References
This package is considered a duplicate. The official version of this package is found at:https://munchfab.r-universe.dev/mlts
Package: mlts 2.0.1
Kenneth Koslowski
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>.
Authors:
mlts_2.0.1.tar.gz
mlts_2.0.1.zip(r-4.7-x86_64)mlts_2.0.1.zip(r-4.7-arm64)mlts_2.0.1.zip(r-4.6-x86_64)mlts_2.0.1.zip(r-4.6-arm64)
mlts_2.0.1.tar.gz(r-4.7-arm64)mlts_2.0.1.tar.gz(r-4.7-x86_64)mlts_2.0.1.tar.gz(r-4.6-arm64)mlts_2.0.1.tar.gz(r-4.6-x86_64)
mlts_2.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mlts/json (API)
| # Install 'mlts' in R: |
| install.packages('mlts', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/munchfab/mlts/issues
Last updated from:c6459f9b24. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 480 | ||
| linux-devel-x86_64 | OK | 588 | ||
| source / vignettes | OK | 894 | ||
| linux-release-arm64 | OK | 512 | ||
| linux-release-x86_64 | OK | 575 | ||
| windows-devel-arm64 | OK | 611 | ||
| windows-devel-x86_64 | OK | 492 | ||
| windows-release-arm64 | OK | 569 | ||
| windows-release-x86_64 | OK | 621 | ||
| wasm-release | OK | 288 |
Exports:create_missingsmlts_fitmlts_modelmlts_model_formulamlts_model_pathsmlts_pathsmlts_plotmlts_posterior_samplemlts_pp_checkmlts_simmlts_standardized
Dependencies:abindaskpassbackportsbase64encBHbslibcachemcallrcheckmateclicowplotcpp11curldescdiagramdigestdistributionaldplyrevaluatefarverfastmapfontawesomefsgenericsggplot2gluegridExtragtablehighrhtmltoolsinlineisobandjquerylibjsonliteknitrlabelinglifecycleloomagrittrmatrixStatsmemoisemimemvtnormnumDerivotelpdftoolspillarpkgbuildpkgconfigposteriorprocessxpsqpdfQuickJSRR6rappdirsRColorBrewerRcppRcppEigenRcppParallelrlangrmarkdownrstanrstantoolsS7sassscalesshapeStanHeaderssystensorAtibbletidyselecttinytexutf8vctrsviridisLitewithrxfunyaml
A Simple Example: Multilevel Manifest AR(1) Model
Last update: 2025-12-08
Started: 2024-06-28
Vector Autoregressive Models
Last update: 2025-12-08
Started: 2024-06-28
Include Predictors for Random Effects on the Between Level
Autoregressive model without predictors | Include a predictor for random effects | References
Last update: 2024-06-28
Started: 2024-06-28
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Simple Time-Series Data | ar1_data |
| Create Missings for Approximation of Continuous Time Dynamic Models | create_missings |
| Fit Bayesian Multilevel Manifest or Latent Time-Series Models | mlts_fit |
| Build a multilevel latent time series model | mlts_model |
| Create TeX Model Formula from mlts model object | mlts_model_formula |
| Create Path Diagrams from mlts model object | mlts_model_paths |
| Plot Paths for Two-Level VAR Model | mlts_paths |
| Plot results of mlts | mlts_plot |
| Generate Posterior Predictive Samples for Multilevel Latent Time Series Models | mlts_posterior_sample |
| Posterior Predictive Checks for Multilevel Latent Time Series Models | mlts_pp_check |
| Simulate data from mlts model | mlts_sim |
| Get Standardized Estimates for an mlts Model | mlts_standardized |
| Create a summary of a fitted model with class 'mltsfit' | summary.mltsfit |
| Simple Time-Series Data | ts_data |