Package: mlts 2.0.1.9000

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:Kenneth Koslowski [aut, cre, cph], Fabian Münch [aut], Tobias Koch [aut], Jana Holtmann [aut]

mlts_2.0.1.9000.tar.gz
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mlts_2.0.1.9000.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://munchfab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/munchfab/mlts/issues

Pkgdown/docs site:https://munchfab.github.io

Uses libs:
  • onetbb– Parallelism library for C++
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

onetbbcpp

6.67 score 12 stars 13 scripts 330 downloads 14 exports 83 dependencies

Last updated from:84a7854135. Checks:13 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE517
linux-devel-x86_64NOTE575
source / vignettesOK1071
linux-release-arm64NOTE523
linux-release-x86_64NOTE699
macos-release-arm64NOTE725
macos-release-x86_64NOTE1460
macos-oldrel-arm64NOTE691
macos-oldrel-x86_64NOTE1003
windows-devel-arm64NOTE687
windows-devel-x86_64NOTE763
windows-release-arm64NOTE719
windows-release-x86_64NOTE680
windows-oldrel-x86_64NOTE635
wasm-releaseOK328

Exports:create_missingsdescribe_tsmlts_cor_tsmlts_fitmlts_modelmlts_model_formulamlts_model_pathsmlts_pathsmlts_plotmlts_posterior_samplemlts_pp_checkmlts_relmlts_simmlts_standardized

Dependencies:abindaskpassbackportsbase64encBHbslibcachemcallrcheckmateclassclicowplotcpp11curldescdiagramdigestdistributionaldplyre1071evaluatefarverfastmapfontawesomefsgenericsggplot2gluegridExtragtablehighrhtmltoolsinlineisobandjquerylibjsonliteknitrlabelinglifecycleloomagrittrmatrixStatsmemoisemimemvtnormnumDerivotelpdftoolspillarpkgbuildpkgconfigposteriorprocessxproxypsqpdfQuickJSRR6rappdirsRColorBrewerRcppRcppEigenRcppParallelrlangrmarkdownrstanrstantoolsS7sassscalesshapeStanHeaderssystensorAtibbletidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

A Simple Example: Multilevel Manifest AR(1) Model
References
Rendered frommanifest_ar.Rmdusingknitr::rmarkdown

Last update: 2025-12-08
Started: 2024-05-16

Include Predictors for Random Effects on the Between Level
Autoregressive model without predictors | Include a predictor for random effects | References
Rendered frombetw_predictors.Rmdusingknitr::rmarkdown

Last update: 2024-06-21
Started: 2024-05-16

Vector Autoregressive Models
Rendered frommanifest_var.Rmdusingknitr::rmarkdown

Last update: 2024-05-16
Started: 2024-05-16