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mlts: Multilevel Latent Time Series Models with 'R' and 'Stan'

Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using 'knitr'. Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: cowplot, dplyr (≥ 1.1.3), ggplot2, methods, mvtnorm, pdftools, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rlang, rmarkdown, rstan (≥ 2.32.3), rstantools (≥ 2.4.0), stats
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
Suggests: knitr, testthat (≥ 3.0.0)
Published: 2024-06-27
DOI: 10.32614/CRAN.package.mlts
Author: Kenneth Koslowski ORCID iD [aut, cre, cph], Fabian Münch ORCID iD [aut], Tobias Koch ORCID iD [aut], Jana Holtmann ORCID iD [aut]
Maintainer: Kenneth Koslowski <kenneth.koslowski at uni-leipzig.de>
BugReports: https://github.com/munchfab/mlts/issues
License: GPL (≥ 3)
URL: https://github.com/munchfab/mlts
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: mlts citation info
Materials: README NEWS
CRAN checks: mlts results

Documentation:

Reference manual: mlts.pdf
Vignettes: Include Predictors for Random Effects on the Between Level
manifest_ar_example
Vector Autoregressive Models

Downloads:

Package source: mlts_1.0.0.tar.gz
Windows binaries: r-devel: mlts_1.0.0.zip, r-release: mlts_1.0.0.zip, r-oldrel: mlts_1.0.0.zip
macOS binaries: r-release (arm64): mlts_1.0.0.tgz, r-oldrel (arm64): not available, r-release (x86_64): mlts_1.0.0.tgz, r-oldrel (x86_64): not available

Linking:

Please use the canonical form https://CRAN.R-project.org/package=mlts to link to this page.

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.