NEWS.md
predict.cmp
that failed to handle new data with factors in it.autoplot
as an alias to gg_plot
. Credit to Emi Tanaka (@emitanaka) for this suggestion.broom
tidier
methods support. Specifically added method for tidy()
, glance()
and augment()
.summary()
was rewritten in order to support tidier
methods.vcov()
, confint()
, influence()
, hatvalues()
, rstandard()
, cooks.distance()
.rcomp()
a bit by precalculating all the dcomp()
values. Credit to Guilherme Parreira (@guilhermeparreira) for the issue request.offset
term cannot be incorporated properly in the mean model. Credit to Sean Hardison (@seanhardison1) for finding this bug.model.matrix()
to extract model matrix from a fitted object.glm.cmp()
to allow varying dispersion. You can now link the dispersion parameters to some covariates via a log-link.fit_glm_cmp_const_nu()
and fit_glm_cmp_vary_nu()
.print()
, summary()
are updated to support the updated glm.cmp()
.sitophilus
dataset to demonstrate the updated glm.cmp()
function.gg_plot()
, gg_histcompPIT()
and gg_qqcompPIT()
are added to provide the ggplots version of the diagnostic plots.NEWS.md
file to track changes to the package.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:
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.glm.cmp()
, dcomp()
, pcomp()
, qcomp()
, rcomp()
and functions that calculate expected values are updated to take advantage of these changes.fish
dataset as a proof of concept that glm.cmp()
can handle some larger count data.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.model.matrix()
now retrieves the design matrix of the model properly.glm.cmp()
gains a few standard glm arguments: start
, contrasts
, na.action
, subset
.