version 1.1-1

cran · May 29, 2013 · 0a5419b · 0a5419b
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diff --git a/DESCRIPTION b/DESCRIPTION
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+Package: AppliedPredictiveModeling
+Type: Package
+Title: Functions and Data Sets for 'Applied Predictive Modeling'
+Version: 1.1-1
+Date: 2013-05-29
+Author: Max Kuhn, Kjell Johnson
+Maintainer: Max Kuhn <[email protected]>
+Description: A few functions and several data set for the Springer book
+        'Applied Predictive Modeling'
+URL: http://appliedpredictivemodeling.com/
+Depends: R (>= 2.10), CORElearn, MASS, plyr, reshape2
+Suggests: caret, lattice, ellipse
+License: GPL
+Packaged: 2013-05-29 19:34:07 UTC; kuhna03
+NeedsCompilation: no
+Repository: CRAN
+Date/Publication: 2013-05-30 07:31:33
diff --git a/MD5 b/MD5
@@ -0,0 +1,75 @@
+a1ba1a42e2fd42c2b6af9ba42e6e5933 *DESCRIPTION
+8b54e5a89fbda3af5e077053d40bec76 *NAMESPACE
+e4e564d2188913c297d854a86868bd37 *R/bookTheme.R
+16af3f1f03fc98647e26edefe3c1ebce *R/easyBoundaryFunc.R
+f4e5463cfcc4da4261f8014b1426c10c *R/getPackages.R
+35a9e06d580a6ed8b8d98c9f3c0a61eb *R/panels.R
+f3956e0be5393bb5d6fcbcfef0b6ff29 *R/permuteRelief.R
+c3519cd360a2dd39cef5b453f8551bc9 *R/quadBoundaryFunc.R
+d35f915bd2268cbb07c258bd8fce5c50 *R/scriptLocation.R
+98d928db47d8347a4f886f0c8e4adde1 *R/transparentTheme.R
+8a34126ad3a2f9d077653b26d950dddb *data/AlzheimerDisease.RData
+ebd3302a547a1064620517a0598f9ebf *data/ChemicalManufacturingProcess.RData
+ef3addd28ad9449688f0c33ba9bfc2d0 *data/FuelEconomy.RData
+833d3d4a90e6afe16ec007d5fc628cd2 *data/abalone.RData
+8fe13332a2419a2c253fb51c396f6000 *data/concrete.RData
+8fc335fdac839cc09b91ff3b5595f00b *data/datalist
+812edff8771d7faaada62fdb2c662e40 *data/hepatic.RData
+7d5b8064233260c344793aaecc045136 *data/logisticCreditPredictions.RData
+ce7019b604378875d2a55e32dde9c05a *data/permeability.RData
+e1590269851cf810fdffa832b6cf6d65 *data/schedulingData.RData
+669172e9b524f9194a23fbc84a2816f8 *data/segmentationOriginal.RData
+06780bd86a4db76cb2a8eb12ef107df7 *data/solubility.RData
+5e5422a8c05125f3ab1822f6c525296a *data/twoClassData.RData
+202cb28b25a21e6cb4f4182056cd3636 *inst/NEWS.Rd
+55afb317aa767a6e82c6c52ee985563f *inst/chapters/02_A_Short_Tour.R
+1f2f2179f8756bc60a5db4d285384e53 *inst/chapters/02_A_Short_Tour.Rout
+fa5a1a6cd542c0f02a5db901afbccc7e *inst/chapters/03_Data_Pre_Processing.R
+b740e1169a13b1d720dbdf82c220e72d *inst/chapters/03_Data_Pre_Processing.Rout
+73ff45e8ce4a2afd6792b3bf7f74d4d0 *inst/chapters/04_Over_Fitting.R
+4472854d26e70e0fbbb5fb389bf03abc *inst/chapters/04_Over_Fitting.Rout
+894a4d8414f73b412f6e4fc8933b1681 *inst/chapters/06_Linear_Regression.R
+cdcf0f83fd8d7d3f27d2c01c310aa68c *inst/chapters/06_Linear_Regression.Rout
+397c1aa95ea73468e2a1e706488faf37 *inst/chapters/07_Non-Linear_Reg.R
+8fcecf4b5e24eeda5fc55927f4f76735 *inst/chapters/07_Non-Linear_Reg.Rout
+d2129d815070f3edd9206227ebe24b9d *inst/chapters/08_Regression_Trees.R
+578e20ebfbd069f0ae2f1a595c323da5 *inst/chapters/08_Regression_Trees.Rout
+ad9f505564c6ca809b9970182f3f8790 *inst/chapters/10_Case_Study_Concrete.R
+83e761236b56017776258ca14149c13d *inst/chapters/10_Case_Study_Concrete.Rout
+c3693cc6dc941a60844d864cf264db28 *inst/chapters/11_Class_Performance.R
+e59029dd4ddb012b5100f5cae3c3fe5a *inst/chapters/11_Class_Performance.Rout
+512616afef1588d9f26d24a45dd577d7 *inst/chapters/12_Discriminant_Analysis.R
+6abe970f2d4d9c64d25e2da453184ab6 *inst/chapters/12_Discriminant_Analysis.Rout
