Package {bvarnet}

Title: Bayesian Estimation of Dynamic VAR Models using Stan
Version: 1.0.2
Author: Florian Metwaly ORCID iD [aut, cre, cph]
Maintainer: Florian Metwaly <f.j.metwaly@uva.nl>
Description: Bayesian estimation of multilevel Vector Autoregression (VAR) models using Stan. Supports Gaussian, Binary, and Ordinal (adjacent category) outcome variables with random effects and customizable priors.
License: GPL (≥ 3)
Copyright: file inst/COPYRIGHTS
Encoding: UTF-8
URL: https://flo1met.github.io/bvarnet/, https://github.com/flo1met/bvarnet
BugReports: https://github.com/flo1met/bvarnet/issues
RoxygenNote: 7.3.3
Additional_repositories: https://stan-dev.r-universe.dev
Imports: digest, instantiate, jsonlite, logspline, mvtnorm, posterior
Suggests: cmdstanr, codetools, bayesplot, knitr, qgraph, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Config/Needs/website: pkgdown
VignetteBuilder: knitr
Depends: R (≥ 4.0.0)
LazyData: true
NeedsCompilation: yes
Packaged: 2026-08-31 16:04:16 UTC; florianmetwaly
Repository: CRAN
Date/Publication: 2026-08-31 20:20:02 UTC

bvarnet: Bayesian Estimation of Dynamic VAR Models using STAN

Description

logo

Bayesian estimation of multilevel Vector Autoregression (VAR) models using Stan. Supports Gaussian, Binary, and Ordinal (adjacent category) outcome variables with random effects and customizable priors.

Author(s)

Maintainer: Florian Metwaly f.j.metwaly@uva.nl (ORCID) [copyright holder]

See Also

Useful links:

Compute Savege-Dickey Bayes factors

Description

Computes Savage-Dickey density ratio Bayes factors for each (requested set of) parameter in the model. By default, all applicable parameters are tested and returned in a tidy data frame. The type argument controls which parameter groups are included; the variable argument can be used to filter to effects involving specific variables. The log_BF10 argument allows including the natural log of the Bayes factor in the output, and round controls numeric rounding of the results.

Usage

bf_table(
  object,
  type = "all",
  lag = 1L,
  null_value = 0,
  variable = NULL,
  log_BF10 = FALSE,
  round = 5L
)

Arguments

object

A bvarnet object returned by bvar().

type

Character vector specifying which parameter groups to test. Use "all" (default) to include all applicable groups automatically. Available options:

"ar"

Autoregressive effects (self-loops). Per-cell BFs for the lag specified by lag, plus a joint BF.

"cl"

Cross-lagged effects. Same structure as "ar".

"intercepts"

Intercept parameters. Skipped automatically for ordinal outcomes.

"fe"

Non-intercept fixed effects (covariates).

"lag_fe"

Joint BFs for lag \times covariate interaction terms. Only available when the model was fitted with fe_interactions containing lag terms.

"temporal"

Joint BF for the entire temporal structure (all AR + CL parameters across all lags). When lag \times covariate interactions are present, additional omnibus rows are included.

lag

Integer; which lag block to use (default 1). Applies to "ar" and "cl" types.

null_value

Numeric scalar; the null hypothesis value (default 0).

variable

Character vector or NULL (default). One or more variable names. When set, only effects involving these variables are included.

log_BF10

Logical; if TRUE, an additional log_BF10 column (natural log of BF10) is appended to the output. Default is FALSE.

round

Integer or NULL; number of decimal places to round numeric output columns. Default is 5. Set to NULL to disable rounding.

Value

A data frame with columns: type, predictor, outcome, BF10 (and optionally log_BF10).

