Package {CPHazard}

Title: Hazard Change Point Models for Different Lifetime Distributions
Version: 0.1.0
Description: Estimates the parameters of models with a single change-point in the hazard rate for time-to-event data. Supported models include the exponential (Gijbels & Gürler (2003) <doi:10.1023/B:LIDA.0000012424.71723.9d>, Matthews & Farewell (1982) <doi:10.2307/2530460>), Exponential-Lindley (Joshi & Rattihalli (2020) <doi:10.1007/978-981-15-5414-8_29>), Lindley (Joshi, Jose, & Bhati (2016) <doi:10.1080/03610918.2015.1096381>), log-logistic (Nadar, Upadhyay, & Joshi (2025) <doi:10.3390/math13091457>), and Weibull (Williams & Kim (2013) <doi:10.1080/03610926.2011.600505>) hazard change-point models. Provides functions for generating random variates and evaluating the probability density function (PDF) and the cumulative distribution function (CDF) of the fitted change-point models. Includes Kaplan-Meier and Nelson-Aalen diagnostic plots, together with goodness-of-fit measures such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), distance metrics such as the L1-norm and L2-norm, and the Kolmogorov-Smirnov (K-S) statistic for model evaluation.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (≥ 4.1.0)
Imports: lamW, survival, EnvStats
Maintainer: Vasudha Upadhyay <vasudhyay@gmail.com>
Suggests: spelling
Language: en-US
NeedsCompilation: no
Packaged: 2026-09-16 11:02:37 UTC; Keerthan Aithal
Author: Vasudha Upadhyay [aut, cre], Dr. Savitri Joshi [aut, cph], Keerthan Aithal [aut]
Repository: CRAN
Date/Publication: 2026-09-27 16:30:29 UTC

Density for the Exponential Hazard Change-Point Model

Description

Computes the PDF of the exponential hazard change-point model with parameters theta1, theta2, and tau.

Usage

dee(x, theta1, theta2, tau)

Arguments

x

Numeric vector of survival times (must be >= 0).

theta1

Rate parameter of the pre-change-point exponential regime; must be > 0.

theta2

Rate parameter of the post-change-point exponential regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of densities, the same length as x.

References

Gijbels, I., & Gürler, Ü. (2003). Estimation of a change point in a Hazard Function Based on Censored Data. Lifetime Data Analysis, 9(4), 395–411. doi:10.1023/B:LIDA.0000012424.71723.9d

Matthews, D. E., & Farewell, V. T. (1982). On Testing for a Constant Hazard against a change point Alternative. Biometrics, 38(2), 463-468. doi:10.2307/2530460

See Also

Other exponential hazard change-point model functions: est_ee(), pee(), ree()

Examples

dee(x = c(1, 2), theta1 = 1, theta2 = 2, tau = 1.25)

Density for the Exponential-Lindley Hazard Change-Point Model

Description

Computes the PDF of the Exponential-Lindley hazard change-point model with parameters theta1, theta2, and tau.

Usage

del(x, theta1, theta2, tau)

Arguments

x

Numeric vector of survival times (must be >= 0).

theta1

Rate parameter of the pre-change-point exponential regime; must be > 0.

theta2

Scale parameter of the post-change-point Lindley regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of densities, the same length as x.

References

Joshi, S., & Rattihalli, R. N. (2020, October). Estimation of parameters in the Exponential-Lindley hazard change-point model. In Proceedings of International Conference on Trends in Computational and Cognitive Engineering: TCCE 2019 , 345–356.doi:10.1007/978-981-15-5414-8_29

See Also

Other Exponential-Lindley hazard change-point model functions: est_el(), pel(), rel()

Examples

del(x = c(0.5, 1, 2), theta1 = 0.3, theta2 = 0.5, tau = 1)

Density for the Log-Logistic Hazard Change-Point Model

Description

Computes the PDF of the log-logistic hazard change-point model with parameters theta1, theta2, and tau.

Usage

dlgl(x, theta1, theta2, tau)

Arguments

x

Numeric vector of survival times (must be >= 0).

theta1

Scale parameter of the pre-change-point log-logistic regime; must be > 0.

theta2

Scale parameter of the post-change-point log-logistic regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of densities, the same length as x.

