to 
	the simulation results from "bcpglm" to help diagnose convergence
	and summarize posterior inference. 

CHANGES:
  * Various methods defined for the class "bcplm" and "bcpglm"
  * Change the use of "R_alloc" in "lbfgsb" to "Calloc" and "Free" 
  * Simplify rejection sampling of latent variables (now twice faster)

================
Version 0.1-3 
================
CHANGES:
  * Fix a bug in rejection sampling of the latent variable 
  * Fix a bug in specifying weights
  * Divide cpglm_str into three parts, one for data and parameters, 
	one for latent variable, and one for EM related 

================
Version 0.1-2
================
NEW FEATURES

  * Add a wrapper of the profile likelihood approach to the "cpglm" 
     function that runs automatically to generate estimate of the 
     index parameter to arbitrary accuracy.

CHANGES:

  * The MCEM algorithm is now implemented in pure C code
  * Remove the restriction on the "weights" argument (but not tested)
  * Add "beta.step" in "control" to allow skips in the update of beta
  * Allow "link" to be both character and numeric
  * Force coercion of argument type before callings the C function
 	- thanks Mikel Esnaola Acebes for pointing out this bug
  * Re-write "summary" and "show" function to produce statistical
	test output automatically
  * Revise "residuals" to allow different types of residuals to be computed
  * Add methods for "formula", "AIC", "deviance", "model.matrix", "terms"
  * Output now returns "deviance", "aic" and "model.frame"
  * Tracing info from MCEM tidied up by showing only the dispersion, the
	index parameter, and the sample size (if necessary)
  * Fix bug in the definition of "[[", add methods for "["