+793602cd5edfc82f9bfef2c53a2fd9dc *inst/chapters/13_Non-Linear_Class.R
+10baa59ef6e6600c44f92a372c96de8e *inst/chapters/13_Non-Linear_Class.Rout
+c1a9e721dafd1f68b415ca3e611d794c *inst/chapters/14_Class_Trees.R
+e33ae9349b008519d48084fca2278005 *inst/chapters/14_Class_Trees.Rout
+2422c3ed8b8f5f0b46c6f1586da4af3e *inst/chapters/16_Class_Imbalance.R
+9331611025109d739d54646693ad71ad *inst/chapters/16_Class_Imbalance.Rout
+ab2f1dc1fb60cdb116d7e335f69d75a9 *inst/chapters/17_Job_Scheduling.R
+9d98d6048300c4f2bbc808d814a1c67f *inst/chapters/17_Job_Scheduling.Rout
+cdd39d98758aa17566201c45150265b8 *inst/chapters/18_Importance.R
+fadb4b6bf8ebbfcd2a28ec12cef5ad1b *inst/chapters/18_Importance.Rout
+4e9ffdf738004484e37287d8df2e3726 *inst/chapters/19_Feature_Select.R
+fe14eb0e0a390ab5522b5548bb50ff8f *inst/chapters/19_Feature_Select.Rout
+ee8d141c6ff92f1878bb1954d21cab67 *inst/chapters/CreateGrantData.R
+6b7d3facf17c4ad5704ca9c54c17acc1 *inst/chapters/CreateGrantData.Rout
+6a51123bb7533bc6ac7cc60e20c30f7c *man/AlzheimerDisease.Rd
+79b66304686ea5f41624e941a839f783 *man/AppliedPredictiveModeling-package.Rd
+2f60f009e2049b3a23bfce4321c6961e *man/ChemicalManufacturingProcess.Rd
+b8fb23f2d87770651df5c0b9ab178180 *man/FuelEconomy.Rd
+a114aed8c4e19f6e471f76aa10607efc *man/Hepatic.Rd
+bb766d31a2c9a73fb64a83ad8edcbf9d *man/abalone.Rd
+7b4f4f04359281d886b4dd90765c2a29 *man/bookTheme.Rd
+1a3a9b303f7a599f89cd2292a359511e *man/concrete.Rd
+20acadadd6df9fce573aea2b7ee52020 *man/getPackages.Rd
+8a02ecb81e0750c23ee2711b492dde91 *man/internal.Rd
+f3f357c33a2b2433fee6c8cecd4876fe *man/jobScheduling.Rd
+e422ed025d73fac0cd25de1a2146af1a *man/logisticCreditPredictions.Rd
+1b347ea3e594dc8f4b0081d90b5764a1 *man/permeability.Rd
+8559d6e7451f0060acb9b80827bb5fc3 *man/permuteRelief.Rd
+a0b9d85cec1c624144825536cc0b4993 *man/quadBoundaryFunc.Rd
+94865b7fd486f04a94e7dae86599f242 *man/scriptLocation.Rd
+d242e9c533e5abb92513999c16dd91d1 *man/segmentationOrignal.Rd
+fc76accf5ef83c49775649d296b61ff7 *man/solubility.Rd
+bc21567d7b20d731be212decec057ab5 *man/twoClassData.Rd
diff --git a/NAMESPACE b/NAMESPACE
@@ -0,0 +1 @@
+exportPattern("^[[:alpha:]]+")
diff --git a/R/bookTheme.R b/R/bookTheme.R
@@ -0,0 +1,70 @@
+bookTheme <- function(set = TRUE)
+  {
+    library(lattice)
+    theme <-    list(plot.polygon = list(alpha = 1, col = "aliceblue", border = "black", lty = 1, lwd = 1),
+                     background = list(col = "transparent"), 
+                     bar.fill = list(col = "#cce6ff"), 
+                     box.rectangle = list(col = "black"), 
+                     box.umbrella = list(col = "black"), 
+                     dot.line = list(col = "#e8e8e8"), 
+                     dot.symbol = list(col = "black"), 
+                     plot.line = list(col = "black", lwd = 1, lty = 1), 
+                     plot.symbol = list(col = "black", pch = 16), 
+                     regions = list(col = 
+                       c("#FEF8FA", "#FDF6F9", "#FBF5F9", "#FAF3F8", 
+                         "#F8F2F7", "#F7F0F7", "#F5EEF6", "#F4EDF5", 
+                         "#F2EBF5", "#F1EAF4", "#EFE8F3", "#EDE7F2", 
+                         "#ECE5F1", "#EAE4F1", "#E8E2F0", "#E6E1EF", 
+                         "#E4DFEE", "#E2DEED", "#E0DCEC", "#DEDAEB", 
+                         "#DCD9EA", "#D9D7E9", "#D7D6E8", "#D4D4E7", 
+                         "#D1D2E6", "#CED1E5", "#CCCFE4", "#C8CEE3",
+                         "#C5CCE2", "#C2CAE1", "#BFC9E0", "#BBC7DF", 
