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The zmctp package extends the Complex Triparametric Pearson (CTP) distribution with zero-modified versions for handling overdispersed count data. It addresses limitations in existing implementations when the parameter b approaches zero.
# Install from GitHub (when available)
# devtools::install_github("yourusername/zmctp")
# Or install from source
devtools::install_local("path/to/zmctp")library(zmctp)
# Generate data
x <- rctp(200, a = 1, b = 0.5, gama = 5)
# Fit CTP model
fit <- ctp.fit(x)
print(fit)
plot(fit)
# Fit Zero-Modified CTP
x_zi <- rzictp(200, a = 1, b = 0.5, gama = 5, omega = 0.3)
fit_zi <- zictp.fit(x_zi)
plot(fit_zi)Existing implementations (e.g., the cpd package)
struggle when b ≈ 0, often estimating b = 0 which
reduces model flexibility. The zmctp package solves this
through:
vignette("introduction", package = "zmctp")?ctp.fit,
?zictp.fit, ?dctplibrary(cpd)
library(zmctp)
# Data where cpd estimates b ≈ 0
x <- rzictp(200, a = 1, b = 0.001, gama = 8, omega = 0.2)
# cpd may fail
fit_cpd <- cpd::fitCTP(x)
# b estimate ≈ 0
# zmctp handles it better
fit_zmctp <- zictp.fit(x)
# Recovers both b and omegaIf you use this package, please cite:
@Manual{zmctp,
title = {zmctp: Zero-Modified Complex Triparametric Pearson Distribution},
author = {Rasheedat Oladoja},
year = {2025},
note = {R package version 0.1.0},
}
And the original CTP paper:
@article{rodriguez2003,
title={A new class of discrete distributions with complex parameters},
author={Rodríguez-Avi, J and Conde-Sánchez, A and Sáez-Castillo, AJ},
journal={Statistical Papers},
volume={44},
pages={67--88},
year={2003},
doi={10.1007/s00362-002-0134-7}
}
GPL-3
Rasheedat Oladoja - roladoja@ttu.edu
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.