quickNet

quickNet provides a compact interface for estimating, plotting, summarizing, and comparing psychological networks in R. The current API returns a unified quicknet_fit object, keeps the main legacy function names, and supports cross-sectional, cross-lagged panel, and intensive longitudinal network models.

Installation

Install from GitHub

if (!require(devtools)) {
  install.packages("devtools")
}

devtools::install_github("LeiGuo0812/quickNet")

Install from a local ZIP file

Click Code -> Download ZIP, then set your R working directory to the folder containing the downloaded ZIP file.

if (!require(devtools)) {
  install.packages("devtools")
}

devtools::install_local("quickNet-main.zip")

Some models require optional backend packages: PanelNet(model = "clpn") requires glmnet, PanelNet(model = "ri_clpm" / "panel_gvar" / "panel_var") requires psychonetrics, LongitudinalNet(model = "graphicalVAR") requires graphicalVAR, LongitudinalNet(model = "mlVAR") requires mlVAR, and LongitudinalNet(model = "psychonetrics_gvar") requires psychonetrics. Additional optional modules use powerly, psychonetrics, lavaan, and MASS for sample size planning, confirmatory networks, latent/residual networks, and SEM-based panel networks.

Unified Output

Main model-fitting functions return a quicknet_fit object. Common fields are:

fit$graph             # Default network matrix
fit$networks          # One or more network layers
fit$edges             # Edge table
fit$nodes             # Node-level table
summary(fit)          # Network-level summary
plot(fit)             # Quick network plot

Use model_registry() to inspect the package-level model map, including the model family, backend, analysis type, network layers, reportable quantities, key references, and known limitations.

model_registry()
model_registry("ConfirmatoryNet")

quicknet_report(fit) returns academic reporting tables. For psychonetrics-backed models, the report includes fit_indices, parameters, modification_indices, and constraints in addition to the common sample, network, edge, and node summaries.

Available Models

Data type	Function	Model name	Brief description
Cross-sectional continuous data	`quickNet()`	`"EBICglasso"`	Sparse Gaussian graphical model estimated with EBICglasso. This is the default option for continuous psychological network data.
Cross-sectional continuous data	`quickNet()`	`"correlation"`	Zero-order correlation network. Edges represent pairwise associations without conditioning on other variables.
Cross-sectional continuous data	`quickNet()`	`"partial"`	Partial correlation network. Edges represent conditional associations after adjusting for all other nodes.
Cross-sectional binary data	`quickNet()`	`"ising"`	Ising model for 0/1 variables. Useful for binary symptoms, endorsements, or event indicators.
Cross-sectional ordinal data	`quickNet()`	`"ordinal"`	Ordinal association network based on polychoric-style correlations. Useful for Likert-type items.
Cross-sectional mixed data	`quickNet()`	`"mgm"`	Mixed Graphical Model for combinations of Gaussian, categorical, and other variable types.
Wide-format panel data	`PanelNet()`	`"clpn"`	Cross-lagged panel network. Edges are directed from previous-wave nodes to next-wave nodes.
Wide-format panel data	`PanelNet()`	`"ri_clpm"`	Random-intercept cross-lagged panel model via `psychonetrics`, separating within-person dynamics from stable between-person differences.
Wide-format panel data	`PanelNet()`	`"panel_gvar"`	Panel graphical VAR via `psychonetrics::panelgvar()`, returning temporal, within-person, and between-person network layers.
Wide-format panel data	`PanelNet()`	`"panel_var"`	Panel VAR via `psychonetrics::panelvar()`, returning temporal and covariance-based within/between layers.
Wide-format panel data	`PanelSEMNet()`	`"panel_sem"`	SEM-based cross-lagged panel network with lavaan fit indices.
Long-format intensive longitudinal data	`LongitudinalNet()`	`"graphicalVAR"`	Multilevel graphical VAR model returning temporal, contemporaneous, and between-person networks.
Long-format intensive longitudinal data	`LongitudinalNet()`	`"mlVAR"`	Multilevel VAR model via `mlVAR`, also returning temporal, contemporaneous, and between-person networks.
Long-format intensive longitudinal data	`LongitudinalNet()`	`"psychonetrics_gvar"`	Lag-1 graphical VAR via `psychonetrics::gvar()`, returning temporal and contemporaneous networks.
Time-ordered mixed data	`MixedVARNet()`	`"mixedVAR"`	Mixed vector autoregressive network for continuous and categorical time-series variables.
Time-ordered mixed data	`TimeVaryingNet()`	`"time_varying_mvar"`	Time-varying mixed VAR networks estimated at user-defined time points.
Cross-sectional continuous data	`ConfirmatoryNet()`	`"confirmatory_ggm"`	Confirmatory Gaussian graphical model with user-specified free and fixed edges.
CFA/SEM data	`LatentNet()`	`"latent_network"`	Latent-variable correlation network and optional residual item network after CFA.
CFA/SEM data	`LatentNet()`	`"lvm"`	Psychonetrics latent variable model with latent and residual covariance layers.
CFA/SEM data	`LatentNet()`	`"lnm"`	Psychonetrics latent network model; the latent network is estimated as a GGM.
CFA/SEM data	`LatentNet()`	`"rnm"`	Psychonetrics residual network model; the residual item network is estimated as a GGM.
CFA/SEM data	`LatentNet()`	`"lrnm"`	Psychonetrics latent-and-residual network model with both GGM layers.
Meta-analytic correlation/covariance data	`MetaNet()`	`"meta_ggm"`	Psychonetrics meta-analytic Gaussian graphical model from multiple study matrices or study-level raw data.
Meta-analytic correlation/covariance data	`MetaNet()`	`"meta_cor"`	Psychonetrics meta-analytic pooled correlation network.
Meta-analytic intensive longitudinal data	`MetaNet()`	`"meta_gvar"`	Psychonetrics single-stage meta-analytic GVAR with temporal and contemporaneous network layers.
Confirmatory cross-sectional data	`ConfirmatoryNet()`	`"confirmatory_ising"`	Psychonetrics confirmatory Ising model for binary variables.
Confirmatory cross-sectional data	`ConfirmatoryNet()`	`"confirmatory_cor"`	Psychonetrics confirmatory correlation model.
Confirmatory cross-sectional data	`ConfirmatoryNet()`	`"confirmatory_covariance"`	Psychonetrics confirmatory covariance model.
Confirmatory cross-sectional data	`ConfirmatoryNet()`	`"confirmatory_precision"`	Psychonetrics confirmatory precision-matrix model.

