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Volume 100 of @jstatsoft: Software for Bayesian Statistics
Guest editors: @micameletti & @precariobecario
20 contributions on a wide range of methods #rstats #python #bayesian #inla @mcmc_stan #nimble #brms #rjags
URL: jstatsoft.org/v100
Guest editors: @micameletti & @precariobecario
20 contributions on a wide range of methods #rstats #python #bayesian #inla @mcmc_stan #nimble #brms #rjags
URL: jstatsoft.org/v100
Van Niekerk, Bakka, Rue, Schenk:
New Frontiers in Bayesian Modeling Using the INLA Package in R
#rstats #inla #bayesian
doi.org/10.18637/jss.v…
New Frontiers in Bayesian Modeling Using the INLA Package in R
#rstats #inla #bayesian
doi.org/10.18637/jss.v…
Michaud, De Valpine, Turek, Paciorek, Nguyen:
Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages
#rstats #bayesian #nimble
doi.org/10.18637/jss.v…
Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages
#rstats #bayesian #nimble
doi.org/10.18637/jss.v…
I've made a cheat sheet and a bunch of applets to give you an intuitive feel for various reaction time distributions. You can choose datasets, fiddle with parameters, and see working code examples: lindeloev.net/shiny/rt/ 1/n
You can choose among several pre-existing datasets, e.g., by @EJWagenmakers and @JeffRouder. You can also copy-paste in your own data and try fitting different distributions. 2/n
For example, take the Ratcliff diffusion model for a spin. You can upload your own data to see if it fits. Or you can plug in some parameters from a paper to get an intuition about their findings. 3/n
