================
Version 0.7-5
================
CHANGES:
o ‘gini’ now can handle a single score
o Fixed bug when supplying initial values
================
Version 0.7-4
================
CHANGES:
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
================
CHANGES:
o update to be compatible with R 3.x (Prior versions failed)
================
Version 0.7-2
================
CHANGES:
o fix bug related to sp2d
================
Version 0.7-1
================
CHANGES:
o remove dependency on lme4 which is now completely re-written
================
Version 0.6-4
================
CHANGES:
o remove dependency on amer which now is withdrawn from CRAN
================
Version 0.6-1
================
NEW FEATURES
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
CHANGES:
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
================
NEW FEATURES
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
'amer'.
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.
CHANGES:
(user-visible)
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
================
NEW FEATURES
o Function 'bcpglmm' is added that handles Bayesian mixed-effect models
using MCMC simulations.
CHANGES:
(user-visible)
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
================
NEW FEATURES
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
CHANGES:
(user-visible)
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
================
NEW FEATURES
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.
CHANGES:
(user-visible)
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
================
NEW FEATURES
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.
CHANGES:
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 "["