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marp

marp fits six parametric renewal-process models and provides AIC-based model selection and model-averaged estimates. It also implements percentile and studentized bootstrap confidence intervals.

Installation

Install the released version from CRAN:

install.packages("marp")

The development version is available from GitHub:

# install.packages("remotes")
remotes::install_github("kanji709/marp")

Basic workflow

library(marp)

set.seed(42)
dat <- rgamma(100, shape = 3, rate = 0.01)

# m controls repeated random-start optimizations for candidate models that
# use nlm(); t contains the times for log-hazard evaluation.
m <- 10
t <- seq(100, 200, by = 10)
y <- 304

# Model codes are 1 Poisson, 2 Gamma, 3 log-logistic, 4 Weibull,
# 5 log-normal, and 6 Brownian passage time (BPT).
fit <- marp(dat, t, m, y, which.model = 2)
fit
summary(fit)

The fitted object retains the original named list components for backward compatibility. For example, AIC weights and model-averaged estimates remain available through $:

fit$weights_AIC
fit$mu_aic
fit$pr_aic    # logit-transformed event probability at y
fit$haz_aic   # model-averaged log-hazards at t

Bootstrap confidence intervals use the original data and can be computationally expensive, especially when both B and BB are large. The standard S3 interface delegates to the existing bootstrap implementation:

## Not run:
# ci <- confint(fit, data = dat, B = 99, BB = 99, level = 0.95)
# ci

See vignette("marp-workflow") for a fuller workflow and interpretation of the returned quantities.

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.