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The np PackageFebruary 16, 2008Vers
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Italy 3Examplesdata("cps71")attach(
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wage1 5Examplesdata("oecdpanel")att
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gradients 7## S3 method for class '
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np 9A variety of bandwidth methods
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npcmstest 11npcmstestKernel Consist
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npcmstest 13ReferencesAitchison, J.
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npcdens 15npcdensKernel Conditional
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npcdens 17Valuenpcdens returns a co
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npcdens 19# Gaussian kernel (defaul
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npcdens 21# (1993) (see their descr
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npcdensbw 23fit
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npcdensbw 25na.actionxdatydatbwspro
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npcdensbw 27data. The approach is b
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npconmode 29# depending on the spee
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npconmode 31tydatexdateydata one (1
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npconmode 33lwt,family=binomial(lin
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npudens 35npudensKernel Density and
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npudens 37Author(s)Tristen Hayfield
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npudens 39# EXAMPLE 1 (INTERFACE=DA
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npudensbw 41library("datasets")data
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npudensbw 43bwsa bandwidth specific
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npudensbw 45fvalobjective function
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npudensbw 47# previous examples.bw
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npudensbw 49# previous examples.bw
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npksum 51Argumentsformuladatanewdat
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npksum 53Usage IssuesIf you are usi
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npksum 55# the bandwidth object its
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npksum 57ss
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npplot 59plot.behavior = c("plot","
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npplot 61xtrim = 0.0,neval = 50,com
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npplot 63xdatydatzdatxqyqzqxtrimytr
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npplot 65DetailsValuenpplot is a ge
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- Page 69 and 70: npplot 69# npplot(). When npplot()
- Page 71 and 72: npplreg 71## S3 method for class 'c
- Page 73 and 74: npplreg 73residR2MSEMAEMAPECORRSIGN
- Page 75 and 76: npplreg 75# Plot the regression sur
- Page 77 and 78: npplregbw 77and dependent data), an
- Page 79 and 80: npplregbw 79Detailsnpplregbw implem
- Page 81 and 82: npplregbw 81x2
- Page 83 and 84: npqcmstest 83npqcmstestKernel Consi
- Page 85 and 86: npqcmstest 85Author(s)Tristen Hayfi
- Page 87 and 88: npqreg 87ftol = 1.19209e-07,tol = 1
- Page 89 and 90: npqreg 89Li, Q. and J.S. Racine (20
- Page 91 and 92: npreg 91Usagenpreg(bws, ...)## S3 m
- Page 93 and 94: npreg 93residR2MSEMAEMAPECORRSIGNif
- Page 95 and 96: npreg 95summary(model)# Use npplot(
- Page 97 and 98: npreg 97# - this may take a few min
- Page 99 and 100: npreg 99# then a noisy samplen
- Page 101 and 102: npregbw 101## S3 method for class '
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- Page 105 and 106: npregbw 105ReferencesAitchison, J.
- Page 107 and 108: npsigtest 107bw
- Page 109 and 110: npsigtest 109Author(s)Tristen Hayfi
- Page 111 and 112: npindex 111Usagenpindex(bws, ...)##
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- Page 117: npindex 117# x1 is chi-squared havi
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- Page 123 and 124: npindexbw 123allows one to deploy t
- Page 125 and 126: npscoef 125x1
- Page 127 and 128: npscoef 127Valueeydatezdaterrorsres
- Page 129 and 130: npscoef 129# We could manually plot
- Page 131 and 132: npscoefbw 131optim.abstol,optim.max
- Page 133 and 134: npscoefbw 133optim.maxattemptsmaxim
- Page 135 and 136: npscoefbw 135ReferencesAitchison, J
- Page 137 and 138: uocquantile 137uocquantileCompute Q
- Page 139: INDEX 139npconmode, 29npindex, 110n