# mpcmp 0.3.7 Unreleased

• Exported more functions to NAMESPACE as requested by @yangchino1.
• Patched a bug in predict.cmp that failed to handle new data with factors in it.

# mpcmp 0.3.6 2020-10-26

• Added autoplot as an alias to gg_plot. Credit to Emi Tanaka (@emitanaka) for this suggestion.
• Also patched an issue in the Rcpp code to pass cran check for solaris.
• There are also other minor QOL improvements. # mpcmp 0.3.5
• Added broom tidier methods support. Specifically added method for tidy(), glance() and augment().
• summary() was rewritten in order to support tidier methods.
• Also added method for vcov(), confint(), influence(), hatvalues(), rstandard(), cooks.distance().

# mpcmp 0.3.4 Unreleased

• Added Github action support.
• From this version onward, glm.cmp() no longer uses getnu().
• General fixes for cran checks.
• Blog post!

# mpcmp 0.3.3 Unreleased

• Optimised rcomp() a bit by precalculating all the dcomp() values. Credit to Guilherme Parreira (@guilhermeparreira) for the issue request.
• Documentations are now generated by Roxygen version 7.1.0.

# mpcmp 0.3.2 Unreleased

• Fixed an issue that offset term cannot be incorporated properly in the mean model. Credit to Sean Hardison (@seanhardison1) for finding this bug.

# mpcmp 0.3.1 Unreleased

• Added the model.matrix() to extract model matrix from a fitted object.
• Documentations are now generated by Roxygen version 7.0.2.

# mpcmp 0.3.0 Unreleased

• Updated glm.cmp() to allow varying dispersion. You can now link the dispersion parameters to some covariates via a log-link.
• Most calculations are now performed inside fit_glm_cmp_const_nu() and fit_glm_cmp_vary_nu().
• Functions such as print(), summary() are updated to support the updated glm.cmp().
• Added spelltest as part of the testing procedure.
• Added the sitophilus dataset to demonstrate the updated glm.cmp() function.

data(sitophilus)
M.sit <- glm.cmp(formula = ninsect ~ extract, formula_nu = ~extract,
data = sitophilus)
summary(M.sit)

# mpcmp 0.2.1 Unreleased

• Added travis.CI support.
• New functions gg_plot(), gg_histcompPIT() and gg_qqcompPIT() are added to provide the ggplots version of the diagnostic plots.
• The package now depends on a more recent version of R ( 3.2).

# mpcmp 0.2.0 Unreleased

• Added a NEWS.md file to track changes to the package.
• Added a draft logo to the package.
• Ribeiro Jr et al. (2018) specification of the CMP model is utilised to provide a better initial estimate for the dispersion parameter. Added comp_mu_loglik_log_nu_only() to facilitate the optimisation.
• Z(), the normalizing constant function, approximates its true value via (a fixed) truncation. This means the approximation would fail if the mean is large. The followings are implemented as a fix:
• A new function logZ() is created, based on a similar function in the cmpreg package of Ribeiro Jr, Zeviani & Demétrio (2019), and will supersede Z() due to its superior numerical stability.
• A Chebyshev’s inequality type argument is now implemented to have a more flexible upper truncation point.
glm.cmp(), dcomp(), pcomp(), qcomp(), rcomp() and functions that calculate expected values are updated to take advantage of these changes.

sum(0:500*dcomp(0:500,100,1.2))
sum(0:500*dcomp(0:500,150,0.8))
qcomp(0.6, 150, 1.2)
• Added the fish dataset as a proof of concept that glm.cmp() can handle some larger count data.

data(fish)
M.fish <- glm.cmp(species~ 1+log(area), data=fish)
max(M.fish\$fitted.values)
• comp_lambdas() now has the ability to scale up & down lambdaub so that the correct λs can be found even if they are outside the preset boundary.

# mpcmp 0.1.4 Unreleased

• model.matrix() now retrieves the design matrix of the model properly.
• glm.cmp() gains a few standard glm arguments: start, contrasts, na.action, subset.

# mpcmp 0.1.3 2019-03-04

• First major release of the package.