README.md

In idiographic: Person-Specific (Idiographic) and Heterogeneous Complex Networks

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:

| Estimator | Method | Validated against | Agreement | |---|---|---|---| | fit_graphical_var() | Regularized graphical VAR (graphical lasso + EBIC) | graphicalVAR | committed tolerance 1e-6 across the supported lag-1 beta/kappa option matrix | | fit_mlvar() | Multilevel and person-specific VAR | mlVAR 0.7.3 | committed tolerance 1e-8 across 20 real ESM panels plus fixed lmer lag 1/1+2, preprocessing, and lag-1 lm/unique oracle slices | | fit_gimme() | Group and individual uSEM path search | gimme 10.0 | exact search/matrix agreement on bivariate and three-variable standard/hybrid/VAR panels, including exogenous and uneven-panel structures; fit tables within 5e-5 | | fit_mlvar_bayes() | Native Bayesian multilevel VAR / DSEM | real Mplus DSEM + Stan/JAGS | Monte-Carlo error | | fit_var_bayes() | Native Bayesian VAR(1) | real Mplus ESTIMATOR = BAYES | committed statistical bounds 0.02-0.03 |

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: Idiographic Person-Specific and Heterogeneous Complex Networks. R package. https://github.com/mohsaqr/idiographic

License

GPL-3.

idiographic documentation built on Aug. 4, 2026, 1:07 a.m.