[Robast-commits] r1259 - in branches/robast-1.3/pkg: ROptEst ROptEst/inst ROptEst/inst/scripts ROptEstOld ROptEstOld/R ROptEstOld/inst ROptEstOld/man ROptRegTS ROptRegTS/inst RandVar/inst RobAStBase RobAStBase/inst RobAStRDA RobAStRDA/inst RobExtremes RobExtremes/R RobExtremes/inst RobExtremes/inst/AddMaterial/interpolation RobExtremes/man RobLox RobLox/inst RobLoxBioC RobLoxBioC/inst RobRex RobRex/inst
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon May 8 16:02:58 CEST 2023
Author: ruckdeschel
Date: 2023-05-08 16:02:58 +0200 (Mon, 08 May 2023)
New Revision: 1259
Modified:
branches/robast-1.3/pkg/ROptEst/DESCRIPTION
branches/robast-1.3/pkg/ROptEst/inst/CITATION
branches/robast-1.3/pkg/ROptEst/inst/NEWS
branches/robast-1.3/pkg/ROptEst/inst/scripts/BinomialModel.R
branches/robast-1.3/pkg/ROptEst/inst/scripts/ExponentialScaleModel.R
branches/robast-1.3/pkg/ROptEst/inst/scripts/NbinomModel.R
branches/robast-1.3/pkg/ROptEstOld/DESCRIPTION
branches/robast-1.3/pkg/ROptEstOld/R/Gumbel.R
branches/robast-1.3/pkg/ROptEstOld/inst/NEWS
branches/robast-1.3/pkg/ROptEstOld/man/ROptEstOldConstants.Rd
branches/robast-1.3/pkg/ROptRegTS/DESCRIPTION
branches/robast-1.3/pkg/ROptRegTS/inst/CITATION
branches/robast-1.3/pkg/ROptRegTS/inst/NEWS
branches/robast-1.3/pkg/RandVar/inst/NEWS
branches/robast-1.3/pkg/RobAStBase/DESCRIPTION
branches/robast-1.3/pkg/RobAStBase/inst/CITATION
branches/robast-1.3/pkg/RobAStBase/inst/NEWS
branches/robast-1.3/pkg/RobAStRDA/DESCRIPTION
branches/robast-1.3/pkg/RobAStRDA/inst/CITATION
branches/robast-1.3/pkg/RobAStRDA/inst/NEWS
branches/robast-1.3/pkg/RobExtremes/DESCRIPTION
branches/robast-1.3/pkg/RobExtremes/R/Gumbel.R
branches/robast-1.3/pkg/RobExtremes/inst/AddMaterial/interpolation/getLMInterpol.R
branches/robast-1.3/pkg/RobExtremes/inst/CITATION
branches/robast-1.3/pkg/RobExtremes/inst/NEWS
branches/robast-1.3/pkg/RobExtremes/man/0RobExtremes-package.Rd
branches/robast-1.3/pkg/RobExtremes/man/RobExtremesConstants.Rd
branches/robast-1.3/pkg/RobLox/DESCRIPTION
branches/robast-1.3/pkg/RobLox/inst/CITATION
branches/robast-1.3/pkg/RobLox/inst/NEWS
branches/robast-1.3/pkg/RobLoxBioC/DESCRIPTION
branches/robast-1.3/pkg/RobLoxBioC/inst/CITATION
branches/robast-1.3/pkg/RobLoxBioC/inst/NEWS
branches/robast-1.3/pkg/RobRex/DESCRIPTION
branches/robast-1.3/pkg/RobRex/inst/CITATION
branches/robast-1.3/pkg/RobRex/inst/NEWS
Log:
[RobASt-devel branch 1.3] updated CITATION files to new format and NEWS and converted to UTF-8
Modified: branches/robast-1.3/pkg/ROptEst/DESCRIPTION
===================================================================
--- branches/robast-1.3/pkg/ROptEst/DESCRIPTION 2023-05-08 06:24:30 UTC (rev 1258)
+++ branches/robast-1.3/pkg/ROptEst/DESCRIPTION 2023-05-08 14:02:58 UTC (rev 1259)
@@ -15,8 +15,8 @@
"Ruckdeschel", role=c("aut", "cph")))
ByteCompile: yes
License: LGPL-3
-URL: http://robast.r-forge.r-project.org/
-Encoding: latin1
+URL: https://r-forge.r-project.org/projects/robast/
+Encoding: UTF-8
LastChangedDate: {$LastChangedDate$}
LastChangedRevision: {$LastChangedRevision$}
VCS/SVNRevision: 1213
Modified: branches/robast-1.3/pkg/ROptEst/inst/CITATION
===================================================================
--- branches/robast-1.3/pkg/ROptEst/inst/CITATION 2023-05-08 06:24:30 UTC (rev 1258)
+++ branches/robast-1.3/pkg/ROptEst/inst/CITATION 2023-05-08 14:02:58 UTC (rev 1259)
@@ -2,19 +2,19 @@
year <- sub("-.*", "", meta$Date)
note <- sprintf("R package version %s", meta$Version)
-citHeader("To cite package ROptEst in publications use:")
-
-citEntry(entry="Manual",
+bibentry(
+ bibtype = "Manual",
+ mheader = "To cite package ROptEst in publications use:",
title = "ROptEst: Optimally robust estimation",
- author = personList(as.person("M. Kohl"),
- as.person("P. Ruckdeschel")),
+ author = c(as.person("M. Kohl"),
+ as.person("P. Ruckdeschel")),
language = "English",
year = year,
note = note,
type = "R package",
- url = "http://robast.r-forge.r-project.org/",
+ url = "https://r-forge.r-project.org/projects/robast/",