References

Nadar, S. S., Upadhyay, V., & Joshi, S. (2025). Detecting Clinical Risk Shift Through log–logistic Hazard Change-Point Model. Mathematics, 13(9), 1457. doi:10.3390/math13091457

See Also

Other log-logistic hazard change-point functions: est_lgl(), plgl(), rlgl()

Examples

dlgl(x = c(0.5, 1, 2), theta1 = 0.3, theta2 = 0.5, tau = 1)

Density for the Lindley Hazard Change-Point Model

Description

Computes the PDF of the Lindley hazard change-point model with parameters theta1, theta2, and tau.

Usage

dlin(x, theta1, theta2, tau)

Arguments

x

Numeric vector of survival times (must be >= 0).

theta1

Scale parameter of the pre-change-point Lindley regime; must be > 0.

theta2

Scale parameter of the post-change-point Lindley regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of densities, the same length as x.

References

Joshi, S., Jose, K. K. and Bhati, D. (2016). Estimation of a Change Point in the Hazard Rate of Lindley Model under Right Censoring. Communications in Statistics - Simulation and Computation. 46(5), 3563–3574. doi:10.1080/03610918.2015.1096381

See Also

Other Lindley hazard change-point model functions: est_lin(), plin(), rlin()

Examples

dlin(x = c(0.5, 1, 2), theta1 = 1, theta2 = 2, tau = 1.25)

Density for the Weibull Hazard Change-Point Model

Description

Computes the PDF of the Weibull hazard change-point model with parameters theta1, theta2, tau, and k.

Usage

dww(x, theta1, theta2, tau, k)

Arguments

x

Numeric vector of survival times (must be >= 0).

theta1

Scale parameter of the pre-change-point Weibull regime; must be > 0.

theta2

Scale parameter of the post-change-point Weibull regime; must be > 0.

tau

Change-point parameter; must be > 0.

k

Shape parameter (constant); must be > 0.

Value

A numeric vector of densities, the same length as x.

References

M. R. Williams and D. Y. Kim. A test for an abrupt change in Weibull Hazard Functions with Staggered Entry and Type I Censoring. Communications in Statistics - Theory and Methods, 42(11): 1922–1933, 2013. doi:10.1080/03610926.2011.600505.

See Also

Other Weibull hazard change-point model functions: est_ww(), pww(), rww()

Examples

dww(x = c(0.5, 1, 2), theta1 = 1, theta2 = 3, tau = 0.5, k = 2)

Parameter Estimation for the Exponential Hazard Change-Point Model

Description

Estimates theta1, theta2, and tau from time-to-event data (xi and di) under the exponential hazard change-point model , and produces goodness-of fit criteria (AIC and BIC) , the distance metrics (L1-Norm, L2-Norm, K-S Statistic) and the Kaplan-Meier / Nelson-Aalen diagnostic plots against the fitted model.

Usage

est_ee(xi, di)

Arguments

xi

Numeric vector of observed times (must be >= 0). Requires at least 5 uncensored observations.

di

Numeric vector of event indicators (1 = event, 0 = censored). Defaults to all events if omitted.

Value

A list containing Estimates (the fitted theta1, theta2, tau), DiagnosticMeasures (L1-Norm, L2-Norm, K-S Statistic, AIC, BIC), and plot (Kaplan-Meier and Nelson-Aalen plots)

References

Gijbels, I., & Gürler, Ü. (2003). Estimation of a change point in a Hazard Function Based on Censored Data. Lifetime Data Analysis, 9(4), 395–411. doi:10.1023/B:LIDA.0000012424.71723.9d

Matthews, D. E., & Farewell, V. T. (1982). On Testing for a Constant Hazard against a change point Alternative. Biometrics, 38(2), 463-468. doi:10.2307/2530460

See Also

Other exponential hazard change-point model functions: dee(), pee(), ree()

Examples

# Demonstration 1 (Parameter estimation with missing di)
est_ee(xi=c(45,32,40,21,12,13,52,60))

# Demonstration 2 (Parameter estimation with (xi,di))
n <- 100
x <- ree(n, 1, 2, 1.25)
d <- sample(c(0,1), n, replace = TRUE, prob = c(0.2, 0.8))
est_ee(xi = x, di = d)

Parameter Estimation for the Exponential-Lindley Hazard Change-Point Model

Description

Estimates theta1, theta2, and tau from time-to-event data (xi and di) under the Exponential-Lindley hazard change-point model with single change-point in hazard, and produces Kaplan-Meier / Nelson-Aalen diagnostic plots for the fitted model.

