Integration of 'Blimp' Software into R

Provides an interface to 'Blimp' software for Bayesian latent variable modeling, missing data analysis, and multiple imputation. The package generates 'Blimp' syntax, executes 'Blimp' models, and imports results back into 'R' as structured objects with methods for visualization and analysis. Requires 'Blimp' software (freely available at < https://www.appliedmissingdata.com/blimp>) to be installed separately.

rblimp

R interface to Blimp for Bayesian latent variable modeling, missing data analysis, and multiple imputation.

Overview

rblimp provides a seamless interface to integrate Blimp software into R workflows. Blimp offers general-purpose Bayesian estimation for a wide range of single-level and multilevel structural equation models with two or three levels, with or without missing data.

Key Features

Flexible Model Specification: Use R formula syntax or raw Blimp syntax
Multiple Imputation: Built-in support for Fully Conditional Specification (FCS)
Convergence Diagnostics: PSR values, trace plots, and posterior distributions
Visualization Tools: Residual plots, simple slopes, Johnson-Neyman regions
Integration: Convert results to mitml format for pooling analyses
Data Simulation: Generate simulated datasets for power analysis and methods research
Special Variable Types: Support for binary, ordinal, nominal, and count outcomes
Interactive Effects: Estimation routines for interactions and polynomial effects

Installation

Step 1: Install rblimp

Install from CRAN:

install.packages("rblimp")

Or install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("blimp-stats/rblimp")

Step 2: Install Blimp Software

rblimp requires the Blimp engine. The simplest path is to let rblimp install it for you:

library(rblimp)
install_blimp()

This downloads the latest Blimp engine into a user-writable directory:

macOS: ~/.blimp/
Windows: %LOCALAPPDATA%/Blimp/
Linux: ~/.blimp/

Override the location with the R_BLIMP_HOME environment variable. Remove with uninstall_blimp().

If you'd rather use the system installer, visit https://www.appliedmissingdata.com/blimp and follow the install instructions there.

Step 3 (optional): Configure Blimp manually

If you've installed Blimp to a non-standard location:

# Automatic detection (also offered the first time you run a model)
detect_blimp()

# Or set manually
set_blimp("/path/to/blimp")

# Verify
has_blimp()

Privacy

Downloads are recorded for usage statistics. See privacy policy: https://www.blimpstats.com/privacy

Getting Started

View the getting started guide:

?rblimp_getting_started

Explore function documentation:

?rblimp          # Fit Bayesian models
?rblimp_fcs      # Multiple imputation
?rblimp_sim      # Data simulation
help(package = "rblimp")

Quick Example

library(rblimp)

# Generate data with latent factor
mydata <- rblimp_sim(
  c(
    'f ~ normal(0, 1)',
    'x1:x5 ~ normal(f, 1)',
    'y ~ normal(10 + 0.3*f, 1 - .3^2)'
  ),
  n = 500,
  seed = 19723,
  variables = c('y', 'x1:x5')
)

# Fit SEM model
model <- rblimp(
  list(
    structure = 'y ~ f',
    measurement = 'f -> x1:x5'
  ),
  mydata,
  seed = 3927,
  latent = ~ f
)

# View results
summary(model)

# Check convergence
trace_plot(model)