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Package overview

Structural equation models (SEMs) rarely reject the null hypothesis that there is no model misspecification. One explanation for this problem is that covariance structures are influenced by major factors which we can hypothesize about and minor factors which we cannot predict a-priori, e.g. MacCallum and Tucker (1991).


The goal of minorbsem is to facilitate fitting Bayesian SEMs that estimate the influence of minor factors on the covariance matrix, following the approach in Uanhoro (2023). Briefly, the method estimates all residual covariances with priors that shrink them towards zero, and the model returns the magnitude of the influence of minor factors.

Permitted models and supported data types

The package only fits a limited number of model configurations:

  • CFA, allowing cross-loadings (which may be automatically estimated), correlated errors terms, and fully oblique or orthogonal factors (useful for fitting bifactor models)
  • SEMs allowing latent regressions (only), cross-loadings, and correlated error terms.

However, the package does not currently support fitting:

  • MIMIC,
  • multi-group models,
  • multilevel models, or
  • models with specially constrained parameters (e.g., setting two parameters equal).

All data are assumed multivariate normal, i.e. no binary, ordinal models.


minorbsem is hosted on GitHub, so we need the remotes package to install it. We also need to install the cmdstanr package and CmdStan in order to use Stan.


install.packages("remotes")  # install remotes

# next install cmdstanr and CmdStan:
  repos = c("", getOption("repos"))
cmdstanr::check_cmdstan_toolchain(fix = TRUE)

# Then finally minorbsem:

A reasonably complete demonstration

# Basic Holzinger-Swineford model
syntax_1 <- "
F1 =~ x1 + x2 + x3
F2 =~ x4 + x5 + x6
F3 =~ x7 + x8 + x9"
# Expect a summary table output
fit_1 <- minorbsem(syntax_1, HS)

# Save output table to html file, see: ?pretty_print_summary for more options
pretty_print_summary(fit_1, save_html = "baseline_model.html")

# Histogram of parameters, see: ?parameter_hist for arguments

# Traceplot of parameters, see: ?parameter_trace for arguments

# Examine all standardized residual covariances
plot_residuals(fit_1, type = "range")

Additional examples

Different methods to capture the influence of minor factors

Default method above is method = "normal" assuming standardized residual covariances are on average 0 and vary from 0 in continuous fashion.

# Fit same model as above but use global-local prior to estimate
# minor factor influences
fit_gdp <- minorbsem(syntax_1, HS, method = "GDP")

# Ignoring minor factor influences
fit_none <- minorbsem(syntax_1, HS, method = "none")

# Error!!!: Plotting residuals will give an error message
# since minor factor influences are assumed null

Relax simple structure

fit_complex <- minorbsem(syntax_1, HS, simple_struc = FALSE)

There are other methods, see details section in ?minorbsem.

Contributions are encouraged

All users of R (or SEM) are invited to submit functions or ideas for functions.

Feel free to:

  • open an issue to report a bug or to discuss recommendations;
  • submit pull requests to recommend modifications or suggest improvements.

You can also email the package maintainer, James Uanhoro (James dot Uanhoro at unt dot edu). Thank you for helping improve minorbsem :).


MacCallum, Robert C., and Ledyard R. Tucker. 1991. “Representing Sources of Error in the Common-Factor Model: Implications for Theory and Practice.” Psychological Bulletin 109 (3): 502–11.
Uanhoro, James Ohisei. 2023. “Modeling Misspecification as a Parameter in Bayesian Structural Equation Models.” Educational and Psychological Measurement 0 (0): 00131644231165306.