nlpsem
Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework
Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Jin Liu (2023) doi:10.48550/arXiv.2302.03237.
- Version0.3
- R versionunknown
- LicenseGPL (≥ 3.0)
- Needs compilation?No
- Last release09/12/2023
Documentation
Team
Jin Liu
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends1 package
- Imports7 packages
- Suggests2 packages