nlpsem

nlpsem provides tools for fitting linear and nonlinear longitudinal models within the structural equation modeling (SEM) framework. The package is built on top of OpenMx and is designed for settings where the trajectory shape is nonlinear and may be intrinsically nonlinear rather than a simple reparameterization of a linear growth model.

Portfolio positioning

nlpsem is a statistical software and measurement-methods asset. It supports nonlinear longitudinal modeling, latent growth and latent change models, mixture modeling, multivariate longitudinal processes, multiple-group comparisons, and psychometric/measurement-science workflows in R.

For AI evaluation roles, the package is relevant because repeated AI behavior across prompts, models, seeds, raters, and deployment conditions can be studied with the same measurement mindset used for longitudinal human-development and latent-process modeling: define the measurement occasions, model systematic variation, quantify reliability, and separate signal from instability.

For hiring reviewers

This repository demonstrates:

• R package development with documented functions, vignettes, tests, and CRAN-facing metadata
• nonlinear longitudinal modeling in the SEM framework
• latent growth, latent change, multivariate, mediation, multiple-group, and mixture modeling workflows
• psychometric and measurement-science foundations relevant to evaluator reliability and repeated-behavior analysis
• reproducible statistical examples backed by OpenMx

The methods implemented in this package are described in:

Liu, J. (2025). Examination of nonlinear longitudinal processes in the framework of structural equation modeling: The R package nlpsem. Behavior Research Methods. https://doi.org/10.3758/s13428-025-02596-4

Connection to AI evaluation

The same measurement-science principles used for longitudinal human-development modeling also inform evaluation of AI system behavior over prompts, models, seeds, raters, and deployment conditions. In both settings, the central questions are how behavior changes across repeated conditions, which sources of variation are meaningful, and how much confidence a reviewer should place in an observed pattern.

Main modeling scenarios

The package supports four broad longitudinal modeling settings:

1. Univariate longitudinal models with growth factors, with or without time-invariant covariates (TICs) and time-varying covariates (TVCs)
2. Multivariate longitudinal models for assessing correlated or potentially directional development across multiple processes
3. Multiple-group longitudinal models for comparing manifested groups
4. Longitudinal mixture models for identifying latent trajectory classes

Core functions

Model-fitting functions

• getLGCM(): latent growth curve models
• getLCSM(): latent change score models
• getTVCmodel(): models with time-varying covariates
• getMGM(): multivariate growth and change models
• getMediation(): longitudinal mediation models
• getMGroup(): multiple-group models
• getMIX(): longitudinal mixture models

Post-processing functions

• getSummary(): model fit summary across one or more fitted models
• getEstimateStats(): p-values and confidence intervals
• getFigure(): trajectory plots
• getLRT(): likelihood ratio tests for nested models
• getIndFS(): individual factor scores
• getPosterior(): posterior class probabilities for mixture models
• getLatentKappa(): agreement between latent class assignments

Supported functional forms

For latent growth curve models, nlpsem supports:

• linear ("LIN")
• quadratic ("QUAD")
• negative exponential ("EXP")
• Jenss-Bayley ("JB")
• bilinear spline ("BLS")

For latent change score models, nlpsem supports:

• nonparametric ("NonP")
• quadratic ("QUAD")
• negative exponential ("EXP")
• Jenss-Bayley ("JB")

Negative exponential, Jenss-Bayley, and bilinear spline models can be fit in their intrinsically nonlinear forms by setting intrinsic = TRUE.

Installation

If the package is available on CRAN:

install.packages("nlpsem")

To install the development version from GitHub:

# install.packages("remotes")
# remotes::install_github("Veronica0206/nlpsem")
install.packages("nlpsem", repos = c('https://veronica0206.r-universe.dev', 'https://cloud.r-project.org'))

Quick example

library(nlpsem)

mxOption(model = NULL, key = "Default optimizer", "CSOLNP", reset = FALSE)

data("RMS_dat")
RMS_dat0 <- RMS_dat

# Re-baseline the measurement occasions
baseT <- RMS_dat0$T1
for (i in 1:9) {
  RMS_dat0[[paste0("T", i)]] <- RMS_dat0[[paste0("T", i)]] - baseT
}

# Fit a bilinear spline LGCM with a fixed knot
Math_BLS <- getLGCM(
  dat = RMS_dat0,
  t_var = "T",
  y_var = "M",
  curveFun = "BLS",
  intrinsic = FALSE,
  records = 1:9,
  paramOut = TRUE
)

# Inspect estimates
printTable(Math_BLS)

What is new in version 0.4

Version 0.4 keeps the modeling scope of version 0.3 while substantially improving robustness, usability, and documentation.

Highlights include:

• stronger initial-value calculations for nonlinear forms, especially EXP, JB, and BLS
• smoothed intrinsic bilinear spline loadings for better optimizer behavior near the knot
• data-driven residual variance and residual correlation starts
• automatic parameter naming when paramOut = TRUE
• expanded validation and cleaner handling of multivariate, mediation, TVC, mixture, and multiple-group edge cases
• synchronized vignettes and saved example objects
• a new testthat test suite and cleaner CRAN-facing documentation

See NEWS.md for a more comprehensive summary of changes from version 0.3.

Documentation

The package includes an overview vignette and scenario-specific example vignettes:

• vignette("nlpsem_overview")
• vignette("getLGCM_examples")
• vignette("getLCSM_examples")
• vignette("getTVCmodel_examples")
• vignette("getMGM_examples")
• vignette("getMediation_examples")
• vignette("getMGroup_examples")
• vignette("getMIX_examples")

License

GPL (>= 3)