# R/hypergeometric1F1.R In BAS: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

#### Documented in hypergeometric1F1

```#' Confluent hypergeometric2F1 function
#'
#' Compute the Confluent Hypergeometric function: 1F1(a,b,c,t) =
#' Gamma(b)/(Gamma(b-a)Gamma(a)) Int_0^1 t^(b-1) (1 - t)^(b-a-1) exp(c t) dt
#'
#'
#' @param a arbitrary
#' @param b Must be greater 0
#' @param c arbitrary
#' @param laplace The default is to use the Cephes library; for large a or s
#' this may return an NA, Inf or negative values,, in which case you should use
#' the Laplace approximation.
#' @param log if TRUE, return log(1F1)
#' @author Merlise Clyde (\email{[email protected]@stat.duke.edu})
#' @references Cephes library hyp1f1.c
#' @keywords math
#' @examples
#' hypergeometric1F1(11.14756, 0.5, 0.00175097)
#'
#'
#' @rdname hypergeometric1F1
#' @family special functions
#' @export
hypergeometric1F1 = function(a,b,c, laplace=FALSE, log=TRUE) {

n = length(a);
out = rep(0, n);
ans = .C(C_hypergeometric1F1, as.numeric(a), as.numeric(b), as.numeric(c), out=as.numeric(out), as.integer(n),
as.integer(rep(laplace, n)))\$out
if (!log) ans = exp(ans)
return(ans)
}
```

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BAS documentation built on June 7, 2018, 5:04 p.m.