Which prior the Bayes factor divides by

The Savage-Dickey density ratio is only valid against the prior the model was actually fitted with, so bf_table() evaluates the prior density using object$priors_effective — resolved per outcome, since Gaussian nodes scale their default priors by the outcome SD (see set_priors). For a Gaussian outcome with default priors this makes intercepts and fe Bayes factors differ from what the unscaled object$priors would give, by roughly a factor of sd(y). ar, cl and temporal tests are unaffected: phi priors are never scaled.

Fit a Bayesian multilevel VAR network model

Description

The bvar function estimates the posterior distribution of the specified Bayesian (Multilevel) Vector Autoregression.

Usage

bvar(
  id_col,
  time_col,
  y_cols,
  x_cols = NULL,
  center_x = FALSE,
  fe_interactions = NULL,
  re_interactions = NULL,
  re_cols = NULL,
  re_temporal = FALSE,
  K = 1,
  na_action = c("listwise"),
  skip_lag = TRUE,
  data,
  family = c("bernoulli", "ordinal", "gaussian"),
  priors = set_priors(),
  iter = 4000,
  warmup = 1000,
  chains = 4,
  cores = 1,
  seed = NULL,
  adapt_delta = NULL,
  max_treedepth = NULL,
  save_data = FALSE,
  ...
)

Arguments

id_col

Character. Name of the subject/group identifier column.

time_col

Character. Name of the time column. Must be integer-valued (one time unit = one lag step); non-integer values error.

y_cols

Character vector. Names of the outcome columns.

x_cols

Character vector or NULL. Names of the covariate columns.

center_x

Logical. Grand-mean centre covariates before fitting? Default FALSE.

fe_interactions

List or NULL. Fixed-effect interaction terms to add to the design matrix. Each element is a character vector of column names to interact, or c("lag", "x") to interact all lag columns with a covariate.

re_interactions

List or NULL. Random-effect interaction terms.

re_cols

Character vector. Columns from X and/or "Intercept" to include as random slopes.

re_temporal

Logical. Include random slopes on lag predictors? Default FALSE.

K

Integer. AR order. Default 1.

na_action

Character. Missing-data strategy; currently only "listwise".

skip_lag

Logical. If TRUE (default), rows with irregular time gaps have their lag predictors zero-filled rather than being dropped. For this, time_col should be scaled so that each step has a length of one (e.g., integer days, weeks, or months). Non-integer values error.

data

Data frame in long format.

family

Character scalar or vector. Observation model per node. A scalar is recycled to all y_cols. A vector of length length(y_cols) (named or positional) specifies per-node families. Valid values: "bernoulli", "ordinal", "gaussian".

priors

A bvarnet_priors object from set_priors(). Defaults to set_priors() (package defaults).

iter

Integer. Number of post-warmup iterations per chain. Default 4000.

warmup

Integer. Number of warmup iterations per chain. Default 1000.

chains

Integer. Number of MCMC chains. Default 4.

cores

Integer. Number of chains to run in parallel. Default 1.

seed

Integer or NULL. RNG seed.

adapt_delta

Numeric in (0, 1). Target average proposal acceptance probability during warmup adaptation. Higher values (e.g., 0.95–0.99) reduce divergences at the cost of slower sampling. Default NULL (CmdStan default of 0.8).

max_treedepth

Integer. Maximum depth of the NUTS binary tree. Increasing this allows the sampler to take more leapfrog steps per iteration, which can help with difficult posteriors (e.g., funnels in hierarchical logistic models) but increases computation. Default NULL (CmdStan default of 10).

save_data

Logical. If TRUE, store the preprocessed (sorted, listwise-deleted) estimation data in the data_used slot of the returned object for reproducibility and downstream analyses. Default FALSE.

...

Additional named arguments forwarded to the CmdStanR $sample() method, e.g. init, refresh, thin, step_size, or show_messages. See ?cmdstanr::"model-method-sample" for the full list. Arguments that bvar() sets itself (data, seed, iter_warmup, iter_sampling, chains, parallel_chains, adapt_delta, max_treedepth, and CmdStanR's deprecated aliases for them) are rejected with an error; use the corresponding bvar() argument instead.

