Revision 4ac7f8edf07c7090ed3a87041bd8f98b556355d7 authored by Yanwei (Wayne) Zhang on 21 August 2015, 00:00:00 UTC, committed by Gabor Csardi on 21 August 2015, 00:00:00 UTC
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Version 0.7-4


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

Version 0.7-3


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

Version 0.7-2


   o fix bug related to sp2d

Version 0.7-1


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

Version 0.6-4


   o remove dependency on amer which now is withdrawn from CRAN

Version 0.6-1

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


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

Version 0.5-1

   o Methods 'mcmcsamp' now is not available for 'cpglm' and 'cpglmm' objects
	as a convenient way to perform MCMC simulations.
   o Function 'cpglmm' handles additive models in a similar way as the package
   o Big data capability is added to function 'cpglm', which uses the bounded
	memory regression facility from the package 'biglm'.
   o 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'.
   o Function 'cpglmm' now implements adaptive Gauss-Hermite quadrature method
	for models with a single grouping factor. 
   o Function 'bcpglmm' now implements an additional latent variable approach. 

   o The MCEM method is now completely removed from 'cpglm'
   o 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. 
   o Add method 'predict' for 'cpglm' and 'cpglmm', which computes the predicted
	values for a new data set, but not the prediction errors. 
   o In 'cpglmm', fix a bug in specifying 'offset'
   o In 'cpglmm', 'sigmaML' is updated after fitting the model so that the 
	'postVar' option in 'ranef' in 'lme4' can be used now. 
   o 'weights' was not reflected in the update of the deviance. This is fixed now. 
   o In 'cpglmm', 'vcov' now computes variances for 'phi' and 'p'

   (not user-visible)	
   o register native routines in initialization

Version 0.4-1

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

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

Version 0.3-1

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

   o Prior distribution of the dispersion parameter in 'bcpglm' is
	changed to be Uniform, specified in the argument 'bound.phi'
   o 'bcpglm' has another argument 'method' that allows users to choose
	from the latent variable approach or direct density evaluation
   o An insurance example 'insLoss' is added in 'bcpglm' 
   o Remove the 'digits' parameter in control in 'cpglm' as the profile 
	likelihood method is automated now 
   o 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. 
   o The default method in 'cpglm' is now set to be 'profile'
   o Remove the 'summary' slot in 'bcpglm'
   o The profile method in 'cpglm' now returns covariance estimate for the 
	dispersion and index parameter 

   (not user-visible):
   o 'bcpglm' replaces ARMS with M-H update. Now the dependency on the 
	ARMS functions is eliminated
   o 'bcpglm' now generates starting values using 'cpglm'
   o Simplify rejection sampling of latent variables (now twice faster)

Version 0.2-1

   o The package now implements MCMC methods for Bayesian compound 
	Poisson GLM in the function "bcpglm" with the use of 
	latent variables.      
   o 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. 

   o Various methods defined for the class "bcplm" and "bcpglm"
   (not user-visible, all in C code):
   o Change the use of "R_alloc" in "lbfgsb" to "Calloc" and "Free" 
	(the old function eats up memory quickly)
   o Simplify rejection sampling of latent variables (now twice faster)

Version 0.1-3 (not released)
CHANGES (not user-visible, all in C code):
   o Fix a bug in rejection sampling of the latent variable 
   o Fix a bug in specifying weights
   o Divide cpglm_str into three parts, one for data and parameters, 
	one for latent variable, and one for EM related 

Version 0.1-2

   o 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.


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