if (FALSE) {
library(brms)
library(neodistr)
x<-runif(100)
e<-rmsnburr(100,0,1,0.8)
y<-0.5+0.8*x+e
data<-data.frame(y,x)
msnburr<-brms_custom_family("msnburr")
fit <- brm(
y ~ x, data = data,
family = msnburr$custom_family, stanvars = msnburr$stanvars_family,
prior=c(set_prior("cauchy(0,5)",class="alpha"),set_prior("cauchy(0,1)",class="sigma"))
)
summary(fit)
pp <- posterior_predict(fit)
ppe <- posterior_epred(fit)
loo(fit)
}
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