Value

A bvarnet object (a named list) with slots: draws, convergence, diagnostics, timing, metadata, return_codes, family, standata, priors, priors_effective. If save_data = TRUE, also includes data_used (the cleaned estimation data frame).

Requested versus effective priors

priors holds the priors as you specified them. priors_effective holds the priors Stan actually sampled under: one bvarnet_priors object per outcome. The two differ for Gaussian outcomes left at their default priors, where intercept, beta and sigma scales are multiplied by the outcome SD so that unit-scale defaults stay weakly informative on the raw data scale. User-supplied priors are never rescaled.

Bayes factors (bf_table) divide by priors_effective, as the Savage-Dickey density ratio requires. Note that the joint path (family a single value) scales every outcome by the mean SD across outcomes, whereas the nodewise path (mixed family) scales each outcome by its own SD, so the same data can imply slightly different effective priors depending on which path runs.

See Also

bvarnet_setup_models, which must be run once before the first bvar() call to set up the required Stan models (either by downloading precompiled binaries or compiling them locally).

Examples

## Not run: 
# Run bvar on studentlife data
data(studentlife, package = "bvarnet")
fit <- bvar(
  id_col = "id",
  time_col = "day",
  y_cols = c("anxious", "calm", "conventional", "critical", "dependable"),
  re_temporal = TRUE,
  K = 1,
  data = studentlife,
  family = "ordinal",
  priors = set_priors(),
  seed = 1337)

summary(fit)

## End(Not run)

Remove bvarnet's cached Stan model binaries

Description

Remove bvarnet's cached Stan model binaries

Usage

bvarnet_clear_model_cache(all_versions = FALSE, quiet = FALSE)

Arguments

all_versions

Logical. Remove the entire cache (all versions), not just the entry for the currently installed version.

quiet

Logical. Suppress the confirmation message.

Value

Invisibly, the path removed.

See Also

bvarnet_setup_models() to set models up again afterward, bvarnet_model_cache_dir() to inspect the cache path.

Path to bvarnet's Stan model cache directory

Description

Path to bvarnet's Stan model cache directory

Usage

bvarnet_model_cache_dir()

Value

Character scalar: the cache directory for the currently installed package version (may not exist yet).

See Also

bvarnet_setup_models() to populate this directory, bvarnet_clear_model_cache() to remove it.

Set up bvarnet's precompiled Stan models

Description

bvar() requires compiled Stan model executables. When installing from a CRAN/r-universe binary (no CmdStan at install time) they are not built automatically, so this function sets them up: either by downloading precompiled, toolchain-free binaries for your platform, or by compiling them locally with your own CmdStan installation. Either way the result is cached in a per-user cache directory (see bvarnet_model_cache_dir()) and persists across sessions.

Usage

bvarnet_setup_models(
  method = c("download", "compile"),
  force = FALSE,
  ask = interactive()
)

Arguments

method

"download" or "compile". If omitted, and the session is interactive, you are prompted to choose.

force

Logical. If TRUE, clear the current cache entry first and set up from scratch.

ask

Logical. Whether to prompt for confirmation before downloading or compiling. Defaults to interactive(). When FALSE, a download is only performed if pre-authorised via options(bvarnet.allow_download = TRUE) or the BVARNET_ALLOW_DOWNLOAD environment variable.

Details

Downloaded binaries are only ever fetched over HTTPS from this package's official GitHub repository, and are verified against a published SHA-256 checksum before being made executable; a mismatch aborts and deletes the download. The download is also pinned to a hash of your installed package's Stan sources, so a binary can never silently be paired with the wrong model version.

bvar() itself never downloads or prompts – all network activity happens here, in this explicitly user-invoked function, so scripts and non-interactive sessions never trigger a download by surprise.