Usage

est_el(xi, di)

Arguments

xi

Numeric vector of observed times (must be >= 0). Requires at least 5 uncensored observations.

di

Numeric vector of event indicators (1 = event, 0 = censored). Defaults to all events if omitted.

Value

A list containing ParameterEstimates (the fitted theta1, theta2, tau), DiagnosticMeasures (L1-Norm, L2-Norm, K-S Statistic, AIC, BIC), and plot (Kaplan-Meier and Nelson-Aalen plots).

References

Joshi, S., & Rattihalli, R. N. (2020, October). Estimation of parameters in the Exponential-Lindley hazard change-point model. In Proceedings of International Conference on Trends in Computational and Cognitive Engineering: TCCE 2019 , 345–356.doi:10.1007/978-981-15-5414-8_29

See Also

Other Exponential-Lindley hazard change-point model functions: del(), pel(), rel()

Examples

# Demonstration 1 (Parameter estimation with missing di)
est_el(xi=c(45,32,45,22,12,13,78,99))

# Demonstration 2 (Parameter estimation with (xi,di))
x <- rel(n = 50, theta1 = 1.5, theta2 = 0.2, tau = 1.5)
d <- sample(c(0, 1), 50, replace = TRUE, prob = c(0.2, 0.8))
est_el(xi = x, di = d)

Parameter Estimation for the Log-Logistic Hazard Change-Point Model

Description

Estimates theta1, theta2, and tau from time-to-event data (xi and di) under the log-logistic hazard change-point model , and produces goodness-of fit criteria (AIC and BIC) , the distance metrics (L1-Norm, L2-Norm, K-S Statistic) and the Kaplan-Meier / Nelson-Aalen diagnostic plots against the fitted model.

Usage

est_lgl(xi, di)

Arguments

xi

Numeric vector of observed times (must be >= 0). Requires at least 5 uncensored observations.

di

Numeric vector of event indicators (1 = event, 0 = censored). Defaults to all events if omitted.

Value

A list containing ParameterEstimates (the fitted theta1, theta2, tau), DiagnosticMeasures (L1-Norm, L2-Norm, K-S Statistic, AIC, BIC), and plot (Kaplan-Meier and Nelson-Aalen plots).

References

Nadar, S. S., Upadhyay, V., & Joshi, S. (2025). Detecting Clinical Risk Shift Through log–logistic Hazard Change-Point Model. Mathematics, 13(9), 1457. doi:10.3390/math13091457

See Also

Other log-logistic hazard change-point functions: dlgl(), plgl(), rlgl()

Examples

# Demonstration 1 (Parameter estimation with missing di)
est_lgl(x=c(45,32,45,22,12,13,78,99))

# Demonstration 2 (Parameter estimation with (xi,di))
x <- rlgl(n = 50, theta1 = 1.5, theta2 = 0.5, tau = 2)
d <- sample(c(0, 1), 50, replace = TRUE, prob = c(0.2, 0.8))
est_lgl(xi = x, di = d)

Parameter Estimation for the Lindley Hazard Change-Point Model

Description

Estimates theta1, theta2, and tau from time-to-event data (xi and di) under the Lindley hazard change-point model , and produces goodness-of fit criteria (AIC and BIC) , the distance metrics (L1-Norm, L2-Norm, K-S Statistic) and the Kaplan-Meier / Nelson-Aalen diagnostic plots against the fitted model.

Usage

est_lin(xi, di)

Arguments

xi

Numeric vector of observed times (must be >= 0). Requires at least 5 uncensored observations.

di

Numeric vector of event indicators (1 = event, 0 = censored). Defaults to all events if omitted.