textVersion = paste("Kohl, M., and Ruckdeschel, P.",
sprintf("(%s).", year),
"ROptEst: Optimally robust estimation.",
paste(note, ".", sep = ""),
- "URL http://robast.r-forge.r-project.org/"))
+ "URL https://r-forge.r-project.org/projects/robast/"))
Modified: branches/robast-1.3/pkg/ROptEst/inst/NEWS
===================================================================
--- branches/robast-1.3/pkg/ROptEst/inst/NEWS 2023-05-08 06:24:30 UTC (rev 1258)
+++ branches/robast-1.3/pkg/ROptEst/inst/NEWS 2023-05-08 14:02:58 UTC (rev 1259)
@@ -17,6 +17,7 @@
+ fixed some broken URLs and changed URLs from http to https where possible
+ triggered by new NOTES uncovered by R CMD check, we deleted duplicate entries for items
in internal-interpolate.Rd
++ changed encoding to UTF-8 and updated URL for r-forge project homepage
#######################################
version 1.2.1
Modified: branches/robast-1.3/pkg/ROptEst/inst/scripts/BinomialModel.R
===================================================================
--- branches/robast-1.3/pkg/ROptEst/inst/scripts/BinomialModel.R 2023-05-08 06:24:30 UTC (rev 1258)
+++ branches/robast-1.3/pkg/ROptEst/inst/scripts/BinomialModel.R 2023-05-08 14:02:58 UTC (rev 1259)
@@ -49,11 +49,11 @@
IC0 <- optIC(model = B, risk = asCov())
IC0 # show IC
-#An object of class “IC”
+#An object of class ''IC''
#### name: Classical optimal influence curve for Binomial family
#### L2-differentiable parametric family: Binomial family
#
-#### 'Curve': An object of class “EuclRandVarList”
+#### 'Curve': An object of class ''EuclRandVarList''
#Domain: Real Space with dimension 1
#[[1]]
#length of Map: 1
@@ -101,7 +101,7 @@
RobB1 <- InfRobModel(center = B, neighbor = ContNeighborhood(radius = 0.5))
RobB1 # show RobB1
-#An object of class “InfRobModel”
+#An object of class ''InfRobModel''
####### center: An object of class "BinomFamily"
#### name: Binomial family
#
@@ -122,13 +122,13 @@
#[1] "The Binomial family is symmetric with respect to prob = 0.5;"
#[2] "i.e., d(Binom(size, prob))(k)=d(Binom(size,1-prob))(size-k)"
#
-####### neighborhood: An object of class “ContNeighborhood”
+####### neighborhood: An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
(RobB2 <- InfRobModel(center = B, neighbor = TotalVarNeighborhood(radius = 0.5)))
-#An object of class “InfRobModel”
+#An object of class ''InfRobModel''
####### center: An object of class "BinomFamily"
#### name: Binomial family
#
@@ -149,7 +149,7 @@
#[1] "The Binomial family is symmetric with respect to prob = 0.5;"
#[2] "i.e., d(Binom(size, prob))(k)=d(Binom(size,1-prob))(size-k)"
#
-####### neighborhood: An object of class “TotalVarNeighborhood”
+####### neighborhood: An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -171,7 +171,7 @@
IC1
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -194,7 +194,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
checkIC(IC1)
@@ -216,13 +216,13 @@
#[1] 0.1213661
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -248,7 +248,7 @@
#prob 0.008544305
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -272,7 +272,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -387,13 +387,13 @@
#[1] 0.1213661
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -441,7 +441,7 @@
#prob 0.008544305
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -465,7 +465,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
#
@@ -505,7 +505,7 @@
IC2
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -528,7 +528,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
checkIC(IC2)
@@ -550,13 +550,13 @@
#[1] 0.2239858
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -582,7 +582,7 @@
#prob 0.03342380
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -606,7 +606,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “TotalVarNeighborhood”
+#An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
#
@@ -671,13 +671,13 @@
#[1] 0.2239858
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -703,7 +703,7 @@
#prob 0.03342380