Value

Invisibly, TRUE on success, FALSE if declined or unavailable.

See Also

bvarnet_model_cache_dir() to inspect where models are cached, bvarnet_clear_model_cache() to remove a cached entry, and bvar() for the function that uses the resulting models.

Examples

## Not run: 
# Interactive: prompts you to choose download vs. compile
bvarnet_setup_models()

# Non-interactive / scripted, pre-authorised for download:
options(bvarnet.allow_download = TRUE)
bvarnet_setup_models(method = "download", ask = FALSE)

# Force a clean re-setup (e.g. after a corrupted cache)
bvarnet_setup_models(method = "download", force = TRUE)

## End(Not run)

Compare fitted model parameters to simulation truth

Description

Extracts posterior summaries from a fitted bvarnet object and compares them to the true parameter values used for data generation.

Usage

compare_to_truth(
  fit,
  truth,
  ci_width = 0.95,
  bayes_factor = FALSE,
  null_value = 0
)

Arguments

fit

A fitted bvarnet object (output from bvar()).

truth

The truth component from sim_var() output.

ci_width

Numeric scalar strictly between 0 and 1. Mass of the equal-tailed credible interval used for ci_lower, ci_upper, and the covered indicator (default 0.95).

bayes_factor

Logical; if TRUE, compute Savage-Dickey BFs for beta and phi parameters and append BF01, BF10, and bf_correct columns. bf_correct is TRUE when BF01 > 1 for true null parameters (true value == null_value) and BF10 > 1 for true non-null parameters. Default FALSE.

null_value

Numeric scalar; the null hypothesis value for Bayes factor computation (default 0). Only used when bayes_factor = TRUE.

Value

A data frame with columns: parameter, node, true_value, post_mean, post_sd, ci_lower, ci_upper, covered (logical), and optionally BF01, BF10, bf_correct.

Extract raw posterior draws for a single parameter block

Description

Returns an (iterations * chains) by params matrix with Stan-indexed column names (e.g. "beta[1,1]", "phi[2,3]").

Usage

extract_draws(object, parameter = c("beta", "phi", "sd_u", "sigma", "kappa"))

Arguments

object

A bvarnet object returned by bvar.

parameter

Character. One of "beta", "phi", "sd_u", "sigma", or "kappa".

Value

A numeric matrix with one row per posterior draw and one column per Stan parameter element.

Extract a network matrix of temporal coefficients

Description

Returns a named p x p matrix of posterior summary statistics for the VAR lag coefficients at a chosen lag, suitable for network visualisation (e.g., with igraph or qgraph).

Usage

extract_network_matrix(
  object,
  lag = 1L,
  stat = c("mean", "median", "ci_lower", "ci_upper"),
  ci_level = 0.95
)

Arguments

object

A bvarnet object returned by bvar.

lag

Integer. Which lag block. Default 1.

stat

Character. Summary statistic to fill the matrix with: "mean" (default), "median", "ci_lower", or "ci_upper".

ci_level

Numeric scalar strictly between 0 and 1; the mass of the equal-tailed credible interval. Default 0.95. Only used when stat is "ci_lower" or "ci_upper".

Value

A named p x p numeric matrix. Element [i, j] gives the effect of variable i (lagged) on variable j (outcome). Row and column names are the outcome variable names.

Extract labelled parameter summaries from a fitted bvarnet model

Description

Returns a single flat data frame with posterior summaries (mean, median, and an equal-tailed credible interval) and convergence diagnostics (Rhat, ESS) for all model parameters.

