Compound Poisson Linear Models

Likelihood-based and Bayesian methods for various compound Poisson linear models based on Zhang, Yanwei (2013) < https://link.springer.com/article/10.1007/s11222-012-9343-7>.


News

================ 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o remove dependency on amer which now is withdrawn from CRAN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 "["

Reference manual

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install.packages("cplm")

0.7-8 by Yanwei (Wayne) Zhang, 9 months ago


https://github.com/actuaryzhang/cplm


Browse source code at https://github.com/cran/cplm


Authors: Yanwei (Wayne) Zhang


Documentation:   PDF Manual  


GPL (>= 2) license


Imports biglm, ggplot2, minqa, nlme, reshape2, statmod, stats, stats4, tweedie

Depends on coda, Matrix, splines, methods

Linking to Matrix


Imported by ChainLadder, GlmSimulatoR, HeritSeq.

Enhanced by MuMIn.


See at CRAN