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| Category | M |
|---|---|
| Domain name | multibd.com.au |
| IP | 110.232.141.252 |
| Country by IP | AU |
| Web server type | LiteSpeed |
| Hostname | c6s3-4m-syd.hosting-services.net.au |
| Emails | info@multibd.com.au |
| Phone numbers | +61-03-8560-5120 |
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GitHub - msuchard/MultiBD
MultiBD. MultiBD is an R package for direct likelihood-based inference of multivariate birth-death processes. Installation. Install CRAN release version: install.packages("MultiBD") Install the bleeding-edge version of MultiBD from github: devtools::install_github("msuchard/MultiBD") Visit website
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MultiBD package - RDocumentation
MultiBD is licensed under Apache License 2.0. Development status. Beta. Acknowledgements. This project is supported in part through the National Science Foundation grant DMS 1264153 and National Institutes of Health grant R01 AI107034. References. Ho LST, Xu J, Crawford FW, Minin VN, Suchard MA. Visit website
GitHub - cran/MultiBD: This is a read-only mirror of the CRAN R …
MultiBD — Multivariate Birth-Death Processes - GitHub - cran/MultiBD: This is a read-only mirror of the CRAN R package repository. MultiBD — Multivariate Birth-Death Processes :exclamation: This is a read-only mirror of the CRAN R package repository. Visit website
Package ‘MultiBD’
Package ‘MultiBD’ December 5, 2016 Type Package Title Multivariate Birth-Death Processes Version 0.2.0 Date 2016-07-19 Author Lam S.T. Ho [aut, cre], Visit website
MultiBD
MultiBD is licensed under Apache License 2.0. Development status. Beta. Acknowledgements. This project is supported in part through the National Science Foundation grant DMS 1264153 and National Institutes of Health grant R01 AI107034. References. Ho LST, Xu J, Crawford FW, Minin VN, Suchard MA. Visit website
CRAN - Package MultiBD
Computationally efficient functions to provide direct likelihood-based inference for partially-observed multivariate birth-death processes. Such processes range from a simple Yule model to the complex susceptible-infectious-removed model in disease dynamics. Efficient likelihood evaluation facilitates maximum likelihood estimation and Bayesian inference. Visit website
A comparison of explainable artificial intelligence methods in the ...
We demonstrate the capabilities of two model-agnostic local post-hoc model interpretability methods, namely breakDown (BD) and shapley (SHAP), to explain the predictions of a black-box ... Visit website
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Rcpp: Seamless R and C++ Integration - cran.microsoft.com
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- Multibd.com.au login page link (you can find on this page above);
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