Usage

extract_param(
  object,
  bayes_factor = FALSE,
  null_value = 0,
  type = NULL,
  ci_level = 0.95
)

Arguments

object

A bvarnet object returned by bvar().

bayes_factor

Logical; if TRUE, append BF01 and BF10 columns computed via the Savage-Dickey density ratio for beta and phi parameters. Default FALSE.

null_value

Numeric scalar; the null hypothesis value for Bayes factor computation (default 0). Only used when bayes_factor = TRUE.

type

Character vector or NULL (default). If supplied, only rows matching the given type(s) are returned. Valid values are: "Intercept", "Fixed Effect", "Autoregressive", "Cross-lagged", "Random Effect SD", "Residual SD", "Threshold".

ci_level

Numeric scalar strictly between 0 and 1; the mass of the equal-tailed credible interval reported in ci_lower and ci_upper. Default 0.95.

Value

A data frame with columns: type, predictor, outcome, mean, median, ci_lower, ci_upper, rhat, ess_bulk, ess_tail, and optionally BF01, BF10.

Extract random-effect summaries

Description

Returns random-effect standard deviations (group-level variance), subject-level posterior means, or the full posterior draws of the subject-level random effects u.

Usage

extract_random_effects(
  object,
  what = c("sd", "mean_u", "draws_u"),
  ci_level = 0.95
)

Arguments

object

A bvarnet object returned by bvar.

what

Character. What to extract:

"sd"

Data frame of random-effect SD summaries (from extract_param).

"mean_u"

3D array [node, subject, re] of posterior means of subject-level effects.

"draws_u"

4D array [draw, node, subject, re] of full posterior draws.

ci_level

Numeric scalar strictly between 0 and 1; the mass of the equal-tailed credible interval reported in ci_lower and ci_upper. Default 0.95. Only used when what = "sd".

Value

Depends on what; see above.

Array layout

The "mean_u" and "draws_u" arrays are indexed [node, subject, re] (with a leading draw dimension for "draws_u"), matching the Stan declaration array[p] matrix[J, n_re] u. node is named after the outcome columns, subject after the subject index 1..J, and re after the random-effect design columns. Elements are placed by parsing the u[node, subject, re] indices from the draws, so the layout is the same whether the model was fitted through the joint or the nodewise path.

Extract temporal (VAR lag) effects

Description

Returns a data frame of autoregressive and/or cross-lagged parameter summaries with convergence diagnostics, filtered by lag and effect type.

Usage

extract_temporal(
  object,
  lag = NULL,
  effect = c("all", "ar", "cl"),
  bayes_factor = FALSE,
  null_value = 0,
  ci_level = 0.95
)

Arguments

object

A bvarnet object returned by bvar.

lag

Integer or NULL. If specified, only effects from this lag are returned. Default NULL (all lags).

effect

Character. One of "all" (default), "ar" (autoregressive only), or "cl" (cross-lagged only).

bayes_factor

Logical; if TRUE, append BF columns. Default FALSE.

null_value

Numeric; null hypothesis for BF. Default 0.

ci_level

Numeric scalar strictly between 0 and 1; the mass of the equal-tailed credible interval reported in ci_lower and ci_upper. Default 0.95.

Value

A data frame with columns type, predictor, outcome, mean, median, ci_lower, ci_upper, rhat, ess_bulk, ess_tail, and optionally BF01, BF10.

Format a bvarnet_prior for printing

Description

Format a bvarnet_prior for printing

Usage

## S3 method for class 'bvarnet_prior'
format(x, half = FALSE, ...)

Arguments

x

A bvarnet_prior object.

half

Logical; if TRUE prepends "Half-" to indicate a half-prior (used for positive-constrained parameters like sd_u and sigma).

...

Ignored.

Value

A character string.

Get the default prior specification for a given model family

Description

Returns a bvarnet_priors object showing the default priors that apply to a particular model configuration. Parameters irrelevant to the chosen family or model structure are omitted, so the returned object reflects what the sampler will actually use.

Usage

get_default_priors(family = NULL, has_re = TRUE)

Arguments

family

Character (optional). One of "bernoulli", "ordinal", "gaussian". When NULL (the default), all parameter priors are shown.

has_re

Logical. Does the model include random effects? Default TRUE. When FALSE, the sd_u prior is omitted.

