Changes in version 0.1.0 (2026-05-27)                  

Initial CRAN release

  - Initial CRAN release of clusteredMSM.

Highlights

  - Nonparametric estimation of population-averaged transition
    probabilities for clustered or independent multistate process data,
    with cluster-bootstrap pointwise confidence intervals and
    simultaneous confidence bands.
  - Two-sample Kolmogorov-Smirnov-type test, dispatched automatically
    when a grouping variable appears on the formula's right-hand side.
    Three regimes via a design argument: "shared" (multicenter, every
    cluster carries both groups), "cluster_random" (cluster-randomized
    trial, stratified cluster bootstrap), and "indep_random"
    (independent observational comparison, unstratified cluster
    bootstrap). "auto" infers the regime from the data.
  - Native support for non-monotone (recovery) multistate processes via
    a generic product-integral estimator (prodint_AJ()).
  - Self-contained: depends only on survival (which ships with R).
    Removes the dependency on mstate that limited earlier
    implementations to progressive models.
  - Estimation methodology from Bakoyannis (2021)
    doi:10.1111/biom.13327; two-sample inference for the
    cluster-randomized and independent-samples designs follows
    Bakoyannis and Bandyopadhyay (2022) doi:10.1007/s10463-021-00819-x.

Public API

The package exposes a single user-facing function, patp(), with a
formula-based interface modelled after survival::Surv():

# One-sample
patp(msm(Tstart, Tstop, Sstart, Sstop) ~ 1,
     data = mydata, tmat = tmat,
     id = "subj_id", cluster = "site",
     h = 1, j = 2, B = 1000, cband = TRUE)

# Two-sample (estimate + test)
patp(msm(Tstart, Tstop, Sstart, Sstop) ~ treatment,
     data = mydata, tmat = tmat,
     id = "subj_id", cluster = "site",
     h = 1, j = 2, B = 1000)

patp() returns an S3 object of class patp with print() and summary()
methods.

Input format

Data are supplied in interval format: one row per mutually-exclusive
time interval per subject, with columns for interval start time, end
time, starting state, and ending state. Censoring is encoded as Sstart
== Sstop on the final row. Within each subject, intervals must be
temporally and spatially contiguous; this is enforced by strict
validation (validate_intervals()).

Known limitations

  - The two-sample Kolmogorov-Smirnov-type test uses unit weight at
    every time point. Bakoyannis (2021) Section 2.5 recommends the
    harmonic-mean weight W(t) = prod_p Y_p(t) / sum_p Y_p(t), which
    downweights regions where one group's at-risk set is small and tames
    tail instability of the difference estimator. A weighted variant
    following Bakoyannis (2021) Section 2.5 and Bakoyannis &
    Bandyopadhyay (2022) is planned for v0.2, alongside the linear and
    L2 tests.
  - Confidence bands use an equal-precision-type construction on the
    cloglog scale; the Hall-Wellner-type construction from Bakoyannis
    (2021) Section 2.3 will be added in v0.2.