Value

A list containing ParameterEstimates (the fitted theta1, theta2, tau), DiagnosticMeasures (L1-Norm, L2-Norm, K-S Statistic, AIC, BIC), and plot (Kaplan-Meier and Nelson-Aalen plots).

References

Joshi, S., Jose, K. K. and Bhati, D. (2016). Estimation of a Change Point in the Hazard Rate of Lindley Model under Right Censoring. Communications in Statistics - Simulation and Computation. 46(5), 3563–3574. doi:10.1080/03610918.2015.1096381

See Also

Other Lindley hazard change-point model functions: dlin(), plin(), rlin()

Examples

# Demonstration 1 (Parameter estimation with missing di)
est_lin(xi=c(22,12,15,25,10,13,30,9))

# Demonstration 2 (Parameter estimation with (xi,di))
n <- 100
x <- rlin(n, 1, 2, 1.25)
d <- sample(c(0,1), n, replace = TRUE, prob = c(0.2, 0.8))
est_lin(xi = x, di = d)

Parameter Estimation for the Weibull Hazard Change-Point Model

Description

Estimates theta1, theta2, tau, and k from time-to-event data (xi and di) under the Weibull hazard change-point model , and produces goodness-of fit criteria (AIC and BIC) , the distance metrics (L1-Norm, L2-Norm, K-S Statistic) and the Kaplan-Meier / Nelson-Aalen diagnostic plots against the fitted model.

Usage

est_ww(xi, di = NULL)

Arguments

xi

Numeric vector of observed times (must be >= 0). Requires at least 5 uncensored observations.

di

Numeric vector of event indicators (1 = event, 0 = censored). Defaults to all events if omitted.

Value

A list containing ParameterEstimates (the fitted theta1, theta2, tau, k), DiagnosticMeasures (L1-Norm, L2-Norm, K-S Statistic, AIC, BIC), and plot (Kaplan-Meier and Nelson-Aalen plots).

References

M. R. Williams and D. Y. Kim. A test for an abrupt change in Weibull Hazard Functions with Staggered Entry and Type I Censoring. Communications in Statistics - Theory and Methods, 42(11): 1922–1933, 2013. doi:10.1080/03610926.2011.600505.

See Also

Other Weibull hazard change-point model functions: dww(), pww(), rww()

Examples

# Demonstration 1 (Parameter estimation with missing di)
est_ww(xi=c(12,8,45,7,47,32,14,10,19,23))

# Demonstration 2 (Parameter estimation with (xi,di))
x <- rww(n = 50, theta1 = 1, theta2 = 3, tau = 0.5, k = 3)
d <- sample(c(0, 1), 50, replace = TRUE, prob = c(0.2, 0.8))
est_ww(xi = x, di = d)

Distribution Function for the Exponential Hazard Change-Point Model

Description

Computes the CDF of the exponential hazard change-point model with parameters theta1, theta2, and tau.

Usage

pee(x, theta1, theta2, tau)

Arguments

x

Numeric vector of survival times (must be >= 0).

theta1

Rate parameter of the pre-change-point exponential regime; must be > 0.

theta2

Rate parameter of the post-change-point exponential regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of probabilities, the same length as x.

References

Gijbels, I., & Gürler, Ü. (2003). Estimation of a change point in a Hazard Function Based on Censored Data. Lifetime Data Analysis, 9(4), 395–411. doi:10.1023/B:LIDA.0000012424.71723.9d

Matthews, D. E., & Farewell, V. T. (1982). On Testing for a Constant Hazard against a change point Alternative. Biometrics, 38(2), 463-468. doi:10.2307/2530460

See Also

Other exponential hazard change-point model functions: dee(), est_ee(), ree()

Examples

pee(x = c(0.5, 1, 2), theta1 = 1, theta2 = 2, tau = 1.25)

Distribution Function for the Exponential-Lindley Hazard Change-Point Model

Description

Computes the CDF of the Exponential-Lindley hazard change-point model with parameters theta1, theta2, and tau.

Usage

pel(x, theta1, theta2, tau)

Arguments

x

Numeric vector of survival times (must be >= 0).

theta1

Rate parameter of the pre-change-point exponential regime; must be > 0.

theta2

Shape parameter of the post-change-point Lindley regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of probabilities, the same length as x.