Sample size planning uses a separate quicknet_power object returned by NetworkPower() or its alias SampleSize().

EBICglassoNet() is retained as a convenience wrapper for the cross-sectional model = "EBICglasso" workflow.

For academic use, cite the methods that match the models and analyses you report. The essential references are listed in References.

Input Requirements

Each model has explicit input checks. Use input_requirements() to inspect expected input format, or check_input() to diagnose a dataset before fitting.

input_requirements("ising")

check_input(binary_data, model = "ising")
check_input(panel_data, model = "clpn", nodes = c("x1", "x2", "x3"), waves = 1:3)
check_input(esm_data, model = "graphicalVAR", vars = c("x1", "x2", "x3"))

Model-fitting functions call the same validator internally. Clear format errors stop early; risk conditions such as very small samples or imbalanced binary variables are shown as warnings.

Minimal Examples

1. EBICglasso Cross-Sectional Network

library(quickNet)

fit <- quickNet(mtcars[, 1:6], model = "EBICglasso", pie = FALSE)

summary(fit)
fit$edges
plot(fit)

Legacy convenience interface:

fit <- EBICglassoNet(mtcars[, 1:6])

2. Correlation Network

fit <- quickNet(
  mtcars[, 1:6],
  model = "correlation",
  cor_method = "pearson",
  pie = FALSE
)

fit$graph
fit$nodes

3. Partial Correlation Network

fit <- quickNet(
  mtcars[, 1:6],
  model = "partial",
  cor_method = "pearson",
  pie = FALSE
)

summary(fit)

4. Ising Network for Binary Data

set.seed(1)
binary_data <- data.frame(
  x1 = rbinom(120, 1, 0.50),
  x2 = rbinom(120, 1, 0.45),
  x3 = rbinom(120, 1, 0.55),
  x4 = rbinom(120, 1, 0.50)
)

fit <- quickNet(binary_data, model = "ising", gamma = 0.25, pie = FALSE)

fit$edges
fit$nodes

5. Ordinal Network

set.seed(1)
ordinal_data <- data.frame(
  x1 = sample(1:5, 120, replace = TRUE),
  x2 = sample(1:5, 120, replace = TRUE),
  x3 = sample(1:5, 120, replace = TRUE),
  x4 = sample(1:5, 120, replace = TRUE)
)

fit <- quickNet(
  ordinal_data,
  model = "ordinal",
  ordinal_method = "polychoric",
  pie = FALSE
)

summary(fit)

