NEWS

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cplm 0.7-10 (2022-04-25)

CHANGES

• Fix issues related to USE_FC_LEN_T

cplm 0.7-9 (2020-10-21)

CHANGES

• Remove zcpglm
• Allow compatibility with changes in statmod

cplm 0.7-5 (2016-12-05)

CHANGES

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

cplm 0.7-4 (2015-08-21)

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

cplm 0.7-3 (2015-07-13)

CHANGES

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

cplm 0.7-2 (2014-03-10)

CHANGES

• fix bug related to sp2d

cplm 0.7-1 (2014-01-17)

CHANGES

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

cplm 0.6-4 (2012-08-16)

CHANGES

• remove dependency on amer which now is withdrawn from CRAN

cplm 0.6-1 (2012-07-17)

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'.

cplm 0.5-1 (2012-01-07)

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

cplm 0.4-1 (2011-11-08)

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

cplm 0.3-1 (2011-10-26)

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)

cplm 0.2-1 (2011-09-15)

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)

cplm 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

cplm 0.1-2 (2011-09-09)

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 "["