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multivar

Penalized estimation of multiple-subject vector autoregressive (VAR) models. Estimates shared (common) and subject-specific (unique) network dynamics across multiple individuals using structured penalties with FISTA optimization.

Installation

# From GitHub
devtools::install_github("zackfisher/multivar")

Quick start

library(multivar)

# Simulate data: 2 subjects, 5 variables, 50 timepoints
sim <- multivar_sim(k = 2, d = 5, n = 50,
                    prop_fill_com = 0.1, prop_fill_ind = 0.1,
                    lb = 0.1, ub = 0.5, sigma = diag(5))

# Fit model
model <- constructModel(data = sim$data)
fit <- cv.multivar(model)

# View estimated dynamics
print_dynamics(fit)

Features

• Adaptive LASSO with debiased initial estimation
• Cross-validation and eBIC model selection
• Subgroup detection
• Parallel cross-validation

References

Fisher, Z. F., Kim, Y., Fredrickson, B. L., & Pipiras, V. (2022). Penalized estimation and forecasting of multiple subject intensive longitudinal data. Psychometrika, 87(4), 1377–1404.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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