library('bayescount')
if(FALSE){
pre <- rnbinom(100, 1, mu=100)
post <- rnbinom(100, 1, mu=20)
pre <- rnbinom(100, 1, mu=10)
post <- rnbinom(100, 1, mu=1)
bayescount:::fecrt_analyses(pre, post)
post <- round(pre * 0.1)
cov(pre,post)
bayescount:::fecrt_analyses(pre, post)
## Outstanding checks:
# C++ BNB, WAAVP, Dobson and Assymptotic methods for fixed dataset
# fecrt_power vs R script generating the same data (first copy power_comparison code to power)
# fecrt_power_comparison
q <- sample(0:10,1)
k <- runif(1,0.1,10)
a <- runif(1,0.1,10)
b <- runif(1,0.1,10)
library('brr')
p1 <- pbeta_nbinom(q,k,a,b)
library('bayescount')
p2 <- bayescount:::pbnb1(q,k,a,b)
p3 <- bayescount:::pbnb2(q,k,a,b)
abs(p1-p2)
abs(p1-p3)
}
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