two PACE functions added

Author: JamesRamsay5

.DS_Store

@@ -1,5 +1,5 @@
 Package: fda
-Version: 5.5.2
+Version: 6.0.0
 Date: 2022-04-19
 Title: Functional Data Analysis
 Author: J. O. Ramsay <ramsay@psych.mcgill.ca> [aut,cre], 

DESCRIPTION

@@ -691,7 +691,7 @@ CSTRfn <- function(parvec, datstruct, fitstruct,
 
 #    DcDtheta <- D2GDc2\D2GDc
     DcDtheta <- try(solve(D2GDc2, D2GDc))
-    if(class(DcDtheta)=="try-error"){
+    if(inherits(DcDtheta,"try-error")) {
 #
       D2GDc2.eig <- eigen(D2GDc2, symmetric=TRUE)
       Dc.ev <- D2GDc2.eig$values

R/CSTRfn.R

@@ -146,7 +146,7 @@ basisfd <- function(type, rangeval, nbasis, params, dropind=vector("list",0),
 
   #  if first argument is a basis object, return
 
-  if (class(type)=="basisfd"){
+  if (inherits(type,"basisfd")) {
     basisobj <- type
     return(basisobj)
   }

R/basisfd.R

@@ -44,7 +44,7 @@ pcaPACE <- function(covestimate, nharm = 2, harmfdPar=NULL, cross = TRUE)
 
   #  set up cross product and penalty matrices
 
-  if(class(covestimate$cov.estimate) == "bifd"){
+  if(inherits(covestimate$cov.estimate, "bifd")) {
     Wmat = covestimate$cov.estimate$coefs
     nvar = 1
     basisobj = covestimate$cov.estimate$sbasis

R/pcaPACE.R

@@ -13,7 +13,7 @@ scoresPACE <- function(data, time, covestimate, PC){
   phi = lapply(indexes, function(x) eval.fd(x,PC$harmonics))
 
 
-  if(class(covestimate$cov.estimate) == "bifd"){
+  if(inherits(covestimate$cov.estimate, "bifd")) {
     nvar = 1
     mean.point = matrix(eval.fd(time, covestimate$meanfd),nrow = nrow(eval.fd(time, covestimate$meanfd)),ncol = nvar)
     data.var.mat = (apply(datamat,2, function(x) x - mean.point))^2 #data given as matrix

R/scoresPACE.R

@@ -240,7 +240,7 @@ fRegress(y, ...)
       \item{Cmatinv:} {A matrix containing the inverse of the coefficient matrix for
 	the  linear equations that define the solution to the regression
 	problem.  This matrix is required for function
-	\code{\link{fRegress.stderr}} that estimates confidence regions
+	\code{fRegress.stderr} that estimates confidence regions
 	for the regression coefficient function estimates.}
       \item{wt:} {The vector of weights input or inferred.}