+                         "#B8C5DF", "#B4C4DE", "#B1C2DD", "#ADC0DC", 
+                         "#A9BFDB", "#A6BDDA", "#A2BBD9", "#9EB9D9", 
+                         "#9BB8D8", "#97B6D7", "#93B4D6", "#8FB2D5", 
+                         "#8BB0D4", "#87AFD3", "#83ADD2", "#7FABD1", 
+                         "#7AA9D0", "#76A7CF", "#71A5CE", "#6CA3CC", 
+                         "#68A1CB", "#63A0CA", "#5D9EC9", "#589CC8",
+                         "#539AC6", "#4E98C5", "#4996C4", "#4493C3", 
+                         "#3F91C1", "#3A8FC0", "#358DBF", "#308BBE", 
+                         "#2C89BD", "#2887BC", "#2385BB", "#1F83BA", 
+                         "#1C80B9", "#187EB7", "#157CB6", "#127AB5", 
+                         "#0F78B3", "#0D76B2", "#0A73B0", "#0971AE", 
+                         "#076FAC", "#066DAA", "#056AA7", "#0568A5")
+                       ), 
+                     strip.shingle = list(col = c(
+                                            "#ff7f00", "#00ff00", "#00ffff",
+                                            "#ff00ff", "#ff0000", "#ffff00", "#0080ff")), 
+                     strip.background = list(col = c(
+                                               "#ffe5cc", "#ccffcc", "#ccffff",
+                                               "#ffccff", "#ffcccc", "#ffffcc", "#cce6ff")), 
+                     reference.line = list(col = "#e8e8e8"), 
+                     superpose.line = list(
+                       col = c(
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black", 
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black", 
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black",
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black", 
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black", 
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black"), 
+                       lty = rep(1:6, each = 6)), 
+                     superpose.symbol = list(
+                       pch = c(
+                         1, 4, 6, 0, 5, 17,
+                         4, 6, 0, 5, 17, 1,
+                         6, 0, 5, 17, 1, 4,
+                         0, 5, 17, 1, 4, 6,
+                         5, 17, 1, 4, 6, 0 ,
+                         17, 1, 4, 6, 0, 5), 
+                       cex = rep(0.7,  6 * 6), 
+                       col = c(
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black", 
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black", 
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black",
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black", 
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black", 
+                         "#053061", "#B2182B", "#F46D43", "#5E4FA2", "#66C2A5", "black")))
+
+    if(set) trellis.par.set(theme)
+    invisible(theme)
+  }
diff --git a/R/easyBoundaryFunc.R b/R/easyBoundaryFunc.R
@@ -0,0 +1,16 @@
+
+easyBoundaryFunc <- function(n, intercept = 0, interaction = 2)
+{
+   require(MASS)
+   sigma <- matrix(c(2,1.3,1.3,2),2,2)
+
+   tmpData <- data.frame(mvrnorm(n=n, c(0,0), sigma))
+   xSeq <- seq(-4, 4, length=40)
+   plotGrid <- expand.grid(x = xSeq, y = xSeq)
+   zFoo <- function(x, y) intercept -4 * x + 4* y + interaction*x*y
+   z2p <- function(x) 1/(1+exp(-x))
+
+   tmpData$prob <- z2p(zFoo(tmpData$X1, tmpData$X2))
+   tmpData$class <- factor(ifelse(runif(length(tmpData$prob)) <= tmpData$prob, "Class1", "Class2"))
+   tmpData
+}
diff --git a/R/getPackages.R b/R/getPackages.R
@@ -0,0 +1,45 @@
+
+
+getPackages <- function(chapter, ...)