References

Joshi, S., & Rattihalli, R. N. (2020, October). Estimation of parameters in the Exponential-Lindley hazard change-point model. In Proceedings of International Conference on Trends in Computational and Cognitive Engineering: TCCE 2019 , 345–356.doi:10.1007/978-981-15-5414-8_29

See Also

Other Exponential-Lindley hazard change-point model functions: del(), est_el(), rel()

Examples

pel(x = c(0.5, 1, 2), theta1 = 0.3, theta2 = 0.5, tau = 1)

Distribution Function for the Log-Logistic Hazard Change-Point Model

Description

Computes the CDF of the log-logistic hazard change-point model with parameters theta1, theta2, and tau.

Usage

plgl(x, theta1, theta2, tau)

Arguments

x

Numeric vector of survival times (must be >= 0).

theta1

Scale parameter of the pre-change-point log-logistic regime; must be > 0.

theta2

Scale parameter of the post-change-point log-logistic regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of probabilities, the same length as xi.

References

Nadar, S. S., Upadhyay, V., & Joshi, S. (2025). Detecting Clinical Risk Shift Through log–logistic Hazard Change-Point Model. Mathematics, 13(9), 1457. doi:10.3390/math13091457

See Also

Other log-logistic hazard change-point functions: dlgl(), est_lgl(), rlgl()

Examples

plgl(x = c(0.5, 1, 2), theta1 = 0.3, theta2 = 0.5, tau = 1)

Distribution Function for the Lindley Hazard Change-Point Model

Description

Computes the CDF of the Lindley hazard change-point model with parameters theta1, theta2, and tau.

Usage

plin(x, theta1, theta2, tau)

Arguments

x

Numeric vector of survival times (must be >= 0).

theta1

Scale parameter of the pre-change-point Lindley regime; must be > 0.

theta2

Scale parameter of the post-change-point Lindley regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of probabilities, the same length as x.

References

Joshi, S., Jose, K. K. and Bhati, D. (2016). Estimation of a Change Point in the Hazard Rate of Lindley Model under Right Censoring. Communications in Statistics - Simulation and Computation. 46(5), 3563–3574. doi:10.1080/03610918.2015.1096381

See Also

Other Lindley hazard change-point model functions: dlin(), est_lin(), rlin()

Examples

plin(x = c(0.5, 1, 2), theta1 = 1, theta2 = 2, tau = 1.25)

Distribution Function for the Weibull Hazard Change-Point Model

Description

Computes the CDF of the Weibull hazard change-point model with parameters theta1, theta2, tau, and k.

Usage

pww(x, theta1, theta2, tau, k)

Arguments

x

Numeric vector of survival times (must be >= 0).

theta1

Scale parameter of the pre-change-point Weibull regime; must be > 0.

theta2

Scale parameter of the post-change-point Weibull regime; must be > 0.

tau

Change-point parameter; must be > 0.

k

Shape parameter (constant); must be > 0.

Value

A numeric vector of probabilities, the same length as x.

References

M. R. Williams and D. Y. Kim. A test for an abrupt change in Weibull Hazard Functions with Staggered Entry and Type I Censoring. Communications in Statistics - Theory and Methods, 42(11): 1922–1933, 2013. doi:10.1080/03610926.2011.600505.

See Also

Other Weibull hazard change-point model functions: dww(), est_ww(), rww()

Examples

pww(x = c(0.5, 1, 2), theta1 = 1, theta2 = 3, tau = 0.5, k = 2)

Random Number Generation for the Exponential Hazard Change-Point Model

Description

Generates random survival times for the exponential hazard change-point model with parameters theta1, theta2, and tau.

Usage

ree(n, theta1, theta2, tau)

Arguments

n

Sample size.

theta1

Rate parameter of the pre-change-point exponential regime; must be > 0.

theta2

Rate parameter of the post-change-point exponential regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of length n of random survival times.