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -727,7 +727,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “TotalVarNeighborhood”
+#An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -738,7 +738,7 @@
#-------------------------------------------------------------------------------
(IC3 <- optIC(model=RobB1, risk=asBias()))
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -780,13 +780,13 @@
#[1] 0.1098910
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -814,7 +814,7 @@
#[1] 0.01011106
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -837,7 +837,7 @@
#[1] 0.25
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -845,7 +845,7 @@
(IC4 <- optIC(model=RobB2, risk=asBias()))
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -886,13 +886,13 @@
#[1] 0.2159161
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -920,7 +920,7 @@
#[1] 0.01148091
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -943,7 +943,7 @@
#[1] 0.25
#
#$asMSE$at
-#An object of class “TotalVarNeighborhood”
+#An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -956,7 +956,7 @@
(IC5 <- optIC(model=RobB1, risk=asHampel(bound=clip(IC1))))
#minimal bound: 0.1098910
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -1001,13 +1001,13 @@
#[1] 0.1213661
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1033,7 +1033,7 @@
#prob 0.008544296
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1057,7 +1057,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -1067,7 +1067,7 @@
(IC6 <- optIC(model=RobB2, risk=asHampel(bound=Risks(IC2)$asBias$value), maxiter = 200))
#minimal bound: 0.2159161
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -1112,13 +1112,13 @@
#[1] 0.2239858
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1144,7 +1144,7 @@
#prob 0.03342372
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1168,7 +1168,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “TotalVarNeighborhood”
+#An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -1185,7 +1185,7 @@
IC7
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -1210,7 +1210,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, 1]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.391"
-#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.103"
+#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.103"
checkIC(IC7)
@@ -1232,13 +1232,13 @@
#[1] 0.1269559
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1264,7 +1264,7 @@
#prob 0.008272273
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1288,7 +1288,7 @@
#[1] 0.3913008
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.3913008
@@ -1301,7 +1301,7 @@
IC8
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -1326,7 +1326,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, 1]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.266"
-#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.146"
+#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.146"
checkIC(IC8)
@@ -1348,13 +1348,13 @@
#[1] 0.2406692
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1380,7 +1380,7 @@
#prob 0.01546324
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1404,7 +1404,7 @@
#[1] 0.2661058
#
#$asMSE$at
-#An object of class “TotalVarNeighborhood”
+#An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.2661058
@@ -1491,7 +1491,7 @@
#Evaluations of Minimum Kolmogorov distance estimate:
#----------------------------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# MDEstimator(x = x, ParamFamily = BinomFamily(size = 25))
#samplesize: 100
@@ -1525,7 +1525,7 @@
#Evaluations of 1-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# oneStepEstimator(x = x, IC = IC9, start = est0)
#samplesize: 100
@@ -1548,7 +1548,7 @@
#asymptotic bias:
#[1] 0.06058743
#(partial) influence curve:
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -1571,7 +1571,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
#steps:
#[1] 1
@@ -1794,7 +1794,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# kStepEstimator(x = x, IC = IC9, start = est0, steps = 3L)
#samplesize: 100
@@ -1817,7 +1817,7 @@
#asymptotic bias:
