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antedep

antedep fits antedependence models for longitudinal data:

Gaussian AD models for continuous outcomes
INAD models for count outcomes
Categorical AD (CAT) models for discrete state outcomes

Installation

# install.packages("remotes")
remotes::install_github("TanchyKing/antedep")

Production-Readiness Matrix

Model	Data type	Complete-data fit/logLik	Missing-data fit/logLik	Notes
AD	Continuous	Ready	Ready (`fit_gau`, `logL_gau`)	Missing-data fit uses EM or observed-data likelihood modes
INAD	Counts	Ready	Ready (`fit_inad`, `logL_inad`)	Missing-data fit supports `na_action = "marginalize"`
CAT	Categorical states	Ready	Ready (`fit_cat`, `logL_cat`)	Missing-data fit supports orders 0, 1, 2

Quick Start

Included datasets:

bolus_inad (morphine bolus counts)
cattle_growth (book companion cattle growth data; continuous)
cochlear_implant (book companion speech recognition/cochlear data; continuous)
labor_force_cat (labor-force categorical table expanded to subject sequences)
race_100km (100km race split times; continuous)

AD example (continuous)

library(antedep)
set.seed(1)

y <- simulate_gau(n_subjects = 80, n_time = 5, order = 1)
fit <- fit_gau(y, order = 1)
fit$log_l

Missing-data workflow

library(antedep)
set.seed(1)

y <- simulate_inad(n_subjects = 60, n_time = 5, order = 1)
y[sample(length(y), 20)] <- NA

# Fit observed-data likelihood under MAR
fit_miss <- fit_inad(y, order = 1, na_action = "marginalize")
fit_miss$log_l

CAT missing-data workflow

library(antedep)
set.seed(1)

y_cat <- simulate_cat(n_subjects = 80, n_time = 5, order = 1, n_categories = 3)
y_cat[sample(length(y_cat), 30)] <- NA

# Observed-data likelihood (orders 0/1/2)
fit_cat_marg <- fit_cat(y_cat, order = 1, na_action = "marginalize")

# EM (orders 0/1) via explicit entry point or fit_cat dispatch
fit_cat_em1 <- em_cat(y_cat, order = 1, max_iter = 80)
fit_cat_em2 <- fit_cat(y_cat, order = 1, na_action = "em", em_max_iter = 80)

If EM becomes unstable or converges slowly, try increasing epsilon, increasing max_iter, and using safeguard = TRUE.

Known Limitations

EM entry points: em_gau (Gaussian), em_inad (INAD), and em_cat (CAT, orders 0/1) are available; for CAT order 2 with missing data, use fit_cat(na_action = "marginalize").
For CAT models, fit_cat() supports both observed-data likelihood (na_action = "marginalize") and EM (na_action = "em" for orders 0/1).
Missing-data confidence intervals are not yet implemented (ci_gau, ci_inad, ci_cat require complete-data fits).
AD missing-data EM for order 2 is currently available with simplified updates and should be treated as provisional.
AD missing-data LRT/mean/covariance tests remain complete-data only.
CAT missing-data stationarity/time-invariance tests (lrt_stationarity_cat, lrt_timeinvariance_cat, run_stationarity_tests_cat) remain complete-data only.
INAD missing-data LRTs and CAT missing-data order/homogeneity LRTs are supported via observed-data likelihood (na_action = "marginalize").
Current model order support is up to order 2 for AD, INAD, and CAT.
CAT missing-data marginalization currently supports orders 0, 1, and 2.

Vignette

Source vignette: vignettes/antedep-intro.Rmd. Rendered site article: docs/articles/antedep-intro.html. Integrated function reference: docs/reference/index.html.

Function Reference Site (pkgdown)

Local build: Rscript -e "pkgdown::build_site(preview = FALSE)"
CI deployment: .github/workflows/pkgdown.yml (GitHub Pages via Actions)

Local Check Guide

See LOCAL_CHECKS.md for tarball-mode vs directory-mode checks and expected NOTE messages.

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.