bizicount-package {bizicount} | R Documentation |
bizicount: Copula-Based Bivariate Zero-Inflated Count Regression Models
Description
The package provides regression functions for copula-based
bivariate count models based on the paper doi:10.18637/jss.v109.i01, with
and without zero-inflation, as well as
regression functions for univariate zero-inflated count models. Generic
methods from the texreg-package
and
DHARMa
are extended to support this
package, namely for the purposes of producing professional tables and
carrying out post-estimation diagnostics. A generic for He et al. (2019)'s
test for zero-modification is provided, with methods for both bizicount
and glm
-class objects.
Bivariate Functions
-
bizicount
– The primary function of this package. Carries out copula-based bivariate count regression via maximum likelihood using numerical optimization. Supports both zero-inflated and non-inflated distributions. -
extract.bizicount
– Method for the texreg package'sextract
generic. Creates a list of texreg objects, one for each margin, for use with that package's other functions. -
make_DHARMa
– Creates a list of DHARMa objects, one for each margin, forbizicount
models. A wrapper aroundcreateDHARMa
. -
simulate.bizicount
– Method that simulates observations using the fitted model's parameters, primarily for use with DHARMa. -
zi_test
– Method for testing for marginal zero-modification using the esimated parameters from the model. This test is preferable to the Vuong, Wald, Score, and LR tests. See He et al. (2019).
Univariate Functions
-
zic.reg
– Univariate zero-inflated count regression models via maximum likelihood. -
extract.zicreg
– Method for the texreg package'sextract
generic. Creates a texreg object that interfaces with that package's methods. -
simulate.zicreg
– Method for simulating from the fitted model. Results are generally used for creating DHARMa objects.#'
-
zi_test
– Method for testing for univariate zero-modification using the esimated parameters from the model. This test is preferable to the Vuong, Wald, Score, and LR tests. See He et al. (2019).
Author(s)
John Niehaus