#[1] 0.05992703
#(partial) influence curve:
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -1840,7 +1840,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
#steps:
#[1] 3
@@ -1946,7 +1946,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# kStepEstimator(x = x, IC = IC10, start = est0, steps = 3L)
#samplesize: 100
@@ -1969,7 +1969,7 @@
#asymptotic bias:
#[1] 0.05982794
#(partial) influence curve:
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -1992,7 +1992,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
#steps:
#[1] 3
@@ -2114,7 +2114,7 @@
#Evaluations of 1-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# oneStepEstimator(x = x, IC = IC11, start = est0)
#samplesize: 100
@@ -2137,7 +2137,7 @@
#asymptotic bias:
#[1] 0.07121993
#(partial) influence curve:
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -2162,7 +2162,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, Inf]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.601"
-#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.165"
+#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.165"
#steps:
#[1] 1
@@ -2184,7 +2184,7 @@
(est3c1 <- roptest(x, BinomFamily(size = 25), eps.upper = 0.5))
#Evaluations of 1-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# roptest(x = x, L2Fam = BinomFamily(size = 25), eps.upper = 0.5)
#samplesize: 100
@@ -2204,7 +2204,7 @@
#asymptotic bias:
#[1] 0.07044238
#(partial) influence curve:
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -2229,7 +2229,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, 5]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.597"
-#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.159"
+#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.159"
#steps:
#[1] 1
@@ -2246,7 +2246,7 @@
#Evaluations of 1-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# roptest(x = x, L2Fam = BinomFamily(size = 25), eps.upper = 0.5,
# neighbor = TotalVarNeighborhood())
@@ -2267,7 +2267,7 @@
#asymptotic bias:
#[1] 0.07270451
#(partial) influence curve:
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -2292,7 +2292,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, 5]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.31"
-#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.166"
+#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.166"
#steps:
#[1] 1
@@ -2383,7 +2383,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# kStepEstimator(x = x, IC = IC11, start = est0, steps = 3L)
#samplesize: 100
@@ -2406,7 +2406,7 @@
#asymptotic bias:
#[1] 0.07071585
#(partial) influence curve:
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -2429,7 +2429,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
#steps:
#[1] 3
@@ -2447,7 +2447,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# kStepEstimator(x = x, IC = IC12, start = est0, steps = 3L)
#samplesize: 100
@@ -2470,7 +2470,7 @@
#asymptotic bias:
#[1] 0.07293007
#(partial) influence curve:
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -2493,7 +2493,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
#steps:
#[1] 3
@@ -2510,7 +2510,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# roptest(x = x, L2Fam = BinomFamily(size = 25), eps.upper = 0.5,
# steps = 3L)
@@ -2531,7 +2531,7 @@
#asymptotic bias:
#[1] 0.07030448
#(partial) influence curve:
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -2554,7 +2554,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
#steps:
#[1] 3
@@ -2571,7 +2571,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# roptest(x = x, L2Fam = BinomFamily(size = 25), eps.upper = 0.5,
# neighbor = TotalVarNeighborhood(), steps = 3L)
@@ -2592,7 +2592,7 @@
#asymptotic bias:
#[1] 0.07266429
#(partial) influence curve:
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -2615,7 +2615,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
#steps:
#[1] 3
Modified: branches/robast-1.3/pkg/ROptEst/inst/scripts/ExponentialScaleModel.R
===================================================================
