Function reference • mpcmp

Core functions
`glm.cmp()`	Fit a Mean Parametrized Conway-Maxwell Poisson Generalized Linear Model
`cmplrtest()`	Likelihood Ratio Test for nested COM-Poisson models
`LRTnu()`	Likelihood Ratio Test for nu = 1 of a COM-Poisson model
`dcomp()` `pcomp()` `qcomp()` `rcomp()`	The Conway-Maxwell-Poisson (COM-Poisson) Distribution.
Data sets
`attendance`	Attendance data set
`fish`	Fish data set
`takeoverbids`	Takeover Bids data set
`cottonbolls`	Cotton Bolls data set
`sitophilus`	Sitophilus data set
Generic extract, plot, print and summary methods
`AIC(<cmp>)`	Akaike's Information Criterion
`coef(<cmp>)`	Extract Model Coefficients from a COM-Poisson Model Fit
`print(<cmp>)`	Print Values of COM-Poisson Model
`plot(<cmp>)`	Plot Diagnostic for a `glm.cmp` Object
`autoplot(<cmp>)` `gg_plot()`	Plot Diagnostic for a `glm.cmp` Object in ggplot style
`summary(<cmp>)` `print(<summary.cmp>)`	Summarizing COM-Poisson Model Fit
`residuals(<cmp>)`	Extract COM-Poisson Model Residuals
`model.frame(<cmp>)`	Extract the Model Frame from a COM-Poisson Model Fit
`model.matrix(<cmp>)`	Extract the Design Matrix from a COM-Poisson Model Fit
`nobs(<cmp>)`	Extract the Number of Observation from a COM-Poisson Model Fit
`fitted(<cmp>)`	Extract Fitted Values from a COM-Poisson Model Fit
`logLik(<cmp>)` `print(<logLik.cmp>)`	Extract the (Maximized) Log-Likelihood from a COM-Poisson Model Fit
`predict(<cmp>)`	Model Predictions for a `glm.cmp` Object
`update(<cmp>)`	Update and Re-fit a COM-Poisson Model
`tidy(<cmp>)`	Tidy a(n) CMP model object
`augment(<cmp>)`	Augment data with information from a(n) CMP model object
`confint(<cmp>)`	Confidence Intervals for CMP Model Parameters
`glance(<cmp>)`	Glance at a(n) CMP model object
`influence(<cmp>)` `hatvalues(<cmp>)` `rstandard(<cmp>)` `cooks.distance(<cmp>)`	CMP Regression Diagnostic
`vcov(<cmp>)`	Extracting the Variance-Covariance Matrix from a COM-Poisson Model Fit
`mpcmp-package`	Mean-parametrized Conway-Maxwell Poisson Regression
`is.wholenumber()`	Test for a whole number
`CBIND()`	Combine R Objects by Columns
`Z()`	Calculate the Normalizing Constant for COM-Poisson distribution
`comp_mean_logfactorialy()` `comp_mean_ylogfactorialy()` `comp_means()` `comp_variances()` `comp_variances_logfactorialy()`	Functions to Compute Various Expected Values for the COM-Poisson Distribution
`comp_lambdas()` `comp_lambdas_fixed_ub()`	Solve for Lambda for a Particular Mean Parametrized COM-Poisson Distribution
`comp_mu_loglik()` `comp_mu_neg_loglik_log_nu_only()`	Calculate the Log-Likelihood of the COM-Poisson model
`getnu()`	Parameter Generator for nu
`histcompPIT()` `qqcompPIT()`	PIT Plots for a CMP Object
`gg_histcompPIT()` `gg_qqcompPIT()`	ggplot version of PIT Plots for a CMP Object
`compPredProb()` `compPIT()`	Non-randomized Probability Integral Transform
`compnormRandPIT()`	Random Normal Probability Integral Transform
`fit_glm_cmp_const_nu()`	Fit a Mean Parametrized Conway-Maxwell Poisson Generalized Linear Model with constant dispersion.
`fit_glm_cmp_vary_nu()`	Fit a Mean Parametrized Conway-Maxwell Poisson Generalized Linear Model with varying dispersion.
`logZ()`	Calculate the Normalizing Constant in log scale for COM-Poisson distribution
`logZ_c()`	Calculate the Normalizing Constant in log scale for COM-Poisson distribution The calculation of the function `logZ` will be performed here. This function is used to approximate the normalizing constant for COM-Poisson distributions via truncation. The standard COM-Poisson parametrization is being used here.

Reference

Core functions

Data sets

Generic extract, plot, print and summary methods