Person-Specific (Idiographic) and Heterogeneous Complex Networks

Person-specific and within-person network estimation from intensive longitudinal and panel data. Estimators include ordinary vector autoregression (VAR), graphical vector autoregression (graphical VAR), multilevel vector autoregression (mlVAR), rolling ordinary and graphical VAR, native Bayesian VAR and multilevel Bayesian VAR, unified Structural Equation Modeling (uSEM), and Group Iterative Multiple Model Estimation (GIMME). All estimators are native clean-room implementations. All functions are validated against authoritative literature. Also provides preprocessing audits, edge-stability diagnostics, model-comparison reports, and rolling forecast validation. Methods are described in and .

idiographic

Network estimation from intensive longitudinal data — person-specific and within-person temporal, contemporaneous, and between-subject networks from ESM / EMA / diary panels, through one tidy verb per method.

idiographic estimates dynamic networks from intensive longitudinal data (ILD): ordinary and regularized vector autoregression, multilevel VAR, native Bayesian multilevel VAR validated against selected Mplus DSEM fixtures, unified SEM, and GIMME — plus the supporting workflow (preprocessing audits, edge-stability diagnostics, rolling windows, forecast validation, model comparison, and idiographic supervised machine-learning models for individualized prediction). Every result has tidy as.data.frame() and summary() views. Network estimates additionally share edges(), nodes(), coefs(), matrices(), plot(), and as_netobject().

Clean-room by design

The core estimators are native R implementations of the published modelling targets, with a consistent interface and validation against reference outputs where a reference implementation is available:

The CRAN package is offline-first: its only imports are the standard R packages stats, utils, and parallel, which ship with R. It has no mandatory third-party package dependency. lme4 and lavaan are optional engines for multilevel frequentist VAR and SEM/GIMME respectively; plotting and the licensed Mplus bridge are optional too. Competitor packages and the 20-panel oracle corpus live in the repository's separate validation/ lane and are not shipped in the CRAN tarball.

The Bayesian DSEM sampler is a particular highlight: fit_mlvar_bayes() targets the output of mlVAR::mlVAR(estimator = "Mplus") — Mplus's two-level Bayesian VAR with latent mean centring — without Mplus installed, using a pure-R conjugate Gibbs sampler with hand-rolled inverse-Wishart draws (no MCMCpack/rstan). The committed evidence consists of fixed bivariate Mplus fixtures, one univariate random-AR fixture, and parameter-recovery tests; use equivalence(fit) to inspect the precise scope rather than assuming blanket DSEM equivalence.

Installation

The core can be installed from a downloaded source tarball without network access; optional engines are only checked when their corresponding methods are called.

From CRAN:

install.packages("idiographic")

From the author's r-universe (recommended — no compilation, binaries included):

install.packages("idiographic",
                 repos = c("https://mohsaqr.r-universe.dev",
                           "https://cloud.r-project.org"))

Or from GitHub:

# install.packages("pak")
pak::pak("mohsaqr/idiographic")

Plotting uses the cograph package; it stays optional and is offered for on-demand install the first time you call plot().

Quick start

library(idiographic)

## simulate an ESM panel: 30 people, 40 beeps, 3 items
set.seed(1)
panel <- do.call(rbind, lapply(1:30, function(id) {
  y <- matrix(0, 40, 3)
  for (t in 2:40) y[t, ] <- c(0.35, 0.30, 0.25) * y[t - 1, ] + rnorm(3)
  data.frame(id = id, beep = 1:40, A = y[, 1], B = y[, 2], C = y[, 3])
}))

## multilevel VAR: temporal, contemporaneous, and between networks
fit <- fit_mlvar(panel, vars = c("A", "B", "C"), id = "id", beep = "beep")

fit                 # tidy printout of all three networks
edges(fit)          # one row per edge (network, from, to, weight)
coefs(fit)          # fixed-effect estimates with SE / p / CI
plot(fit)           # draw all layers with cograph
plot(fit, layer = "temporal")

## the same call through the registry-driven front door
fit2 <- fit_idiographic(
  panel, method = "mlvar",
  params = list(vars = c("A", "B", "C"), id = "id", beep = "beep")
)
equivalence(fit2)  # exact validation scope and tolerance declaration

## inspect the complete package and argument-by-argument evidence ledgers
equivalence_table()
argument_coverage("mlvar")

All fitting functions use named, readable arguments. list_estimators(), estimator_info(), and get_estimator() expose the registry; custom methods can be added with register_estimator(). equivalence_table() reports the package-wide evidence status, while argument_coverage() guarantees every current public formal is classified as oracle/engine/statistical/internal, delegated, extension, or an explicit rejection boundary.

