Implements methods for bivariate and univariate Laplace transforms
of residual lives and reversed residual lives, associated stochastic ordering
concepts, and entropy measures for reliability analysis. The package covers:
(1) Bivariate Laplace transform of residual lives and stochastic comparisons
based on the bivariate Laplace transform order of residual lives (BLt-rl),
including weak bivariate hazard rate, mean residual life, and relative mean
residual life orders, nonparametric estimation, and NBUHR/NWUHR aging class
characterisation; Jayalekshmi, Rajesh, and Nair (2022)
"Bivariate Laplace Transform of Residual Lives and Their Properties"

BivLaplaceRL is an R package for bivariate and univariate Laplace transforms of residual lives, stochastic ordering concepts, and entropy measures in reliability analysis.
Bivariate Laplace transforms of residual lives — closed-form Gumbel results, general numerical integration, nonparametric estimation, and NBUHR/NWUHR aging class characterisation.
BLt-Rrl framework: reversed hazard gradient, reversed mean residual life, and closed-form transforms for FGM and bivariate power distributions.
Univariate LT of residual life, hazard rate, mean residual life, and three stochastic orders (Lt-rl, hazard rate, MRL) with nonparametric estimation.
Seven bivariate and three univariate stochastic order checks, each returning a logical flag and supporting diagnostic values.
| Paper | Journal | Authors |
|---|---|---|
| Bivariate Laplace transform of residual lives and their properties | Communications in Statistics — Theory and Methods (2022) | Jayalekshmi S., Rajesh G., Nair N.U. |
| Bivariate Laplace transform order and ordering of reversed residual lives | Int. J. Reliability, Quality and Safety Engineering | Jayalekshmi S., Rajesh G. |
dgumbel_biv, sgumbel_biv, rgumbel_biv, pgumbel_biv)dfgm_biv, pfgm_biv, sfgm_biv, rfgm_biv)dbivpower, pbivpower, sbivpower, rbivpower)sschur_biv, rschur_biv)blt_residual() — numerical computation for any survival functionblt_residual_gumbel() — closed-form for Gumbel distributionbiv_hazard_gradient() — bivariate hazard gradientbiv_mean_residual() — bivariate mean residual lifenbuhr_test() — NBUHR/NWUHR aging class testnp_blt_residual() — nonparametric estimatorsim_blt_residual() — Monte-Carlo simulation studyblt_reversed() — for any joint CDFblt_reversed_fgm() — closed form for FGMblt_reversed_power() — for bivariate power distributionbiv_rhazard_gradient() — reversed hazard gradientbiv_rmrl() — reversed mean residual lifelt_residual() — LT of residual life: E[e^{-sX} | X > t]hazard_rate() — hazard rate h(t) = f(t)/S(t)mean_residual() — mean residual life m(t) = E[X-t | X>t]np_lt_residual() — nonparametric estimatorblt_order_residual() — BLt-rl orderblt_order_reversed() — BLt-Rrl orderbiv_whr_order() — weak bivariate hazard rate orderbiv_wmrl_order() — weak bivariate MRL orderbiv_brlmr_order() — bivariate relative MRL orderbiv_wrhr_order() — weak bivariate reversed hazard rate orderbiv_wrmrl_order() — weak bivariate reversed MRL orderlt_rl_order() — Lt-rl order: L_X(s,t) ≤ L_Y(s,t) for all s, thr_order() — hazard rate order: h_X(t) ≤ h_Y(t) for all tmrl_order() — MRL order: m_X(t) ≤ m_Y(t) for all tshannon_entropy() — Shannon differential entropyinfo_gen_function() — Golomb information generating functionplot_blt_residual(), plot_blt_reversed()# Install from CRAN
install.packages("BivLaplaceRL")
# Development version from GitHub
# install.packages("devtools")
devtools::install_github("itsmdivakaran/BivLaplaceRL")
library(BivLaplaceRL)
# 1. Simulate from Gumbel bivariate exponential
set.seed(42)
dat <- rgumbel_biv(500, k1 = 1, k2 = 1, theta = 0.5)
# 2. Nonparametric estimate of BLT of residual lives
np_blt_residual(dat, s1 = 1, s2 = 1, t1 = 0.3, t2 = 0.3)
# 3. Compare with closed-form
blt_residual_gumbel(s1 = 1, s2 = 1, t1 = 0.3, t2 = 0.3, k1 = 1, k2 = 1, theta = 0.5)
# 4. Univariate LT of residual life for Exp(1)
f <- function(x) dexp(x, 1)
Fb <- function(x) pexp(x, 1, lower.tail = FALSE)
lt_residual(f, Fb, s = 1, t = 0.5)
# 5. Hazard rate and MRL
hazard_rate(f, Fb, t = c(0.5, 1, 2))
mean_residual(Fb, t = c(0, 0.5, 1, 2))
# 6. Check univariate stochastic orders: Exp(2) <=_hr Exp(1)?
f2 <- function(x) dexp(x, 2)
Fb2 <- function(x) pexp(x, 2, lower.tail = FALSE)
hr_order(f2, Fb2, f, Fb, t_grid = c(0.5, 1, 2))$order_holds
Mahesh Divakaran (maintainer) Research Scholar, Amity School of Applied Sciences, Amity University Lucknow [email protected]
S. Jayalekshmi, G. Rajesh, N. Unnikrishnan Nair Department of Statistics, Cochin University of Science and Technology
Jayalekshmi S., Rajesh G., Nair N.U. (2022). Bivariate Laplace transform of residual lives and their properties. Communications in Statistics — Theory and Methods. https://doi.org/10.1080/03610926.2022.2085874
Jayalekshmi S., Rajesh G. Bivariate Laplace transform order and ordering of reversed residual lives. International Journal of Reliability, Quality and Safety Engineering. https://doi.org/10.1142/S0218539322500061
Belzunce F., Ortega E., Ruiz J.M. (1999). The Laplace order and ordering of residual lives. Statistics & Probability Letters, 42(2), 145--156. https://doi.org/10.1016/S0167-7152(98)00202-8
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