Functions for generating prior functions for use with
mcmc, etc.
make.prior.exponential(r)
make.prior.uniform(lower, upper, log=TRUE)
Scalar or vector of rate parameters
Lower bound of the parameter
Upper bound of the parameter
Logical: should the prior be on a log basis?
Richard G. FitzJohn
The exponential prior probability distribution has probability
density $$\sum_i r_i e^{-r_i x_i}$$
where the \(i\) denotes the \(i\)th parameter. If r is a
scalar, then the same rate is used for all parameters.
These functions each return a function that may be used as the
prior argument to mcmc().