annual aggregation, 147–52
areal data, 120–22
autocorrelation, definition, 110–11

Bayesian models, 301–39
Bayesian statistics
	computation, 85–91
	credible interval, 81–82
	definition, 7
	described, 76–97
	inferences, 80–81
	JAGS (Just Another Gibbs Sampler), 91–94
	landfall example, 76–80
	posterior distribution, 76–80
	predictive density, 82–85
	WinBUGS (Windows version of Bayesian inference Using Gibbs Sampling), 94–97
best-track data, 133–46
beta density, 77, 79
boundaries (maps), 114–15
box plot, 99–100
BUGS data, 91–94, 330–33, 336

categorical wind speed by county, 214–20
change points, 260–64
classical statistics, 33–75
climate, 5–6
cluster models, 275. See also feature clusters; spatial clusters; time clusters
coastal county winds, 152–55
CODA (COnvergence Diagnostic and Analysis), 93–94, 333
conditional scatter plots, 107–8
consensus model, 310–20
continuous time series, 264–68
coordinate reference systems (CRS), 123–26
correlation, 59–63
counts
	annual data, 146–47
	frequency models, 161–65
	hurricane landfalls, 76–80
	magnitudes, 154–55
	time series models, 261–63
CRAN site, 11–12
credible interval, 81–82
cyclone counts, 161–65

data, R language, 15–25
data frames, 24–27
data sets, 133–58
data types (maps), 116–23
dates and times, 111–13
density
	beta density, 77, 79
	continuous distributions, 42–44
	density plots, 102–4
	gamma density, 87–90
	predictive density, 82–85
	spatial, 288–90
descriptive statistics, 6, 33–38, 56–57
discrete time series, 254–60
distributions, 39–47

El Niño, definition, 6
exploratory analysis, 185–86, 194–200, 205, 341–42
exporting data (maps), 126–28
extreme losses, 341–44
extreme value theory, 207–8

fastest hurricane winds, 204–14
feature clusters, 292–300
field data, 122–23
fit and adequacy, 189–91
fit and interpretation, 187–88
forecasts, 75, 283–84
forecast skill, 174–77
frequency models
	bivariate relationships, 167
	counts, 161–65
	described, 161–93
	environmental variables, 165–67
	forecast skill, 174–77
	interpretation, 171–72
	logistic regression, 183–93
	machine learning, 181–83
	nonlinear regression structure, 177–78
	Poisson regression, 167–72, 177–78
	predictions, 172–73
	zero-inflated count models, 179–81
frequentist statistics (classical), 7
functions. See also packages
	beta density, 77, 79
	datasupport.R, 149–50
	glm function, 170–73
	interpolate.R, 139–40
	manipulating data, 16–17
	plot function, 106–7
	quartiles, 27–28
	R language, 13–14, 20–24, 31
	tables and summaries, 25–27
future wind damage, 344–51

Gaussian distribution. See normal distribution
generalized Pareto distribution (GPD), 208–14
Gibbs sampling, 90–91
graphs
	box plot, 99–100
	conditional scatter plots, 107–8
	density plots, 102–4
	described, 98–108
	histograms, 100–102
	Q-Q plots, 104–6
	scatter plots, 106–7
	time series models, 108–13
	time series network, 268–74

histograms, 100–102, 162–64
hurricanes, 3–5, 204–14, 258–60. See also counts; SST data

impact models, 340–51
importing data, 23–25, 135–37
inferential statistics, 6–7, 47
inhomogeneous Poisson process, 164–65, 287
intensification rates, 137–38
intensity models
	categorical wind speed by county, 214–20
	exploratory analysis, 194–200
	fastest hurricane winds, 204–14
	lifetime highest intensity, 194–204
	SST data, 230–31
interpolation, described, 138–40

JAGS (Just Another Gibbs Sampler), 91–94, 306–10

k-means clustering, 295–96
kriging, 248–50, 287

landfall, 4, 76–80
lattice data, 320–25
lattice package, 99, 128–29
lifetime highest intensity, 194–204
linear regression, 64–70, 168–69, 236–37
location-specific attributes, 145–46
logistic regression, 183–93
long-range outlook, 301–6

machine-learning algorithms, 181–83
manuscript review, 86–97
maps
	boundaries (maps), 114–16
	coordinate reference system (CRS), 123–26
	data types (maps), 116–23
	described, 114–23
	exporting data (maps), 126–28
	other packages, 128–32
	relative risk maps, 338–39
	spatial clusters, 288–90
	track hexagons, 226–27
MARS (multivariate adaptive regression splines), 177–78
matrices, 25–27
maximum intensity, 142–43
maximum likelihoods, 169–71
MCMC (Markov chain Monte Carlo), 90–97, 306–8, 331–36
Miami-Dade County, 219–20
missing values, 36–37
Moran's I, 231–34, 330
multiple linear regression, 70–75