6. Mixed Graphical Model

set.seed(1)
mixed_data <- data.frame(
  c1 = rnorm(120),
  c2 = rnorm(120),
  d1 = sample(1:2, 120, replace = TRUE),
  d2 = sample(1:2, 120, replace = TRUE)
)

fit <- quickNet(
  mixed_data,
  model = "mgm",
  types = c("g", "g", "c", "c"),
  levels = c(1, 1, 2, 2),
  gamma = 0.25,
  pie = FALSE
)

fit$nodes

Longitudinal Network Examples

7. Cross-Lagged Panel Network

PanelNet() expects wide-format panel data. By default, column names should follow the pattern node_twave, for example x1_t1 and x1_t2.

set.seed(1)
n <- 80
panel_data <- data.frame(id = seq_len(n))

for (wave in 1:3) {
  panel_data[[paste0("x1_t", wave)]] <- rnorm(n)
  panel_data[[paste0("x2_t", wave)]] <- rnorm(n)
  panel_data[[paste0("x3_t", wave)]] <- rnorm(n)
}

panel_fit <- PanelNet(
  panel_data,
  nodes = c("x1", "x2", "x3"),
  waves = 1:3,
  id = "id",
  nfolds = 5
)

panel_fit$networks$default       # Autoregressive and cross-lagged paths
panel_fit$networks$cross_lagged  # Cross-lagged paths only
panel_fit$edges

8. psychonetrics Panel Models

The same wide panel format can be used for random-intercept CLPM and panel GVAR models.

ri_fit <- PanelNet(
  panel_data,
  nodes = c("x1", "x2", "x3"),
  waves = 1:3,
  model = "ri_clpm"
)

panel_gvar <- PanelNet(
  panel_data,
  nodes = c("x1", "x2", "x3"),
  waves = 1:3,
  model = "panel_gvar"
)

ri_fit$networks$temporal
ri_fit$networks$random_intercept
panel_gvar$networks$within
panel_gvar$networks$between

9. graphicalVAR Intensive Longitudinal Network

LongitudinalNet() expects long-format data with a subject ID, a day/date variable, and a within-day measurement occasion variable.

set.seed(1)
ids <- rep(1:8, each = 12)
time <- rep(1:12, times = 8)

esm_data <- data.frame(
  id = ids,
  day = ceiling(time / 4),
  beep = ((time - 1) %% 4) + 1,
  x1 = rnorm(length(ids)),
  x2 = rnorm(length(ids)),
  x3 = rnorm(length(ids))
)

gvar_fit <- LongitudinalNet(
  esm_data,
  vars = c("x1", "x2", "x3"),
  id = "id",
  day = "day",
  beep = "beep",
  model = "graphicalVAR"
)

gvar_fit$networks$temporal
gvar_fit$networks$contemporaneous
gvar_fit$networks$between

10. psychonetrics GVAR Intensive Longitudinal Network

psy_gvar <- LongitudinalNet(
  esm_data,
  vars = c("x1", "x2", "x3"),
  id = "id",
  day = "day",
  beep = "beep",
  model = "psychonetrics_gvar"
)

psy_gvar$networks$temporal
psy_gvar$networks$contemporaneous

11. mlVAR Intensive Longitudinal Network

mlvar_fit <- LongitudinalNet(
  esm_data,
  vars = c("x1", "x2", "x3"),
  id = "id",
  day = "day",
  beep = "beep",
  model = "mlVAR",
  temporal = "fixed",
  contemporaneous = "fixed",
  nCores = 1
)

mlvar_fit$edges
mlvar_fit$nodes

12. Network Power and Sample Size Planning

power <- NetworkPower(
  nodes = 8,
  density = 0.30,
  sample_sizes = c(100, 200, 400),
  replications = 100,
  target_metric = "sensitivity",
  target_value = 0.60,
  target_probability = 0.80
)

summary(power)
plot(power)
quicknet_report(power)$text

For powerly-based GGM planning:

powerly_plan <- NetworkPower(
  method = "powerly",
  nodes = 8,
  density = 0.30,
  target_metric = "sensitivity",
  target_value = 0.60,
  target_probability = 0.80
)

