The Ultimate Guide to the msm Package in R
Explore valuable documentation and insights to make the most of the msm package in R. Get ready to unlock the full potential of the msm package!
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What is the msm package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
The msm package in R provides methods for fitting continuous-time multi-state Markov and hidden Markov models to longitudinal data. It can be used to estimate transition intensities (hazard rates) between states, stationary and transient probability distributions, and expected sojourn times within states.
- Title: Multi-State Markov and Hidden Markov Models in Continuous Time
- Description: Functions for fitting continuous-time Markov and hidden Markov multi-state models to longitudinal data. Designed for processes observed at arbitrary times in continuous time (panel data) but some other observation schemes are supported. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.
- Author: Christopher Jackson [aut, cre]
- Maintainer: Christopher Jackson
How to install the msm package?
In this section, we’ll walk you through the process of installing and loading the msm package. By following these steps, you can seamlessly add new functions, datasets, and other resources to your R environment for a more robust workflow.
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What package information should you know?
In this section, we’ll go over the technical aspects of the msm package.
Key features
- Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify built-in datasets. To identify the datasets for the msm package, visit our database of R datasets.
- Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes for the msm package, you can visit our visit our database of R vignettes.
- Citation: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find citation information for the msm package, visit our database of R package citations.
Technical details
- License type: GPL (>= 2). For license details, visit the Open Source Initiative website.
- Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The msm package has compilation requirements.
- Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The msm package has no required dependencies.
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The msm package has the following suggested dependencies: mstate, minqa, doParallel, foreach, numDeriv, testthat, flexsurv, covr.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The msm package does not use any external sources.
- Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The msm package has the following imported packages: survival, mvtnorm, expm.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The msm package has no enhancements.
How to get help with the msm package?
In this section, we’ll discuss a variety of available resources for getting help with the msm package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("msm") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
-
Package website: The msm package has a dedicated website. You can visit: https://github.com/chjackson/msm, https://chjackson.github.io/msm/.
- Developer support: You can email Christopher Jackson . For contact information, visit our R community directory.
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Other package guides
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