add lifecycle badges

NAMESPACE

@@ -25,5 +25,6 @@ importFrom(doRNG,"%dorng%")
 importFrom(foreach,"%:%")
 importFrom(foreach,"%do%")
 importFrom(foreach,"%dopar%")
+importFrom(lifecycle,deprecated)
 importFrom(magrittr,"%>%")
 useDynLib(WpProj, .registration = TRUE)

R/HC.R

@@ -28,6 +28,8 @@
 #' 
 #' @return a `WpProj` object with selected covariates and their values
 #' 
+#' @references Hahn, P. Richard and Carlos M. Carvalho. (2014) "Decoupling Shirnkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective." <https://arxiv.org/pdf/1408.0464.pdf>
+#' 
 #' @export
 #' 
 #' @examples

R/WpProj-package.R

@@ -11,6 +11,7 @@
 "_PACKAGE"
 
 ## usethis namespace: start
+#' @importFrom lifecycle deprecated
 #' @importFrom magrittr %>%
 ## usethis namespace: end
 NULL
@@ -20,4 +21,4 @@ NULL
 dummy_fun <- function() {
   rqPen::rq.group.pen()
   lifecycle::badge("experimental")
-}
+}

R/WpProj.R

@@ -36,7 +36,7 @@
 #' 
 #' ## Wasserstein distances and powers
 #' The Wasserstein distance is a measure of distance between two probability distributions. It is defined as:
-#' \deqn{W_p(\mu,\nu) = \left(\inf_{\pi \in \Pi(\mu,\nu)} \int_{\mathbb{R}^d \times \mathbb{R}^d} \|x-y\|^p d\pi(x,y)\right)^{1/p}}.
+#' \deqn{W_p(\mu,\nu) = \left(\inf_{\pi \in \Pi(\mu,\nu)} \int_{\mathbb{R}^d \times \mathbb{R}^d} \|x-y\|^p d\pi(x,y)\right)^{1/p},}
 #' where \eqn{\Pi(\mu,\nu)} is the set of all joint distributions with marginals \eqn{\mu} and \eqn{\nu}. The Wasserstein distance is a generalization of the Euclidean distance, which is the case when \eqn{p=2}. In our function we have argument `power` that corresponds to the \eqn{p} of the equation above. The default `power` is `2.0` but any value greater than or equal to `1.0` is allowed. For more information, see the references.
 #' 
 #' The particular implementation of the Wasserstein distance is as follows. If  \eqn{\mu} is the original prediction from the original model, then we seek to find a new prediction \eqn{\nu} that minimizes the Wasserstein distance between the two: \eqn{\text{argmin}_\nu W_p(\mu,\nu)}.