--- branches/robast-1.3/pkg/ROptEst/inst/scripts/ExponentialScaleModel.R 2023-05-08 06:24:30 UTC (rev 1258)
+++ branches/robast-1.3/pkg/ROptEst/inst/scripts/ExponentialScaleModel.R 2023-05-08 14:02:58 UTC (rev 1259)
@@ -38,11 +38,11 @@
## classical optimal IC
E1.IC0 <- optIC(model = E1, risk = asCov())
E1.IC0 # show IC
-#An object of class “IC”
+#An object of class ''IC''
#### name: Classical optimal influence curve for Exponential scale family
#### L2-differentiable parametric family: Exponential scale family
#
-#### 'Curve': An object of class “EuclRandVarList”
+#### 'Curve': An object of class ''EuclRandVarList''
#Domain: Real Space with dimension 1
#[[1]]
#length of Map: 1
@@ -70,7 +70,7 @@
## L_2 family + infinitesimal neighborhood
E1.Rob1 <- InfRobModel(center = E1, neighbor = ContNeighborhood(radius = 0.5))
E1.Rob1 # show E1.Rob1
-#An object of class “InfRobModel”
+#An object of class ''InfRobModel''
####### center: An object of class "ExpScaleFamily"
#### name: Exponential scale family
#
@@ -91,11 +91,11 @@
#[2] "the group of transformations 'g(y) = scale*y'"
#[3] "with scale parameter 'scale'"
#
-####### neighborhood: An object of class “ContNeighborhood”
+####### neighborhood: An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
(E1.Rob2 <- InfRobModel(center = E1, neighbor = TotalVarNeighborhood(radius = 0.5)))
-#An object of class “InfRobModel”
+#An object of class ''InfRobModel''
####### center: An object of class "ExpScaleFamily"
#### name: Exponential scale family
#
@@ -116,13 +116,13 @@
#[2] "the group of transformations 'g(y) = scale*y'"
#[3] "with scale parameter 'scale'"
#
-####### neighborhood: An object of class “TotalVarNeighborhood”
+####### neighborhood: An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
## MSE solution
(E1.IC1 <- optIC(model=E1.Rob1, risk=asMSE()))
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Exponential scale family
@@ -145,7 +145,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
checkIC(E1.IC1)
#precision of centering: -2.924022e-05
#precision of Fisher consistency:
@@ -163,13 +163,13 @@
#[1] 0.8449229
#
#$asBias$biastype
-#An object of class “symmetricBias”
+#An object of class ''symmetricBias''
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -195,7 +195,7 @@
#scale 0.3465008
#
#$trAsCov$normtype
-#An object of class “NormType”
+#An object of class ''NormType''
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -219,7 +219,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
Modified: branches/robast-1.3/pkg/ROptEst/inst/scripts/NbinomModel.R
===================================================================
--- branches/robast-1.3/pkg/ROptEst/inst/scripts/NbinomModel.R 2023-05-08 06:24:30 UTC (rev 1258)
+++ branches/robast-1.3/pkg/ROptEst/inst/scripts/NbinomModel.R 2023-05-08 14:02:58 UTC (rev 1259)
@@ -48,11 +48,11 @@
IC0 <- optIC(model = N, risk = asCov())
IC0 # show IC
-#An object of class “IC”
+#An object of class ''IC''
#### name: Classical optimal influence curve for Negative Binomial family
#### L2-differentiable parametric family: Negative Binomial family
#
-#### 'Curve': An object of class “EuclRandVarList”
+#### 'Curve': An object of class ''EuclRandVarList''
#Domain: Real Space with dimension 1
#[[1]]
#length of Map: 1
@@ -100,7 +100,7 @@
RobN1 <- InfRobModel(center = N, neighbor = ContNeighborhood(radius = 0.5))
RobN1 # show RobN1
-#An object of class “InfRobModel”
+#An object of class ''InfRobModel''
####### center: An object of class "NbinomFamily"
#### name: Negative Binomial family
#
@@ -120,13 +120,13 @@
#### props:
#[1] ""
#
-####### neighborhood: An object of class “ContNeighborhood”
+####### neighborhood: An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
(RobN2 <- InfRobModel(center = N, neighbor = TotalVarNeighborhood(radius = 0.5)))
-#An object of class “InfRobModel”
+#An object of class ''InfRobModel''
####### center: An object of class "NbinomFamily"
#### name: Negative Binomial family
#
@@ -146,7 +146,7 @@
#### props:
#[1] ""
#
-####### neighborhood: An object of class “TotalVarNeighborhood”
