six files fixed

Author: James Ramsay

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: fda
-Version: 6.1.2
-Date: 2023-01-31
+Version: 6.1.3
+Date: 2023-04-24
 Title: Functional Data Analysis
 Authors@R: c(person("James", "Ramsay", 
             role=c("aut","cre"),
@@ -11,7 +11,7 @@ Authors@R: c(person("James", "Ramsay",
           person("Spencer", "Graves", 
             role="ctb",
             email="spencer.graves@effectivedefense.org"))
-Maintainer: J. O. Ramsay <james.ramsay@mcgill.ca>
+Maintainer: James Ramsay <ramsay@psych.mcgill.ca>
 Depends: R (>= 3.5), splines, fds, deSolve
 Suggests: 
     rmarkdown,

R/.DS_Store

@@ -1,5 +1,5 @@
 eval.surp <- function(evalarg, Wfdobj, nderiv=0) {
-  #  Evaluates a value of a surprisal coordinate functional  data object. 
+  #  Evaluates a value of a surprisal coordinate functional data object. 
   # #  Evaluates a value or a derivative of a surprisal coordinate functional  
   # #  data object. 
   #  A positive functional data object h  is <- the form
@@ -16,7 +16,7 @@ eval.surp <- function(evalarg, Wfdobj, nderiv=0) {
   #  Returns:  An array of function values corresponding to the 
   #              argument values in EVALARG
 
-  #  Last modified 24 September 2021 by Jim Ramsay
+  #  Last modified 25 April 2025 by Jim Ramsay
 
   #  check arguments
 
@@ -86,6 +86,9 @@ eval.surp <- function(evalarg, Wfdobj, nderiv=0) {
   SumMXmat <- matrix(rep(sum0,each=M), ncol=M, byrow=TRUE)
 
   #  Case where EVALARG is a vector of values to be used for all curves
+  #  NB:  Resist the temptation of use /log(M) anywhere else.  
+  #  This code sets up Smat as defined with log basis M, and no further
+  #  definition of log basis is required.
 
   if (nderiv == 0) {
     Smat   <- -Xmat + log(SumMXmat)/log(M)
@@ -115,7 +118,8 @@ eval.surp <- function(evalarg, Wfdobj, nderiv=0) {
     matSum  <- rowSums(Pmat*DXmat)
     Rmat    <- matrix(rep(matSum,each=M), ncol=M, byrow=TRUE)
     matSum2 <- rowSums((Pmat*(D2Xmat + DXmat^2) - Rmat*DXmat))
-    D2Smat  <- -D2Xmat + matrix(rep(matSum2,each=M), ncol=M, byrow=TRUE)
+    D2Smat  <- -D2Xmat + 
+      matrix(rep(matSum2,each=M), ncol=M, byrow=TRUE)
     return(D2Smat)
   }
 }

R/eval.surp.R

@@ -84,6 +84,7 @@ smooth.surp <- function(argvals, y, Bmat0, WfdPar, wtvec=NULL, conv=1e-4,
 
   WfdPar   <- fdParcheck(WfdPar,M)
   Wbasis   <- WfdPar$fd$basis
+  
   Wnbasis  <- Wbasis$nbasis
   Wlambda  <- WfdPar$lambda
   Wpenalty <- eval.penalty(Wbasis, WfdPar$Lfd)

R/smooth.surp.R

@@ -1,5 +1,5 @@
 ---
-title: "BasisBasics"
+title: "Introduction to the Basics of FDA"
 output: html_document
 vignette: >
   %\VignetteIndexEntry{BasisBasics}
@@ -8,10 +8,8 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
+knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
+system.file(package="fda")
 ```
 
 ```{r setup}

data/refinery.R

@@ -1,5 +1,5 @@
 ---
-title: "monotoneFunctions"
+title: "monotone Functions"
 author: "Jim Ramsay"
 date: "16/02/2022"
 output: html_document
@@ -11,9 +11,9 @@ vignette: >
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
+system.file(package="fda")
 ```
 
-
 ## R Markdown
 
 Let $x$ be a value of a strictly monotone function $h(x)$.  Let $W(x)$ be an arbitrary unconstrained function.
@@ -39,7 +39,7 @@ print(paste("h(2*pi) =",round(hof2pi,2)))
 
 Now display the complete function $h(x)$ from 0 to $2 \pi$ along with $W(x)$.
 
-```{r,fig.height = 7}
+```{r,fig.width = 7}
 h <- delta*(cumsum(EW) - EW[101]/2)
 par(mfcol=c(2,1))
 plot(x, W, type="l")
@@ -50,7 +50,7 @@ Notice that, near $W(\pi/2) = 1$, $h$ is increasing the most rapidly, and that w
 
 Often we want $h(x)$ to end up at a fixed point.  Let's use 1, for example.  Then we simply divide $h(x)$ by $h(2 \pi)$.
 
-```{r,fig.height = 7}
+```{r,fig.width = 7}
 h <- delta*(cumsum(EW) - EW[101]/2)/hof2pi
 par(mfcol=c(2,1))
 plot(x, W, type="l")