Together these ledgers provide complete package-wide evidence closure: there are no unassessed registered methods or arguments. Numerical equivalence remains method- and configuration-specific rather than a blanket package claim.

Native Bayesian DSEM (no Mplus needed)

bayes <- fit_mlvar_bayes(panel, vars = c("A", "B", "C"),
                           id = "id", beep = "beep",
                           n_iter = 4000, n_chains = 2)
bayes               # posterior medians, SDs, 95% CIs, convergence (max PSR)
coefs(bayes)

## full DSEM with person-specific slopes, random residuals, and
## within-model imputation of missing observations (needs enough subjects to
## identify the random-effect covariance: at least 2 * (p + p^2) + 1):
fit_mlvar_bayes(panel, vars = c("A", "B", "C"), id = "id", beep = "beep",
                  temporal = "random", residual = "random", impute = TRUE)

What's included

Estimators

• fit_var() / fit_var_each() — ordinary VAR(1) (OLS), pooled or per subject
• fit_graphical_var() / fit_graphical_var_each() — regularized graphical VAR (GLASSO + EBIC), including explicit multi-lag layers
• fit_mlvar() — frequentist multilevel VAR with fixed, correlated, orthogonal, or unique person-specific temporal/contemporaneous structures
• fit_mlvar_bayes() — native Bayesian multilevel VAR / DSEM (fixed or random slopes, fixed or random residual covariance, optional within-model imputation)
• fit_var_bayes() — native Bayesian VAR(1)
• fit_mlvar_mplus() — true-Mplus backend (wraps mlVAR(estimator = "Mplus"))
• fit_usem() — unified Structural Equation Modeling (lavaan)
• fit_gimme() — Group Iterative Multiple Model Estimation with explicit Bonferroni/FDR corrections, alpha, and stopping criteria
• fit_ml() — individualized supervised prediction models, comparing person-specific models against a pooled baseline on held-out within-person rows, with no new dependencies

Workflow & diagnostics

• preprocess() — preprocessing audit for ILD (compliance, variance, stationarity)
• estimate_stability() — bootstrap edge-stability diagnostics (experimental)
• fit_rolling_var() / fit_rolling_graphical_var() — rolling-window (time-varying) networks
• validate_forecast() — rolling out-of-sample forecast validation (experimental)
• compare_idiographic() — model-comparison reports

Tidy contract

Every result: as.data.frame() · summary() · print()

Network results: edges() · nodes() · coefs() · matrices() · plot() / plot_gimme() · as_netobject()

Idiographic machine learning

ml <- fit_ml(
  panel,
  outcome = "A",
  predictors = c("B", "C"),
  id = "id",
  beep = "beep",
  compare = "both",
  model = c("linear", "ridge", "knn")
)

ml                 # per-person and pooled held-out performance
ml$metrics         # MAE / RMSE / bias / R-squared by subject and overall
coefs(ml)          # coefficients for each individualized and pooled model
ml$predictions     # row-level held-out predictions

Use model = "all" to run all native models for the selected task. For regression this includes mean baseline, OLS (linear), ridge, lasso, elastic net, PCR, kNN, and a one-split tree. For binary classification this includes majority baseline, logistic regression, ridge/lasso/elastic-net logistic, LDA, Gaussian naive Bayes, kNN, and a one-split tree. Use estimator = "native" explicitly only when you want to pin the implementation; future package backends should live behind the same model name.

Bundled data

• srl — a self-regulated-learning ESM dataset (data(srl))
• inst/extdata/esm_demo.tsv — a small synthetic demo panel

Documentation

Package page and binaries: https://mohsaqr.r-universe.dev/idiographic.

Citation

Saqr, M., & López-Pernas, S. (2026). idiographic: Person-Specific (Idiographic) and Heterogeneous Complex Networks. R package. https://github.com/mohsaqr/idiographic

License

GPL-3.