Help for package gofPHCS

Type:

Package

Title:

Goodness-of-Fit Tests for Complete, Progressively Type-II, Type-I Hybrid, and Type-II Hybrid Censored Data

Version:

0.1.0

Description:

Provides goodness-of-fit tests for lifetime data collected under complete sampling, progressive Type-II censoring, and Type-I/Type-II hybrid censoring schemes. Users supply the observed (censored) data and the assumed probability density/mass function, cumulative distribution function, or survival function of the target model, and the package returns the corresponding test statistic together with an asymptotic or Monte Carlo p-value. Implements the spacings-based exponentiality test of Balakrishnan, Ng and Kannan (2002, in "Goodness-of-Fit Tests and Model Validity", Birkhauser, pp. 89-111) and its location-scale generalization Balakrishnan, Ng and Kannan (2004) <doi:10.1109/TR.2004.833317>, the power comparison and Kaplan-Meier based tests of Doering and Cramer (2019) <doi:10.1080/00949655.2019.1648468>, the Kolmogorov-Smirnov type tests for hybrid censored data of Banerjee and Pradhan (2018) <doi:10.1080/03610926.2016.1205616>, and follows the unified treatment of hybrid censoring schemes reviewed in Balakrishnan and Kundu (2013) <doi:10.1016/j.csda.2012.03.025> and in Cramer and Balakrishnan (2023, "Hybrid Censoring Know-How", Chapter 11) <doi:10.1016/B978-0-12-398387-9.00019-2>.

License:

GPL (≥ 3)

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.3

Depends:

R (≥ 4.1.0)

Imports:

stats, withr

Suggests:

testthat (≥ 3.0.0), knitr, rmarkdown, spelling, covr

VignetteBuilder:

knitr

Config/testthat/edition:

Language:

en-US

NeedsCompilation:

Packaged:

2026-07-24 21:00:01 UTC; shikhar tyagi

Author:

Shikhar Tyagi

[aut, cre], Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi [aut]

Maintainer:

Shikhar Tyagi <shikhar1093tyagi@gmail.com>

Repository:

CRAN

Date/Publication:

2026-08-04 14:00:02 UTC

gofPHCS: Goodness-of-Fit Tests for Complete, Progressively Type-II, Type-I Hybrid, and Type-II Hybrid Censored Data

Description

Provides goodness-of-fit tests for lifetime data collected under complete sampling, progressive Type-II censoring, and Type-I/Type-II hybrid censoring schemes. Users supply the observed (censored) data and the assumed probability density/mass function, cumulative distribution function, or survival function of the target model, and the package returns the corresponding test statistic together with an asymptotic or Monte Carlo p-value. Implements the spacings-based exponentiality test of Balakrishnan, Ng and Kannan (2002, in "Goodness-of-Fit Tests and Model Validity", Birkhauser, pp. 89-111) and its location-scale generalization Balakrishnan, Ng and Kannan (2004) doi:10.1109/TR.2004.833317, the power comparison and Kaplan-Meier based tests of Doering and Cramer (2019) doi:10.1080/00949655.2019.1648468, the Kolmogorov-Smirnov type tests for hybrid censored data of Banerjee and Pradhan (2018) doi:10.1080/03610926.2016.1205616, and follows the unified treatment of hybrid censoring schemes reviewed in Balakrishnan and Kundu (2013) doi:10.1016/j.csda.2012.03.025 and in Cramer and Balakrishnan (2023, "Hybrid Censoring Know-How", Chapter 11) doi:10.1016/B978-0-12-398387-9.00019-2.

Author(s)

Maintainer: Shikhar Tyagi shikhar1093tyagi@gmail.com (ORCID)

Authors:

Arvind Pandey arvindmzu@gmail.com
Bhupendra Singh bhupendra.rana@gmail.com
Vrijesh Tripathi vrijesh.tripathi@uwi.edu

Aluminum Coupon Fatigue Lifetimes Data

Description

Fatigue lifetimes (in 10^3 cycles) of 101 aluminum coupons under periodic stress loading.

Usage

aluminum_coupons

Format

A numeric vector of length 101.

Source

Birnbaum, Z. W., & Saunders, S. C. (1958). A statistical model for life-length of materials. JASA, 53(281), 151-160.

References

Banerjee, B., & Pradhan, B. (2018). Kolmogorov-Smirnov test for life test data with hybrid censoring. Communications in Statistics - Theory and Methods, 47(11), 2590-2604.

Create a Censored Data Container

Description

Constructor for creating a structured censored dataset object of class "cens_data".