+{
+  if(is.numeric(chapter)) chapter <- paste(chapter)
+  pkg <- list()
+  pkg[["2"]] <- c("earth", "caret", "lattice")
+  pkg[["3"]] <- c("e1071", "caret", "corrplot")
+  pkg[["4"]] <- c("kernlab", "caret")
+  pkg[["6"]] <- c("lattice", "corrplot", "pls", "elasticnet")
+  pkg[["7"]] <- c("caret", "earth", "kernlab","lattice", "nnet")  
+  pkg[["8"]] <- c("caret", "Cubist", "gbm", "lattice", "party", "partykit", 
+                  "randomForest", "rpart",  "RWeka") 
+  pkg[["10"]] <- c("caret", "Cubist", "earth", "elasticnet", "gbm", "ipred", 
+                   "lattice", "nnet", "party","pls", "randomForests", "rpart", 
+                   "RWeka")  
+  pkg[["11"]] <- c("caret", "MASS", "randomForest", "pROC", "klaR")  
+  pkg[["12"]] <- c("caret", "glmnet", "lattice", 
+                   "MASS", "pamr", "pls", "pROC", "sparseLDA")  
+  pkg[["13"]] <- c("caret", "kernlab", "klaR",  "lattice", "latticeExtra", 
+                   "MASS", "mda", "nnet", "pROC")  
+  pkg[["14"]] <- c("C50", "caret", "gbm",  "lattice", "partykit", "pROC", 
+                   "randomForest", "reshape2", 
+                   "rpart", "RWeka")
+  pkg[["16"]] <- c("caret", "C50", "earth", "DMwR", "DWD", " kernlab", "mda", 
+                   "pROC", "randomForest", "rpart") 
+  pkg[["17"]] <- c("C50", "caret", "earth", "Hmisc", "ipred", "tabplot", 
+                   "kernlab", "lattice", "MASS", "mda", "nnet", "pls", 
+                   "randomForest", "rpart", "sparseLDA")
+  pkg[["18"]] <- c("caret", "CORElearn", "corrplot", "pROC", "minerva")
+  pkg[["19"]] <- c("caret", "MASS", "corrplot", "RColorBrewer", "randomForest", 
+                   "kernlab", "klaR")
+  plist <- paste(paste("'", names(pkg), "'", sep = ""), collapse = ", ")
+  if(!any(chapter %in% names(pkg))) stop(paste("'chapter' must be: ",
+                                               paste(plist, collapse = ", ")),
+                                         sep = "")  
+
+
+  pkg <- unlist(pkg[chapter])
+  pkg <- pkg[!is.na(pkg)]
+  pkg <- pkg[pkg != ""]
+  pkg <- pkg[order(tolower(pkg))]
+
+  install.packages(pkg, ...)
+}
diff --git a/R/panels.R b/R/panels.R
@@ -0,0 +1,30 @@
+upperp <- function(...)
+  {
+    library(ellipse)
+    args <- list(...)
+    circ1 <- ellipse(diag(rep(1, 2)), t = .1)
+    panel.xyplot(circ1[,1], circ1[,2],
+                 type = "l",
+                 lty = trellis.par.get("reference.line")$lty,
+                 col = trellis.par.get("reference.line")$col,
+                 lwd = trellis.par.get("reference.line")$lwd)
+    circ2 <- ellipse(diag(rep(1, 2)), t = .2)
+    panel.xyplot(circ2[,1], circ2[,2],
+                 type = "l",
+                 lty = trellis.par.get("reference.line")$lty,
+                 col = trellis.par.get("reference.line")$col,
+                 lwd = trellis.par.get("reference.line")$lwd)
+    circ3 <- ellipse(diag(rep(1, 2)), t = .3)
+    panel.xyplot(circ3[,1], circ3[,2],
+                 type = "l",
+                 lty = trellis.par.get("reference.line")$lty,
+                 col = trellis.par.get("reference.line")$col,
+                 lwd = trellis.par.get("reference.line")$lwd)
+    panel.xyplot(args$x, args$y, groups = args$groups, subscripts = args$subscripts)
+  }
+
+lowerp <- function(...)