13. Confirmatory, Latent, and Dynamic Networks

omega <- matrix(1, 6, 6)
diag(omega) <- 0
colnames(omega) <- rownames(omega) <- paste0("x", 1:6)

confirmatory <- ConfirmatoryNet(data, vars = paste0("x", 1:6), omega = omega)

confirmatory_cor <- ConfirmatoryNet(data, vars = paste0("x", 1:6), model = "cor")
confirmatory_precision <- ConfirmatoryNet(data, vars = paste0("x", 1:6), model = "precision")

confirmatory_ising <- ConfirmatoryNet(binary_data, model = "ising")
confirmatory_ising$networks$default

cfa_model <- "
Depression =~ d1 + d2 + d3
Anxiety    =~ a1 + a2 + a3
"

latent <- LatentNet(data, model = cfa_model)
latent$networks$latent
latent$networks$residual

lambda <- matrix(0, 6, 2, dimnames = list(paste0("x", 1:6), c("Depression", "Anxiety")))
lambda[1:3, "Depression"] <- 1
lambda[4:6, "Anxiety"] <- 1

lnm <- LatentNet(data, model = "lnm", vars = paste0("x", 1:6), lambda = lambda)
lrnm <- LatentNet(data, model = "lrnm", vars = paste0("x", 1:6), lambda = lambda)

lnm$networks$latent
lrnm$networks$residual

panel_sem <- PanelSEMNet(panel_data, nodes = c("x1", "x2", "x3"), waves = 1:3)

mixed_var <- MixedVARNet(
  time_data,
  types = c("g", "g", "c"),
  levels = c(1, 1, 2)
)

tv_mvar <- TimeVaryingNet(
  time_data,
  types = c("g", "g", "c"),
  levels = c(1, 1, 2),
  estpoints = c(0.25, 0.50, 0.75)
)

14. Meta-Analytic Networks

MetaNet() estimates psychonetrics meta-analytic network models from multiple study correlation/covariance matrices or multi-study raw data.

cors <- list(study1_cor, study2_cor, study3_cor)
nobs <- c(150, 180, 220)

meta_ggm <- MetaNet(
  cors = cors,
  nobs = nobs,
  vars = c("x1", "x2", "x3"),
  model = "meta_ggm"
)

meta_ggm$networks$default
quicknet_report(meta_ggm)$sample

For multi-study intensive longitudinal data:

meta_gvar <- MetaNet(
  data = multi_study_esm,
  studyvar = "study",
  vars = c("x1", "x2", "x3"),
  id = "id",
  day = "day",
  beep = "beep",
  model = "meta_gvar"
)

meta_gvar$networks$temporal
meta_gvar$networks$contemporaneous

Common Follow-Up Analyses

Centrality and Bridge Centrality

fit <- quickNet(mtcars[, 1:6], pie = FALSE)

centrality <- Centrality(fit)
centrality$node_table

bridge <- Bridge(
  fit,
  communities = list(group1 = 1:3, group2 = 4:6)
)
bridge$bridge_data

Stability Analysis

Cross-sectional network:

fit <- quickNet(mtcars[, 1:6], model = "correlation", pie = FALSE)
stability <- Stability(fit, nboot = 100)

stability$edge_bootstrap_stability
stability$case_drop_centrality_stability

Longitudinal network:

longitudinal_stability <- LongitudinalStability(panel_fit, nboot = 100)

Academic Reporting Parameters

Use quicknet_report() to extract report-ready sample information, estimation settings, network summaries, edge summaries, node-level indices, and model-specific parameters from any quicknet_fit object.

fit <- quickNet(mtcars[, 1:6], model = "EBICglasso", pie = FALSE)
report <- quicknet_report(fit)

report$sample          # Sample size and node count
report$estimation      # Estimator and tuning parameters
report$networks        # Density and edge-weight summary by network layer
report$edges           # Positive/negative/nonzero edge counts
report$nodes           # Node-level centrality and predictability
report$model_specific  # Model-specific report fields
report$text            # Short plain-language summary

Virtual Perturbation and Intervention Simulation

Use Perturbation() for model-implied in silico perturbation analyses. These results are useful for hypothesis generation and candidate target screening, but they should not be interpreted as causal intervention effects.

fit <- quickNet(mtcars[, 1:6], model = "partial", pie = FALSE)

dosage <- Perturbation(
  fit,
  method = "dosage",
  targets = c("mpg", "cyl"),
  dose = c(0.25, 0.50, 1.00)
)

dosage$metrics
dosage$rankings
quicknet_report(dosage)$text

plot(dosage)
get_perturbation_plot(dosage, type = "rank")
get_perturbation_plot(dosage, type = "dose_response")
get_perturbation_plot(dosage, type = "node_change", perturbation_id = 1)