References

Gijbels, I., & Gürler, Ü. (2003). Estimation of a change point in a Hazard Function Based on Censored Data. Lifetime Data Analysis, 9(4), 395–411. doi:10.1023/B:LIDA.0000012424.71723.9d

Matthews, D. E., & Farewell, V. T. (1982). On Testing for a Constant Hazard against a change point Alternative. Biometrics, 38(2), 463-468. doi:10.2307/2530460

See Also

Other exponential hazard change-point model functions: dee(), est_ee(), pee()

Examples

ree(n = 100, theta1 = 1, theta2 = 0.5, tau = 1.25)

Random Number Generation for the Exponential-Lindley Hazard Change-Point Model

Description

Generates random survival times for the Exponential-Lindley hazard change-point model with parameters theta1, theta2, and tau.

Usage

rel(n, theta1, theta2, tau)

Arguments

n

Sample size.

theta1

Rate parameter of the pre-change-point exponential regime; must be > 0.

theta2

Shape parameter of the post-change-point Lindley regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of length n of random survival times.

References

Joshi, S., & Rattihalli, R. N. (2020, October). Estimation of parameters in the Exponential-Lindley hazard change-point model. In Proceedings of International Conference on Trends in Computational and Cognitive Engineering: TCCE 2019 , 345–356.doi:10.1007/978-981-15-5414-8_29

See Also

Other Exponential-Lindley hazard change-point model functions: del(), est_el(), pel()

Examples

rel(n = 100, theta1 = 1.5, theta2 = 0.2, tau = 5)

Random Number Generation for the Log-Logistic Hazard Change-Point Model

Description

Generates random survival times for the log-logistic hazard change-point model with parameters theta1, theta2, and tau.

Usage

rlgl(n, theta1, theta2, tau)

Arguments

n

Sample size.

theta1

Scale parameter of the pre-change-point log-logistic regime; must be > 0.

theta2

Scale parameter of the post-change-point log-logistic regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of length n of random survival times

References

Nadar, S. S., Upadhyay, V., & Joshi, S. (2025). Detecting Clinical Risk Shift Through log–logistic Hazard Change-Point Model. Mathematics, 13(9), 1457. doi:10.3390/math13091457

See Also

Other log-logistic hazard change-point functions: dlgl(), est_lgl(), plgl()

Examples

rlgl(n = 100, theta1 = 1.5, theta2 = 0.2, tau = 5)

Random Number Generation for the Lindley Hazard Change-Point Model

Description

Generates random survival times for the Lindley hazard change-point model with parameters theta1, theta2, and tau.

Usage

rlin(n, theta1, theta2, tau)

Arguments

n

Sample size.

theta1

Scale parameter of the pre-change-point Lindley regime; must be > 0.

theta2

Scale parameter of the post-change-point Lindley regime; must be > 0.

tau

Change-point parameter; must be > 0.

Value

A numeric vector of length n of random survival times.

References

Joshi, S., Jose, K. K. and Bhati, D. (2016). Estimation of a Change Point in the Hazard Rate of Lindley Model under Right Censoring. Communications in Statistics - Simulation and Computation. 46(5), 3563–3574. doi:10.1080/03610918.2015.1096381

See Also

Other Lindley hazard change-point model functions: dlin(), est_lin(), plin()

Examples

rlin(n = 100, theta1 = 1, theta2 = 2, tau = 1.25)

Random Number Generation for the Weibull Hazard Change-Point Model

Description

Generates random survival times for the Weibull hazard change-point model with parameters theta1, theta2, tau, and k.

Usage

rww(n, theta1, theta2, tau, k)

Arguments

n

Sample size.

theta1

Scale parameter of the pre-change-point Weibull regime; must be > 0.

theta2

Scale parameter of the post-change-point Weibull regime; must be > 0.

tau

Change-point parameter; must be > 0.

k

Shape parameter (constant); must be > 0.

Value

A numeric vector of length n of random survival times.

References

M. R. Williams and D. Y. Kim. A test for an abrupt change in Weibull Hazard Functions with Staggered Entry and Type I Censoring. Communications in Statistics - Theory and Methods, 42(11): 1922–1933, 2013. doi:10.1080/03610926.2011.600505.

See Also

Other Weibull hazard change-point model functions: dww(), est_ww(), pww()

Examples

rww(n = 100, theta1 = 1, theta2 = 3, tau = 0.5, k = 2)