removing ppp back to Rfast

Author: william-denault

DESCRIPTION

@@ -25,6 +25,7 @@ Imports:
     smashr,
     Rcpp,
     ebnm,
+    Rfast,
 Suggests:
     susieR,
     knitr,

NAMESPACE

@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
-useDynLib(fsusieR, .registration = TRUE)
-importFrom(Rcpp, sourceCpp)
+
 S3method(Eloglik,susiF)
 S3method(HMM_regression,susiF)
 S3method(L_mixsq,mixture_normal)

R/RcppExports.R

@@ -63,7 +63,9 @@ cal_cor_cs <- function(obj,X){
 # \item{Shat}{ matrix pxJ standard error, Shat[j,t] corresponds to standard error of the regression coefficient of Y[,t] on X[,j] }
 #
 # @export
-#
+#' @importFrom Rfast colsums
+#' @importFrom Rfast colVars
+#' @importFrom Rfast cova
 cal_Bhat_Shat   <- function(Y,
                             X ,
                             v1 ,
@@ -77,12 +79,12 @@ cal_Bhat_Shat   <- function(Y,
   if(missing(ind_analysis)){
 
 
-    d <- colSumsCpp(X^2)
+    d <- colSums(X^2)
     Bhat <- (t(X)%*%Y )/d
 
     Shat  <- do.call( cbind,
                       lapply( 1:ncol(Bhat),
-                              function(i)  ( colVarsCpp(  Y [,i] -sweep( X,2, Bhat[,i], "*")))
+                              function(i)  (Rfast::colVars(  Y [,i] -sweep( X,2, Bhat[,i], "*")))
                       )
     )
 
@@ -95,7 +97,7 @@ cal_Bhat_Shat   <- function(Y,
 
       Bhat <-  do.call(cbind,lapply(1:length(ind_analysis),
                                     function(l){
-                                      d   <-  colSumsCpp(X[ind_analysis[[l]], ]^2)
+                                      d   <- Rfast::colsums(X[ind_analysis[[l]], ]^2)
                                       out <- (t(X[ind_analysis[[l]], ])%*%Y[ind_analysis[[l]], l])/d
                                       return(out)
                                     }
@@ -105,7 +107,7 @@ cal_Bhat_Shat   <- function(Y,
 
 
       Shat  <-   matrix(mapply(function(l,j)
-        ( covCpp(matrix(Y[ind_analysis[[l]],l] - X[ind_analysis[[l]], j]  *  Bhat[j,l])) /(length(ind_analysis[[l]])-1)),
+        (Rfast::cova(matrix(Y[ind_analysis[[l]],l] - X[ind_analysis[[l]], j]  *  Bhat[j,l])) /(length(ind_analysis[[l]])-1)),
         l=rep(1:dim(Y)[2],each= ncol(X)),
         j=rep(1:dim(X)[2], ncol(Y))
       ),
@@ -114,12 +116,12 @@ cal_Bhat_Shat   <- function(Y,
       Shat<- sqrt( pmax(Shat, 1e-64))
 
     }else{
-      d <-  colSumsCpp(X[ind_analysis , ]^2)
+      d <- Rfast::colsums(X[ind_analysis , ]^2)
       Bhat <- (t(X[ind_analysis , ])%*%Y[ind_analysis , ])/d
 
       Shat  <- do.call( cbind,
                         lapply( 1:ncol(Bhat),
-                                function(i) ( colVarsCpp(Y [ind_analysis,i] -sweep( X[ind_analysis,],2, Bhat[ ,i], "*")))
+                                function(i) (Rfast::colVars(Y [ind_analysis,i] -sweep( X[ind_analysis,],2, Bhat[ ,i], "*")))
                         )
       )
       Shat<- sqrt( pmax(Shat, 1e-64))
@@ -143,6 +145,7 @@ cal_Bhat_Shat   <- function(Y,
 }
 
 
+
 #' @title Compute likelihood for the weighted ash problem
 #
 #' @description Add description here.

R/computational_functions.R

@@ -26,15 +26,15 @@ is.wholenumber <- function (x, tol = .Machine$double.eps^0.5)
 '%!in%' <- function(x,y)!('%in%'(x,y))
 
 # Based on Rfast implementation.
-#
+#' @importFrom Rfast cova
 fast_lm <- function(x,y)
 {
 
-    be <- solve(crossprod(x),crossprod(x,y))
-    sd <-  sqrt(covCpp(y - x %*% be) /(length(x)-1))
+  be <- solve(crossprod(x),crossprod(x,y))
+  sd <-  sqrt(Rfast::cova(y - x %*% be) /(length(x)-1))
 
 
-    return(c(be,sd))
+  return(c(be,sd))
 }