Continuous networks support Gaussian conditioning, virtual knockout, virtual knockdown, precision-edge blocking, combination perturbation, and greedy sequence optimization:

Perturbation(fit, method = "knockout", targets = "mpg")
Perturbation(fit, method = "knockdown", targets = "mpg", remaining_strength = 0.50)
blocked <- Perturbation(fit, method = "edge_block", targets = "mpg")
Perturbation(fit, method = "combination", targets = c("mpg", "cyl", "disp"))
sequence <- Perturbation(fit, method = "sequence", targets = c("mpg", "cyl", "disp"), steps = 2)

get_perturbation_plot(blocked, type = "edge_block")
get_perturbation_plot(sequence, type = "sequence")

For Ising models, use NIRA-style threshold perturbation:

ising_fit <- quickNet(binary_data, model = "ising", gamma = 0.25, pie = FALSE)

ising_result <- Perturbation(
  ising_fit,
  method = "ising_threshold",
  targets = c("b1", "b2"),
  threshold_shift = -0.5
)

get_perturbation_plot(ising_result, type = "rank")
get_perturbation_plot(ising_result, type = "node_change", target = "b1")

Perturbation plots are intentionally conservative. They display only computed simulation summaries: target ranking, dosage response, node-level state or activity change, edge-block spillover summaries, and greedy sequence steps. They do not imply clinical efficacy or causal treatment effects.

This interpretation follows the network intervention and simulation literature, while also respecting cautions that centrality or model-implied perturbation rankings should not be treated as direct causal treatment effects without a suitable causal design.

Network Comparison

net1 <- quickNet(mtcars[, 1:6], pie = FALSE)
net2 <- quickNet((mtcars[, 1:6])^2, pie = FALSE)

comparison <- NetCompare(mtcars[, 1:6], (mtcars[, 1:6])^2, it = 100)
plots <- get_compare_plot(comparison, net1, output = FALSE)

Export Plots and Tables

fit <- quickNet(mtcars[, 1:6], pie = FALSE)

get_network_plot(fit, path = tempdir(), prefix = "example")
get_edges_df(fit)
globalCoeff(fit)

References

If you use quickNet in academic work, cite the package and the method references relevant to the models or helper functions used:

Cross-sectional psychological network estimation and visualization: Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1-18. https://doi.org/10.18637/jss.v048.i04
Network estimation, accuracy, and stability: Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50, 195-212. https://doi.org/10.3758/s13428-017-0862-1
EBIC model selection for Gaussian graphical models: Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. Advances in Neural Information Processing Systems, 23. https://proceedings.neurips.cc/paper/2010/hash/072b030ba126b2f4b2374f342be9ed44-Abstract.html
Regularized generalized linear models used by panel-network backends: Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22. https://doi.org/10.18637/jss.v033.i01
Binary Ising networks: van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports, 4, 5918. https://doi.org/10.1038/srep05918
Mixed Graphical Models: Haslbeck, J. M. B., & Waldorp, L. J. (2020). mgm: Estimating time-varying mixed graphical models in high-dimensional data. Journal of Statistical Software, 93(8), 1-46. https://doi.org/10.18637/jss.v093.i08
Cross-sectional and time-series Gaussian graphical models, including graphicalVAR-style models: Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453-480. https://doi.org/10.1080/00273171.2018.1454823
Longitudinal psychopathology networks and vector autoregression: Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., Borsboom, D., & Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLOS ONE, 8(4), e60188. https://doi.org/10.1371/journal.pone.0060188
Network sample size planning: Constantin, M. A., Schuurman, N. K., & Vermunt, J. K. (2021). A general Monte Carlo method for sample size analysis in the context of network models. https://doi.org/10.31234/osf.io/j5v7u
Generalized network psychometrics and confirmatory network models: Epskamp, S., Rhemtulla, M., & Borsboom, D. (2017). Generalized network psychometrics: Combining network and latent variable models. Psychometrika, 82, 904-927. https://doi.org/10.1007/s11336-017-9557-x
Random-intercept cross-lagged panel models: Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102-116. https://doi.org/10.1037/a0038889
Meta-analytic structural equation modeling: Jak, S., & Cheung, M. W.-L. (2020). Meta-analytic structural equation modeling with moderating effects on SEM parameters. Psychological Methods, 25(4), 430-455. https://doi.org/10.1037/met0000245
SEM/CFA backend: Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
Predictability in network models: Haslbeck, J. M. B., & Waldorp, L. J. (2018). How well do network models predict observations? On the importance of predictability in network models. Behavior Research Methods, 50, 853-861. https://doi.org/10.3758/s13428-017-0910-x
Bridge centrality: Jones, P. J., Ma, R., & McNally, R. J. (2021). Bridge centrality: A network approach to understanding comorbidity. Multivariate Behavioral Research, 56(2), 353-367. https://doi.org/10.1080/00273171.2019.1614898
Network comparison testing: van Borkulo, C. D., van Bork, R., Boschloo, L., Kossakowski, J. J., Tio, P., Schoevers, R. A., Borsboom, D., & Waldorp, L. J. (2023). Comparing network structures on three aspects: A permutation test. Psychological Methods, 28(6), 1273-1285. https://doi.org/10.1037/met0000476
Network Intervention Analysis: Blanken, T. F., van der Zweerde, T., van Straten, A., van Someren, E. J. W., Borsboom, D., & Lancee, J. (2019). Introducing Network Intervention Analysis to investigate sequential, symptom-specific treatment effects: A demonstration in co-occurring insomnia and depression. Psychotherapy and Psychosomatics, 88(1), 52-54. https://doi.org/10.1159/000495045
Simulation-based intervention target evaluation: Lunansky, G., van Borkulo, C. D., & Borsboom, D. (2021). Intervening on psychopathology networks: Evaluating intervention targets through simulations. PsyArXiv. https://doi.org/10.31234/osf.io/sqhje
Interpreting centrality cautiously: Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., Wigman, J. T. W., & Snippe, E. (2019). What do centrality measures measure in psychological networks? Journal of Abnormal Psychology, 128(8), 892-903. https://doi.org/10.1037/abn0000446