+  {
+
+  }
+
diff --git a/R/permuteRelief.R b/R/permuteRelief.R
@@ -0,0 +1,28 @@
+permuteRelief <-
+function(x, y, nperm = 100, ...)
+  {
+    library(CORElearn)
+    library(plyr)
+    library(reshape2)
+    dat <- x
+    dat$y <- y
+
+    obs <- attrEval(y ~ ., data = dat, ...)
+    permuted <- matrix(NA, ncol = length(obs), nrow = nperm)
+    colnames(permuted) <- names(obs)
+    for(i in 1:nperm)
+      {
+        dat$y <- sample(y)
+        permuted[i,] <- attrEval(y ~ ., data = dat, ...)
+      }
+    means <- colMeans(permuted)
+    sds <- apply(permuted, 2, sd)
+    permuted <- melt(permuted)
+    names(permuted)[2] <- "Predictor"
+    permuted$X1 <- NULL
+    list(standardized = (obs - means)/sds,
+         permutations = permuted,
+         observed = obs,
+         options = list(...))
+  }
+
diff --git a/R/quadBoundaryFunc.R b/R/quadBoundaryFunc.R
@@ -0,0 +1,16 @@
+quadBoundaryFunc <-
+function(n)
+{
+   require(MASS)
+   sigma <- matrix(c(1,.7,.7,2),2,2)
+
+   tmpData <- data.frame(mvrnorm(n=n, c(1,0), sigma))
+   xSeq <- seq(-4, 4, length=40)
+   plotGrid <- expand.grid(x = xSeq, y = xSeq)
+   zFoo <- function(x, y) -1 - 2 * x - 0 * y - .2 * x^2 + 2 * y^2
+   z2p <- function(x) 1/(1+exp(-x))
+
+   tmpData$prob <- z2p(zFoo(tmpData$X1, tmpData$X2))
+   tmpData$class <- factor(ifelse(runif(length(tmpData$prob)) <= tmpData$prob, "Class1", "Class2"))
+   tmpData
+}
diff --git a/R/scriptLocation.R b/R/scriptLocation.R
@@ -0,0 +1,2 @@
+scriptLocation <- function() system.file("chapters", package = "AppliedPredictiveModeling")
+
diff --git a/R/transparentTheme.R b/R/transparentTheme.R
@@ -0,0 +1,59 @@
+transparentTheme <-
+function(set = TRUE, pchSize = 1, trans = .2)
+  {
+    library(lattice)
+    theme <-    list(plot.polygon = list(alpha = 1, col = "aliceblue", border = "black", lty = 1, lwd = 1),
+                     background = list(col = "transparent"), 
+                     bar.fill = list(col = "#cce6ff"), 
+                     box.rectangle = list(col = "black"), 
+                     box.umbrella = list(col = "black"), 
+                     dot.line = list(col = "#e8e8e8"), 
+                     dot.symbol = list(col = "black"), 
+                     plot.line = list(col = "black"), 
+                     plot.symbol = list(col = "black"), 
+                     regions = list(col = 
+                       c("#FEF8FA", "#FDF6F9", "#FBF5F9", "#FAF3F8", 
+                         "#F8F2F7", "#F7F0F7", "#F5EEF6", "#F4EDF5", 
+                         "#F2EBF5", "#F1EAF4", "#EFE8F3", "#EDE7F2", 
+                         "#ECE5F1", "#EAE4F1", "#E8E2F0", "#E6E1EF", 
+                         "#E4DFEE", "#E2DEED", "#E0DCEC", "#DEDAEB", 
+                         "#DCD9EA", "#D9D7E9", "#D7D6E8", "#D4D4E7", 
+                         "#D1D2E6", "#CED1E5", "#CCCFE4", "#C8CEE3",
+                         "#C5CCE2", "#C2CAE1", "#BFC9E0", "#BBC7DF", 
+                         "#B8C5DF", "#B4C4DE", "#B1C2DD", "#ADC0DC", 
+                         "#A9BFDB", "#A6BDDA", "#A2BBD9", "#9EB9D9", 
+                         "#9BB8D8", "#97B6D7", "#93B4D6", "#8FB2D5", 
+                         "#8BB0D4", "#87AFD3", "#83ADD2", "#7FABD1", 
+                         "#7AA9D0", "#76A7CF", "#71A5CE", "#6CA3CC", 
+                         "#68A1CB", "#63A0CA", "#5D9EC9", "#589CC8",
+                         "#539AC6", "#4E98C5", "#4996C4", "#4493C3", 