Usage

cens_data(
  x,
  scheme = c("complete", "typeII", "progtypeII", "hybridI", "hybridII", "hybridI_hcs",
    "hybridII_hcs"),
  n = length(x),
  r = NULL,
  R = NULL,
  T0 = NULL,
  verbose = FALSE
)

Arguments

x

Numeric vector of observed failure times. Must be finite.

scheme

Character string specifying the censoring scheme. Options are: "complete", "typeII", "progtypeII", "hybridI", "hybridII", "hybridI_hcs", and "hybridII_hcs".

n

Total sample size at the beginning of the experiment. Defaults to length(x).

r

Integer specifying the pre-fixed failure count for Type-II or hybrid censoring schemes.

R

Integer vector of progressive removals for "progtypeII", "hybridI_hcs", or "hybridII_hcs".

T0

Positive numeric value for pre-fixed time threshold in hybrid censoring schemes.

verbose

Logical; if TRUE, emits informative messages (e.g. when sorting unsorted x). Default is FALSE.

Value

An S3 object of class "cens_data" containing:

x

Numeric vector of sorted observed failure times.

scheme

Censoring scheme name.

n

Total initial sample size.

n_obs

Number of observed failure times length(x).

r

Pre-fixed failure count (if applicable).

R

Progressive removal vector (if applicable).

T0

Time threshold (if applicable).

References

Cramer, E., & Balakrishnan, N. (2023). Goodness-of-fit tests. In Hybrid Censoring Know-How (Chapter 11, pp. 321-329). Academic Press. doi:10.1016/B978-0-12-398387-9.00019-2

Examples

# Complete data
cd1 <- cens_data(x = c(0.5, 1.2, 2.1, 3.4), scheme = "complete")
print(cd1)

# Progressive Type-II censored data
cd2 <- cens_data(x = c(0.19, 0.78, 0.96, 1.31), scheme = "progtypeII", R = c(0, 1, 0, 2))
summary(cd2)

Goodness-of-Fit Test Dispatcher for Censored Lifetime Data

Description

Main function for conducting goodness-of-fit tests on complete, progressively Type-II, and hybrid censored lifetime data.

Usage

gof_test(
  data,
  pdf = NULL,
  cdf = NULL,
  sf = NULL,
  pmf = NULL,
  distribution = NULL,
  params = NULL,
  statistic = "auto",
  p.method = c("auto", "asymptotic", "montecarlo"),
  nsim = 9999,
  seed = NULL,
  conf.level = 0.95,
  ...
)

Arguments

data

A "cens_data" object created by cens_data.

pdf

Optional probability density function f(x, ...).

cdf

Optional cumulative distribution function F(x, ...).

sf

Optional survival function S(x, ...).

pmf

Optional probability mass function P(x, ...).

distribution

Optional pre-constructed "gof_distribution" object created by make_distribution.

params

Parameter vector or named list for the null distribution.

statistic

Character string specifying test statistic name. Default "auto" selects default test for data$scheme. Use "all" to compute all applicable test statistics.

p.method

Character string specifying p-value calculation method ("auto", "asymptotic", or "montecarlo").

nsim

Integer specifying number of Monte Carlo replicates. Default is 9999.

seed

Optional integer seed for reproducible Monte Carlo simulation.

conf.level

Confidence level (default 0.95).

...

Additional arguments passed to specific statistic functions (e.g. mc_moments for "genT").

Value

An object of class c("gof_htest", "htest") containing:

statistic

Observed test statistic value.

p.value

Calculated p-value.

method

Character string describing the test.

data.name

Character string describing the dataset.

estimate

Parameter estimates / fitted values.

References

Balakrishnan, N., Ng, H. K. T., & Kannan, N. (2002). A test of exponentiality based on spacings for progressively type-II censored data. In Goodness-of-Fit Tests and Model Validity (pp. 89-111). Birkhauser.

Banerjee, B., & Pradhan, B. (2018). Kolmogorov-Smirnov test for life test data with hybrid censoring. Communications in Statistics - Theory and Methods, 47(11), 2590-2604. doi:10.1080/03610926.2016.1205616

Cramer, E., & Balakrishnan, N. (2023). Goodness-of-fit tests. In Hybrid Censoring Know-How (Chapter 11). Academic Press. doi:10.1016/B978-0-12-398387-9.00019-2

Examples

set.seed(42)
x_data <- rexp(15, rate = 0.5)
cd <- cens_data(x = x_data, scheme = "complete")
dist <- make_distribution(cdf = function(x, rate) pexp(x, rate), params = c(rate = 0.5))
res <- gof_test(cd, distribution = dist, statistic = "KS", p.method = "montecarlo", nsim = 99)
print(res)

Insulating Fluid Breakdown Times Data

Description

Times to breakdown (in minutes) of an insulating fluid subjected to high voltage stress.

