The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling
Open accessAbstract
Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects.However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions.We present a new package, glmmTMB, and compare it to other R packages that fit zero-inflated mixed models.The glmmTMB package fits many types of GLMMs and extensions, including models with continuously distributed responses, but here we focus on count responses.glmmTMB is faster than glmmADMB, MCMCglmm, and brms, and more flexible than INLA and mgcv for zero-inflated modeling.One unique feature of glmmTMB (among packages that fit zero-inflated mixed models) is its ability to estimate the Conway-Maxwell-Poisson distribution parameterized by the mean.Overall, its most appealing features for new users may be the combination of speed, flexibility, and its interface's similarity to lme4.
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Citations
Cites
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
brms : An R Package for Bayesian Multilevel Models Using Stan
Bürkner, P.-C. (2017) · Journal of Statistical Software · doi:10.18637/jss.v080.i01 · Cited by 9,476 · cited 6×
“…GLMMs with predictors of zero-inflation, but they are relatively slow (as we will show) because they rely on Markov chain Monte Carlo (MCMC) sampling (Bürkner, 2017; Hadfield, 2010). gamlss is a flexible package that fits generalized additive models with predictors on all parameters of a…”
BackgroundMethodInfluentialZero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
Lambert, D. (1992) · Technometrics · doi:10.2307/1269547 · Cited by 3,988
Cited by
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Romano, S., Savva, G. M., Bedarf, J. R., Charles, I. G., Hildebrand, F., & Narbad, A. (2021) · npj Parkinson's Disease · doi:10.1038/s41531-021-00156-z · Cited by 707
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Karmakar, M., Lantz, P. M., & Tipirneni, R. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.36462 · Cited by 580
A practical solution to pseudoreplication bias in single-cell studies
Zimmerman, K. D., Espeland, M. A., & Langefeld, C. D. (2021) · Nature Communications · doi:10.1038/s41467-021-21038-1 · Cited by 356
Bryois, J., Calini, D., Macnair, W., Foo, L., Urich, E., Ortmann, W., Iglesias, V. A., Selvaraj, S., Nutma, E., Marzin, M., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01128-z · Cited by 311
Metadata
- Authors
- Brooks, M., Kristensen, K., Benthem, K., Magnusson, A., Berg, C., Nielsen, A., Skaug, H., Mächler, M., & Bolker, B.
- Venue
- The R Journal
- Publisher
- The R Foundation
- Published
- 2017
- Volume
- 9
- Issue
- 2
- Pages
- 378
- ISSN
- 2073-4859 (ISSN-L)
- OpenAlex
- W2786358356
- Cited by
- 12,915
- References
- 3
- FWCI
- 188.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
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