Value

A bvarnet_priors object.

Print a bvarnet model object

Description

Displays a brief summary of the fitted model: family, dimensions, Rhat, divergences, chain return codes, priors, and total sampling time.

Usage

## S3 method for class 'bvarnet'
print(x, ...)

Arguments

x

A bvarnet object.

...

Ignored.

Value

x invisibly.

Print a bvarnet_prior

Description

Print a bvarnet_prior

Usage

## S3 method for class 'bvarnet_prior'
print(x, ...)

Arguments

x

A bvarnet_prior object.

...

Passed to format.bvarnet_prior().

Value

x invisibly.

Print a bvarnet_priors specification

Description

Shows only the priors explicitly set by the user. When no priors have been overridden (all defaults), a compact note is printed instead.

Usage

## S3 method for class 'bvarnet_priors'
print(x, ...)

Arguments

x

A bvarnet_priors object.

...

Ignored.

Value

x invisibly.

Print a bvarnet summary

Description

Pretty-prints the output of summary.bvarnet, grouping parameters by type and displaying convergence information. Each group is truncated to max_rows rows; use extract_param() or dedicated extractors to see full output.

Usage

## S3 method for class 'summary.bvarnet'
print(x, digits = 3, max_rows = 10, ...)

Arguments

x

A summary.bvarnet object.

digits

Number of decimal digits for numeric columns. Default 3.

max_rows

Maximum number of rows to print per parameter group. Default 10.

...

Ignored.

Value

x invisibly.

Construct a single prior distribution

Description

Builds a bvarnet_prior object specifying the prior family and its parameters. Supported families in Phase 1 are "normal", "student_t", and "cauchy".

Usage

prior(family, loc = 0, scale = 1, df = 7)

Arguments

family

Character. One of "normal", "student_t", "cauchy".

loc

Location parameter (default 0).

scale

Scale parameter (default 1). Must be > 0.

df

Degrees of freedom for "student_t" (default 7). Must be0 when family = "student_t".

Value

A bvarnet_prior S3 object.

Build a prior specification object for bvar()

Description

Returns a bvarnet_priors object containing a bvarnet_prior for every model parameter type. Any argument left as NULL uses the package default. Available prior distributions are:

Usage

set_priors(
  intercept = NULL,
  beta = NULL,
  phi = NULL,
  sd_u = NULL,
  kappa = NULL,
  sigma = NULL
)

Arguments

intercept

Prior for the intercept. Only applies to gaussian and bernoulli models; for ordinal models the intercept is absorbed into the kappa (threshold parameter).

beta

Prior for fixed-effect regression coefficients (slopes).

phi

Prior for lag coefficients.

sd_u

Prior for random-effect standard deviations (half-prior).

kappa

Prior for ordinal cut-points (ordinal models only).

sigma

Prior for residual standard deviation (gaussian models only; half-prior).

Value

A bvarnet_priors S3 object.

Automatic scaling of Gaussian defaults

Gaussian outcomes are modelled on their raw scale, so bvar() widens the default intercept, beta and sigma scales by the outcome SD before passing them to Stan. A default beta ~ Normal(0, 1) on data with sd(y) = 17.8 therefore becomes Normal(0, 17.8). This keeps the unit-scale defaults weakly informative whatever the units of y, but it means the default you see here is not the prior that was used.

A prior you pass explicitly is taken at face value and never rescaled. The priors actually used are recorded on the fitted object as priors_effective (see bvar), are reported by print(), and are what bf_table divides by.

Simulate data from a multilevel VAR model

Description

Generates data from the generative model implied by each Stan model family. Useful for testing parameter recovery and model validation.