+####### neighborhood: An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -168,7 +168,7 @@
IC1
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -191,7 +191,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
checkIC(IC1)
@@ -269,7 +269,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -433,7 +433,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
#
@@ -460,7 +460,7 @@
IC2
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -483,7 +483,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for ‘asMSE’"
+#[1,] "optIC" "optimally robust IC for 'asMSE'"
checkIC(IC2)
@@ -561,7 +561,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “TotalVarNeighborhood”
+#An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
#
@@ -684,7 +684,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “TotalVarNeighborhood”
+#An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -697,7 +697,7 @@
(IC3 <- optIC(model=RobN1, risk=asBias()))
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -797,7 +797,7 @@
#[1] 0.25
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -806,7 +806,7 @@
(IC4 <- optIC(model=RobN2, risk=asBias()))
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -904,7 +904,7 @@
#[1] 0.25
#
#$asMSE$at
-#An object of class “TotalVarNeighborhood”
+#An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -918,7 +918,7 @@
(IC5 <- optIC(model=RobN1, risk=asHampel(bound=clip(IC1))))
#minimal bound: 0.05458835
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -1020,7 +1020,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -1033,7 +1033,7 @@
#minimal bound: 0.1089328
#maximum iterations reached!
# achieved precision: 5.583403e-07
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -1136,7 +1136,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class “TotalVarNeighborhood”
+#An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -1155,7 +1155,7 @@
IC7
-#An object of class “ContIC”
+#An object of class ''ContIC''
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -1180,7 +1180,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0.01, 3.9]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.581"
-#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.177"
+#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.177"
checkIC(IC7)
@@ -1258,7 +1258,7 @@
#[1] 0.5814856
#
#$asMSE$at
-#An object of class “ContNeighborhood”
+#An object of class ''ContNeighborhood''
#type: (uncond.) convex contamination neighborhood
#radius: 0.5814856
@@ -1272,7 +1272,7 @@
IC8
-#An object of class “TotalVarIC”
+#An object of class ''TotalVarIC''
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -1297,7 +1297,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0.01, 1.8]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.294"
-#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.168"
+#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.168"
checkIC(IC8)
@@ -1377,7 +1377,7 @@
#[1] 0.2944932
#
#$asMSE$at
-#An object of class “TotalVarNeighborhood”
+#An object of class ''TotalVarNeighborhood''
#type: (uncond.) total variation neighborhood
#radius: 0.2944932
@@ -1469,7 +1469,7 @@
#Evaluations of Maximum likelihood estimate:
#-------------------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# MLEstimator(x = x, ParamFamily = NbinomFamily(size = 25))
#samplesize: 100
@@ -1492,7 +1492,7 @@
#Evaluations of Minimum Kolmogorov distance estimate:
#----------------------------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# MDEstimator(x = x, ParamFamily = NbinomFamily(size = 25))
#samplesize: 100
@@ -1528,7 +1528,7 @@
#Evaluations of 1-step estimate:
#-------------------------------
-#An object of class “Estimate”
+#An object of class ''Estimate''
#generated by call
# oneStepEstimator(x = x, IC = IC9, start = est0)
#samplesize: 100
@@ -1551,7 +1551,7 @@
#asymptotic bias:
#[1] 0.03064529
#(partial) influence curve:
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/robast -r 1259
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