+                         "#3F91C1", "#3A8FC0", "#358DBF", "#308BBE", 
+                         "#2C89BD", "#2887BC", "#2385BB", "#1F83BA", 
+                         "#1C80B9", "#187EB7", "#157CB6", "#127AB5", 
+                         "#0F78B3", "#0D76B2", "#0A73B0", "#0971AE", 
+                         "#076FAC", "#066DAA", "#056AA7", "#0568A5")
+                       ), 
+                     strip.shingle = list(col = c(
+                                            "#ff7f00", "#00ff00", "#00ffff",
+                                            "#ff00ff", "#ff0000", "#ffff00", "#0080ff")), 
+                     strip.background = list(col = c(
+                                               "#ffe5cc", "#ccffcc", "#ccffff",
+                                               "#ffccff", "#ffcccc", "#ffffcc", "#cce6ff")), 
+                     reference.line = list(col = "#e8e8e8"), 
+                     superpose.line = list(
+                       col = c(
+                         rgb(1, 0, 0, trans), rgb(0, 0, 1, trans),
+                         rgb(0.3984375, 0.7578125, 0.6445312, max(.6, trans)),
+                         rgb(0, 0, 0, trans)), 
+                       lty = rep(1:2, 6)), 
+                     superpose.symbol = list(
+                       pch = c(16, 15, 17, 18, 16), 
+                       cex = rep(pchSize,  5), 
+                       col = c(
+                         rgb(1, 0, 0, trans), rgb(0, 0, 1, trans),
+                         rgb(0.3984375, 0.7578125, 0.6445312, max(.6, trans)),
+                         rgb(0, 0, 0, trans))))
+
+    if(set) trellis.par.set(theme, warn = FALSE)
+    invisible(theme)
+  }
diff --git a/data/AlzheimerDisease.RData b/data/AlzheimerDisease.RData
diff --git a/data/ChemicalManufacturingProcess.RData b/data/ChemicalManufacturingProcess.RData
diff --git a/data/FuelEconomy.RData b/data/FuelEconomy.RData
diff --git a/data/abalone.RData b/data/abalone.RData
diff --git a/data/concrete.RData b/data/concrete.RData
diff --git a/data/datalist b/data/datalist
@@ -0,0 +1,12 @@
+AlzheimerDisease: diagnosis predictors
+ChemicalManufacturingProcess
+FuelEconomy: cars2010 cars2011 cars2012
+abalone
+concrete: concrete mixtures
+hepatic: bio chem injury
+logisticCreditPredictions
+permeability: fingerprints permeability
+schedulingData
+segmentationOriginal
+solubility: solTestX solTestXtrans solTestY solTrainX solTrainXtrans solTrainY
+twoClassData: classes predictors
diff --git a/data/hepatic.RData b/data/hepatic.RData
diff --git a/data/logisticCreditPredictions.RData b/data/logisticCreditPredictions.RData
diff --git a/data/permeability.RData b/data/permeability.RData
diff --git a/data/schedulingData.RData b/data/schedulingData.RData
diff --git a/data/segmentationOriginal.RData b/data/segmentationOriginal.RData
diff --git a/data/solubility.RData b/data/solubility.RData
diff --git a/data/twoClassData.RData b/data/twoClassData.RData
diff --git a/inst/NEWS.Rd b/inst/NEWS.Rd
@@ -0,0 +1,9 @@
+\name{NEWS}
+\title{News for Package \pkg{AppliedPredictiveModeling}}
+\newcommand{\cpkg}{\href{http://CRAN.R-project.org/package=#1}{\pkg{#1}}}
+
+\section{Changes in version 1.1-1}{
+\itemize{
+\item Initial Version
+}}
+
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,2 @@
		scriptLocation <- function() system.file("chapters", package = "AppliedPredictiveModeling")