Usage

sim_var(
  N,
  T_obs,
  p,
  K = 1L,
  family = c("bernoulli", "ordinal", "gaussian"),
  alpha = NULL,
  gamma = NULL,
  Phi = NULL,
  sigma = NULL,
  kappa = NULL,
  q = 0L,
  x_gen = NULL,
  sd_alpha = 0.5,
  sd_phi = 0.2,
  sd_gamma = NULL,
  re_temporal = FALSE,
  C = 5L,
  burnin = 500L,
  seed = NULL
)

Arguments

N

Integer. Number of subjects (groups).

T_obs

Integer. Number of time points per subject.

p

Integer. Number of outcome nodes.

K

Integer. AR order (default 1).

family

Character. One of "bernoulli", "ordinal", "gaussian".

alpha

Numeric vector of length p. Population intercepts (on logit scale for bernoulli, identity for gaussian). For ordinal, this is absorbed into kappa and should be left NULL. Generated if NULL.

gamma

Matrix q x p. Population covariate effects. Generated if NULL and q > 0.

Phi

Matrix (p*K) x p. Population lag coefficients. Generated if NULL.

sigma

Numeric vector of length p. Residual SD per node (gaussian only). Generated if NULL.

kappa

List of p ordered vectors, each of length C-1. Cutpoints per node (ordinal only). Generated if NULL.

q

Integer. Number of covariates (default 0).

x_gen

Function f(N, T_obs) returning an N x T_obs x q array of covariates. If NULL, default generation is used.

sd_alpha

Numeric. SD of random intercepts (scalar or p-vector). Default 0.5. Set to 0 to simulate a fixed-effects-only model with no between-person variation in intercepts.

sd_phi

Numeric. SD of random lag coefficients (scalar or matrix). Default 0.2.

sd_gamma

Numeric or NULL. SD of random covariate slopes. NULL means no random slopes on covariates.

re_temporal

Logical. Include random slopes on lag predictors? Default FALSE.

C

Integer. Number of ordinal categories (ordinal only, default 5).

burnin

Integer. Number of time points to discard as warmup before recording data (default 500). The VAR process is simulated for burnin + T_obs time points per subject, and the first burnin are discarded. This allows the process to reach its stationary distribution before data collection begins.

seed

Integer or NULL. RNG seed.

Details

To simulate a VAR without any random effects (i.e. all subjects share identical parameters), set sd_alpha = 0, re_temporal = FALSE (the default), and sd_gamma = NULL (the default).

Value

A list with two components:

data

A long-format data frame with columns id, t, y_1, ..., y_p, and optionally x_1, ..., x_q.

truth

A list of true generating parameters.

StudentLife Data

Description

Assessing mental health, academic performance and behavioral trends of college students using smartphones. Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T. (2014). StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 3–14. https://doi.org/10.1145/2632048.2632054

Usage

studentlife

Format

studentlife

A data frame with 1912 rows and 72 columns:

Source

https://openesmdata.org/datasets/0004_wang/

https://doi.org/10.1145/2632048.2632054

Summary method for bvarnet objects

Description

Returns a labelled posterior summary table grouped by parameter type, with convergence diagnostics and optional Bayes factors. Wraps extract_param.

Usage

## S3 method for class 'bvarnet'
summary(object, bayes_factor = FALSE, null_value = 0, ..., ci_level = 0.95)

Arguments

object

A bvarnet object returned by bvar.

bayes_factor

Logical; if TRUE, append Savage-Dickey BF columns. Default FALSE.

null_value

Numeric scalar; null hypothesis value for BF computation. Default 0.

...

Ignored.

ci_level

Numeric scalar strictly between 0 and 1; the mass of the equal-tailed credible interval reported in ci_lower and ci_upper. Default 0.95. Must be supplied by name.

Value

An object of class "summary.bvarnet" (a list) with elements:

table

Data frame from extract_param().

family

Model family.

p

Number of outcome variables.

K

AR order.

n

Number of observations.

rhat_max

Maximum Rhat across all parameters.

n_divergences

Total divergent transitions.

ci_level

Credible-interval mass used for the table.