This function has options for a likelihood that is either normal- or t-distributed, and a model of H1 that is either uniform, or normal or t- (or Cauchy-) distributed, with the normal/t/cauchy models being 1- or 2-tailed. In addition, the 1-tailed models are compatible with any mode (unlike the Dienes, 2008, calculator that assumed that 1-tailed models had a mode of zero). Thanks to Bence Palfi for pulling various bits of my code together to make this one all-inclusive function!

# BF function

Bf<-function(sd, obtained, dfdata = 1, likelihood = c("normal", "t"), modeloftheory= c("normal","t","cauchy", "uniform") ,lower =0, upper=1, modeoftheory = 0, scaleoftheory = 1, dftheory = 1, tail = 2)

{

if(likelihood=="normal"){

dfdata=10^10

}

if(modeloftheory=="normal"){

dftheory = 10^10

} else if(modeloftheory=="cauchy"){

dftheory = 1

}

area <- 0

normarea <- 0

if(modeloftheory=="uniform"){

theta <- lower

range <- upper - lower

incr <- range / 2000

for (A in -1000:1000){

theta <- theta + incr

dist_theta <- 1 / range

height <- dist_theta * dt((obtained-theta)/sd, df=dfdata)

area <- area + height * incr

}

LikelihoodTheory <- area

}else{

theta <- modeoftheory - 8 * scaleoftheory

incr <- scaleoftheory/200

for (A in -1600:1600){

theta <- theta + incr

dist_theta <- dt((theta-modeoftheory)/scaleoftheory, df=dftheory)

if(identical(tail, 1)){

if (theta <= modeoftheory){

dist_theta <- 0

} else {

dist_theta <- dist_theta * 2

}

}

height <- dist_theta * dt((obtained-theta)/sd, df = dfdata)

area <- area + height * incr

normarea <- normarea + dist_theta*incr

}

LikelihoodTheory <- area/normarea

}

Likelihoodnull <- dt(obtained/sd, df = dfdata)

BayesFactor <- LikelihoodTheory/Likelihoodnull

BayesFactor

}