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mpcmp 0.3.8

  • Fixed a few typos in glm.cmp documentation.
  • Fixes for cran checks.

mpcmp 0.3.7

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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 ($\geq$ 3.2).

mpcmp 0.2.0

  • 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 $\lambda$s can be found even if they are outside the preset boundary.

mpcmp 0.1.4

  • 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

  • First major release of the package.