Usage

insulating_fluid

Format

A numeric vector of length 18.

Source

Nelson, W. (1982). Applied Life Data Analysis. John Wiley & Sons.

References

Cramer, E., & Balakrishnan, N. (2023). Goodness-of-fit tests. In Hybrid Censoring Know-How (Chapter 11). Academic Press.

Create a Unified Distribution Adapter

Description

Constructs a distribution object encapsulating probability density function (pdf), cumulative distribution function (cdf), survival function (sf), and quantile function (qdist). Missing functions are derived automatically using numerical calculus and root-finding.

Usage

make_distribution(
  pdf = NULL,
  cdf = NULL,
  sf = NULL,
  pmf = NULL,
  discrete = FALSE,
  support = c(-Inf, Inf),
  params = NULL
)

Arguments

pdf

Probability density function f(x, ...).

cdf

Cumulative distribution function F(x, ...).

sf

Survival function S(x, ...).

pmf

Probability mass function P(x, ...) for discrete models.

discrete

Logical indicating if the distribution is discrete. Default is FALSE.

support

Numeric vector of length 2 defining distribution support bounds c(lower, upper). Default is c(-Inf, Inf).

params

Named list or numeric vector of parameters passed to pdf, cdf, sf, or pmf.

Value

An S3 object of class "gof_distribution" containing:

pdf

Vectorized density function pdf(x).

cdf

Vectorized CDF function cdf(x).

sf

Vectorized survival function sf(x).

qdist

Vectorized quantile function qdist(p).

discrete

Logical flag.

support

Support interval.

params

User-supplied parameter vector or list.

Examples

# Create an Exponential distribution adapter
exp_dist <- make_distribution(
  cdf = function(x, rate) pexp(x, rate = rate),
  params = c(rate = 0.5),
  support = c(0, Inf)
)
exp_dist$cdf(1)
exp_dist$pdf(1)

Random Generation Engine for Censored Data

Description

Generates simulated datasets under specified censoring schemes from a quantile function qdist.

Usage

rcens(
  scheme = c("complete", "progtypeII", "hybridI", "hybridII", "hybridI_hcs",
    "hybridII_hcs"),
  n,
  R = NULL,
  r = NULL,
  T0 = NULL,
  qdist,
  nsim = 1
)

Arguments

scheme

Character string specifying censoring scheme ("complete", "progtypeII", "hybridI", "hybridII", "hybridI_hcs", "hybridII_hcs").

n

Integer specifying initial sample size.

R

Integer vector of progressive removals.

r

Integer specifying target failure count for Type-II or hybrid schemes.

T0

Numeric value specifying time threshold for hybrid schemes.

qdist

Quantile function qdist(p) of the target distribution.

nsim

Integer specifying the number of datasets to simulate. Default is 1.

Value

If nsim = 1, returns a single "cens_data" object. If nsim > 1, returns a list of "cens_data" objects.

References

Balakrishnan, N., & Sandhu, R. A. (1995). A simple simulational algorithm for generating progressive Type-II censored samples. The American Statistician, 49(2), 229-230.

Examples

# Simulate a progressive Type-II censored sample from Standard Exponential
set.seed(123)
sim_data <- rcens(
  scheme = "progtypeII", n = 10, R = c(0, 1, 0, 1, 3),
  qdist = function(p) qexp(p, rate = 1)
)
print(sim_data)

Package {gofPHCS}

gofPHCS: Goodness-of-Fit Tests for Complete, Progressively Type-II, Type-I Hybrid, and Type-II Hybrid Censored Data

Description

Author(s)

Aluminum Coupon Fatigue Lifetimes Data

Description

Usage

Format

Source

References

Create a Censored Data Container

Description

Usage

Arguments

Value

References

Examples

Goodness-of-Fit Test Dispatcher for Censored Lifetime Data

Description

Usage

Arguments

Value

References

Examples

Insulating Fluid Breakdown Times Data

Description

Usage

Format

Source

References

Create a Unified Distribution Adapter

Description

Usage

Arguments

Value

Examples

Random Generation Engine for Censored Data

Description

Usage

Arguments

Value

References

Examples