ctp.fit: Maximum Likelihood Estimation for the CTP Distribution

In zmctp: Zero-Modified Complex 'Tri-Parametric' Pearson Distribution for Overdispersed Count Data

Maximum Likelihood Estimation for the CTP Distribution

Description

Fits the Complex Triparametric Pearson (CTP) distribution to count data using maximum likelihood estimation.

Usage

ctp.fit(
  x,
  a_start = NULL,
  b_start = NULL,
  gama_start = NULL,
  method = "L-BFGS-B",
  penalty = 1e+10
)

Arguments

x	Numeric vector of nonnegative counts.
a_start	Optional starting value for parameter a.
b_start	Optional starting value for parameter b.
gama_start	Optional starting value for parameter gamma.
method	Optimization method (default: "L-BFGS-B").
penalty	numeric penalty added for numerical stability when b → 0

Value

An object of class "ctpfit" containing:

estimates	Named vector of MLEs
se	Standard errors
vcov	Variance-covariance matrix
logLik	Log-likelihood
AIC	Akaike Information Criterion
BIC	Bayesian Information Criterion
pearson_chisq	Pearson's chi-squared statistic
wald_chisq	Wald's chi-squared statistic
fitted_freq	Data frame of observed vs expected frequencies
data	Original data
converged	Convergence status

Examples

set.seed(123)

x <- rctp(30, a = 1, b = 0.5, gama = 5)
fit <- ctp.fit(x)
print(fit)
plot(fit)

zmctp documentation built on Sept. 20, 2026, 5:07 p.m.