Fractals and Statistics: An R Package Called IFS

By S. Iacus, Davide La Torre. 2003

4,193 citations recorded by the source corpus.

Subfields: Condensed Matter Physics, Economics and Econometrics, Mathematical Physics, Statistics and Probability

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Citations in the corpus

An Introduction to Probabilistic Programming (2018, background): “, 2018), STAN (Stan Development Team, 2014), JAGS (Plummer, 2003), BUGS (Spiegelhalter et al.”
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo (2011, background): “The need to tune Lmakes it especially challenging to increase the scalability of general-purpose inference engines such as BUGS [5], JAGS [6], or Infer.”
Automatic Differentiation Variational Inference (2016, neutral): “, 1995), JAGS (Plummer, 2003), and Stan (Stan Development Team, 2016).”
Stan: A Probabilistic Programming Language (2017, background): “Compared to BUGS and JAGS , Stan is often relatively slow per iteration but relatively fast per eﬀective sample.”
Estimating Fisheries Reference Points from Catch and Resilience (2017, methodology): “The JAGS software (Plummer 2003) was used for sampling the probability distributions of the parameters with the Markov chain Monte Carlo method.”
Definition and estimation of vital rates from repeated censuses: Choices, comparisons and bias corrections focusing on trees (2017, methodology): “Kohyama, Kohyama, and Sheil (2017) in the Dryad Digital Repository: https://doi.org/10.5061/dryad.0722b provides a tutorial for estimating vital rates using JAGS via R (ver.”
Biips: Software for Bayesian Inference with Interacting Particle Systems (2014, methodology): “Biips belongs to the BUGS language software family of WinBUGS , OpenBUGS (Lunn et al. 2000, 2012) and JAGS software (Plummer 2003).”
Integrating demographic data: towards a framework for monitoring wildlife populations at large spatial scales (2014, methodology): “To explore and summarize the posterior distribution, we used the Markov chain Monte Carlo (MCMC) algorithm implemented in JAGS 3.3.0 called via the R package RJAGS 3.10 (Plummer 2003, 2013).”
Dealing with false‐positive and false‐negative errors about species occurrence at multiple levels (2017, methodology): “Implementation of the model in programs such as BUGS or JAGS (Plummer 2003; Lunn et al. 2009) is in principle straightforward, but applying these general-purpose tools can potentially lead to the same inference problems discussed above (Appendix S6).”
Bayesian Item Response Modeling in R with brms and Stan (2019, methodology): “Similarly, blavaan can fit a subset of the models supported by lavaan using MCMCmethods implemented in JAGS (Plummer 2003) or Stan (Carpenter et al.”
Generalized Additive Models: An Introduction with R (2nd Edition) (2018, methodology): “For instance, the JAGS software (Plummer 2003) for Bayesian inference is sometimes used (Wood 2016).”
The pitfalls of ignoring behaviour when quantifying habitat selection (2014, methodology): “Models were fitted using Monte Carlo Markov Chain (MCMC) techniques implemented in the software JAGS (Plummer, 2003) using three chains with a burn-in period of 10,000 samples, which were discarded, followed by 50,000 samples, thinned to retain every 25th sample, resulting in 2000 independent sample”