Updated docs

Author: andrewherren

R/bcf.R

@@ -55,7 +55,7 @@
 #' @param adaptive_coding Whether or not to use an "adaptive coding" scheme in which a binary treatment variable is not coded manually as (0,1) or (-1,1) but learned via parameters `b_0` and `b_1` that attach to the outcome model `[b_0 (1-Z) + b_1 Z] tau(X)`. This is ignored when Z is not binary. Default: T.
 #' @param b_0 Initial value of the "control" group coding parameter. This is ignored when Z is not binary. Default: -0.5.
 #' @param b_1 Initial value of the "treatment" group coding parameter. This is ignored when Z is not binary. Default: 0.5.
-#' @param rfx_prior_var Prior (diagonals of the) covariance of the random effects model. Must be a vector of length `ncol(rfx_basis_train)`. Default: `rep(1, ncol(rfx_basis_train))`
+#' @param rfx_prior_var Prior on the (diagonals of the) covariance of the additive group-level random regression coefficients. Must be a vector of length `ncol(rfx_basis_train)`. Default: `rep(1, ncol(rfx_basis_train))`
 #' @param random_seed Integer parameterizing the C++ random number generator. If not specified, the C++ random number generator is seeded according to `std::random_device`.
 #' @param keep_burnin Whether or not "burnin" samples should be included in cached predictions. Default FALSE. Ignored if num_mcmc = 0.
 #' @param keep_gfr Whether or not "grow-from-root" samples should be included in cached predictions. Default FALSE. Ignored if num_mcmc = 0.