Moved sginv() and xTAx_seigen() into statnet.common and bumped the re…

…quired version.

DESCRIPTION

@@ -25,10 +25,8 @@ Imports:
     robustbase (>= 0.93.7),
     coda (>= 0.19.4),
     trust (>= 0.1.8),
-    Matrix (>= 1.3.2),
     lpSolveAPI (>= 5.5.2.0.17.7),
-    MASS (>= 7.3.53.1),
-    statnet.common (>= 4.9.0),
+    statnet.common (>= 4.10.0),
     rle (>= 0.9.2),
     purrr (>= 0.3.4),
     rlang (>= 0.4.10),

NAMESPACE

@@ -279,8 +279,6 @@ import(purrr)
 import(rle)
 import(statnet.common)
 import(stats)
-importFrom(MASS,ginv)
-importFrom(Matrix,nearPD)
 importFrom(Rdpack,reprompt)
 importFrom(coda,mcmc)
 importFrom(graphics,abline)

R/ergm.utility.R

@@ -264,59 +264,3 @@ trim_env_const_formula <- function(x, keep=NULL){
 
   if(needs_env) x else trim_env(x, keep)
 }
-
-### A patched version of statnet.common::sginv() that uses
-### eigendecomposition rather than the SVD for the case when symmetric
-### non-negative-definite (snnd) is TRUE.
-###
-### TODO: Delete after incorporation into statnet.common.
-sginv <- function(X, tol = sqrt(.Machine$double.eps), ..., snnd = TRUE){
-  if(!snnd) statnet.common::sginv(X, ..., snnd)
-
-  d <- diag(as.matrix(X))
-  d <- ifelse(d==0, 1, 1/d)
-
-  d <- sqrt(d)
-  if(anyNA(d)) stop("Matrix a has negative elements on the diagonal, and snnd=TRUE.")
-  dd <- rep(d, each = length(d)) * d
-  X <- X * dd
-
-  ## Perform inverse via eigendecomposition, removing too-small eigenvalues.
-  e <- eigen(X, symmetric=TRUE)
-  keep <- e$values > max(tol * e$values[1L], 0)
-  e$vectors[, keep, drop=FALSE] %*% ((1/e$values[keep]) * t(e$vectors[, keep, drop=FALSE])) * dd
-}
-
-#' Evaluate a quadratic form with a possibly singular matrix using
-#' eigendecomposition after scaling to correlation.
-#'
-#' Let \eqn{A} be the matrix of interest, and let \eqn{D} is a
-#' diagonal matrix whose diagonal is same as that of \eqn{A}.
-#'
-#' Let \eqn{R = D^{-1/2} A D^{-1/2}}. Then \eqn{A = D^{1/2} R D^{1/2}} and
-#' \eqn{A^{-1} = D^{-1/2} R^{-1} D^{-1/2}}.
-#'
-#' Decompose \eqn{R = P L P'} for \eqn{L} diagonal matrix of eigenvalues
-#' and \eqn{P} orthogonal. Then \eqn{R^{-1} = P L^{-1} P'}.
-#'
-#' Substituting,
-#' \deqn{x' A^{-1} x  = x' D^{-1/2} P L^{-1} P' D^{-1/2} x = h' L^{-1} h}
-#' for \eqn{h = P' D^{-1/2} x}.
-#'
-#' @noRd
-xTAx_seigen <- function(x, A, tol=sqrt(.Machine$double.eps), ...) {
-  d <- diag(as.matrix(A))
-  d <- ifelse(d<=0, 0, 1/d)
-
-  d <- sqrt(d)
-  dd <- rep(d, each = length(d)) * d
-  A <- A * dd
-
-  e <- eigen(A)
-
-  keep <- e$values > max(tol * e$values[1L], 0)
-
-  h <- drop(crossprod(x*d, e$vectors[,keep,drop=FALSE]))
-
-  structure(sum(h*h/e$values[keep]), rank = sum(keep), nullity = sum(!keep))
-}

R/zzz.R

@@ -8,8 +8,6 @@
 #  Copyright 2003-2023 Statnet Commons
 ################################################################################
 #' @importFrom Rdpack reprompt
-#' @importFrom MASS ginv
-#' @importFrom Matrix nearPD
 .onAttach <- function(libname, pkgname){
   #' @importFrom statnet.common statnetStartupMessage
   sm <- statnetStartupMessage("ergm", c("statnet","ergm.count","tergm"), TRUE)