Changelog

Development version

Introduced the unified quicknet_fit object for cross-sectional and longitudinal network models.
Extended cross-sectional support to EBICglasso, correlation, partial correlation, Ising, ordinal, and MGM networks through a consistent quickNet() interface.
Added longitudinal modeling interfaces: PanelNet() for cross-lagged panel networks and LongitudinalNet() for graphicalVAR and mlVAR models.
Added model-agnostic edge tables, node tables, network summaries, centrality helpers, and stability summaries.
Added virtual perturbation and intervention simulation helpers for Gaussian-style networks and Ising threshold perturbation.
Added literature-aligned perturbation plotting helpers for ranking, dosage response, node-level change, edge blocking, and greedy sequence summaries.
Added NetworkPower() / SampleSize() for simulation-based network sample size planning.
Added confirmatory, latent, SEM-panel, mixed VAR, and time-varying mixed VAR network wrappers.
Added psychonetrics-backed panel and longitudinal models: PanelNet(model = "ri_clpm"), PanelNet(model = "panel_gvar"), PanelNet(model = "panel_var"), and LongitudinalNet(model = "psychonetrics_gvar").
Added psychonetrics latent/residual network models through LatentNet(model = "lvm"), "lnm", "rnm", and "lrnm".
Added psychonetrics meta-analytic network models through MetaNet(model = "meta_ggm"), "meta_cor", and "meta_gvar".
Extended ConfirmatoryNet() to psychonetrics confirmatory Ising, correlation, covariance, and precision-matrix models.
Added model-specific input requirement helpers and automatic input validation for main fitting functions.
Added essential method references for supported network models, stability, bridge centrality, network comparison, and perturbation analyses.
Updated legacy helper functions so they work with the new quicknet_fit object.
Added testthat coverage for cross-sectional and longitudinal workflows.
Cleaned package documentation, examples, spelling, and R CMD check issues.

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NAMESPACE		NAMESPACE
README.md		README.md
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quickNet

Installation

Install from GitHub

Install from a local ZIP file

Unified Output

Available Models

Input Requirements

Minimal Examples

1. EBICglasso Cross-Sectional Network

2. Correlation Network

3. Partial Correlation Network

4. Ising Network for Binary Data

5. Ordinal Network

6. Mixed Graphical Model

Longitudinal Network Examples

7. Cross-Lagged Panel Network

8. psychonetrics Panel Models

9. graphicalVAR Intensive Longitudinal Network

10. psychonetrics GVAR Intensive Longitudinal Network

11. mlVAR Intensive Longitudinal Network

12. Network Power and Sample Size Planning

13. Confirmatory, Latent, and Dynamic Networks

14. Meta-Analytic Networks

Common Follow-Up Analyses

Centrality and Bridge Centrality

Stability Analysis

Academic Reporting Parameters

Virtual Perturbation and Intervention Simulation

Network Comparison

Export Plots and Tables

References

Changelog

Development version

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