NA. See missing values
names (vector elements), 19
NAO (North Atlantic Oscillation), 27, 147–51
netCDF files, 156–58
nonlinear regression structure, 177–78
normal distribution, 41–54

one-sample test, 47–52

packages. See also functions
	BMA, 313–20
	bootstrap, 303
	cluster package, 294–96
	coda package, 97
	colorRamps package, 118, 226–27
	described, 12
	gamlss package, 218, 261
	getTracks.R, 143–45, 344–46
	ggmap package, 132–33, 266
	ggplot2 package, 99, 129–32, 266
	igraph, 272–74
	ismev package, 210–13
	lattice package, 99, 128–29
	LearnBayes package, 82
	lubridate package, 113
	maps package, 114–15, 226–27, 288
	quantreg package, 201–4
	spatstat package, 285, 288, 292
	sp package, 221–23, 321
	Survival package, 155, 218
percentiles. See quantiles
plots, 28–32, 104–7, 298–300
point data, 116–20
Poisson distribution, 162–64
Poisson regression, 167–72, 177–78
posterior distribution, 76–80
predictions, 188–89
predictive density, 82–85
probability, 37–47

Q-Q plots, 104–6
quantiles, 27–28, 36, 45
quartile regression, 200–204

random numbers, 46–47
random samples, 38–40
receiver operating characteristic (ROC), 191–93
R language. See also functions; packages
	assignments, 14–15
	as calculator, 12–13
	data, 15–25
	data frames, 24–26
	dates and times, 111–13
	described, 8, 10–11
	distributions, 42
	handling data, 15–25
	help function, 15
	importing data, 23–25
	installing, 11–12
	JAGS (Just Another Gibbs Sampler), 91–94
	logic operators, 21–23
	named arguments, 21
	plots, 28–32
	quantiles, 27–28
	remove objects, 22–23
	structured data, 20–21
	summary statistics, 25–27, 35–36
	tables, 25–28
	vectors, 17–20
	warnings and errors, 14
	WinBUGS (Windows version of Bayesian inference Using Gibbs Sampling), 94–97

Saffir-Simpson scale, 5, 214–20
scatter plots, 106–7
seasonal models, 306–10
shapefiles, 120–21
space-time model
	Bayesian models, 320–39
	BUGS data, 330–31
	convergence and mixing, 333–36
	lattice data, 320–25
	local independent regressions, 325–28
	MCMC (Markov chain Monte Carlo) output, 331–36
	relative risk maps, 338–39
	spatial autocorrelation, 231–34, 329–30
	updates, 336–38
spatial clusters, 284–92
spatial interpolation, 243–52
spatial models
	autocorrelation, 231–34, 329–30
	interpretation, 243–52
	spatial regression models, 234–43
	SST data, 228–31
	track hexagons, 221–27
spreadsheets, 11, 23–24
SST data
	continuous time series, 264–68
	environmental variables, 147–52
	intensity changes, 347–51
	intensity models, 197–204
	spatial models, 228–31
standard deviation, definition, 19
statistics
	classical, described, 33–75
	correlation, 58–64
	defined, 6–8
	linear regression, 64–70
	multiple linear regression, 70–75
	two-sample Wilcoxon test, 57
	variances, 57–58
	Wilcoxon signed-rank test, 52–54
Student's t-distribution. See one-sample test

tables, 25–32
time clusters
	cluster detection, 276–77
	cluster models, 278–79
	conditional counts, 277–78
	forecasts, 283–84
	model diagnostics, 280–83
	parameter estimation, 279–80
time series models
	autocorrelation, 110–11
	change points, 260–64
	continuous time series, 264–68
	discrete time series, 254–60
	graphs, 108–13
	time-series network, 268–74
	time series overlays, 254
time series plots, 109–10
time-to-acceptance, 86–97
track clusters, 297–98
track hexagons, 221–27, 320–25
track plots, 298–300
two-sample test, 54–56
two-sample Wilcoxon test, 57

variance, 18, 35–36, 57–61, 73–75
vectors, 17–20, 25–27, 54, 82–85

Wilcoxon signed-rank test, 52–54
WinBUGS (Windows version of Bayesian inference Using Gibbs Sampling), 94–97
word processor, 19

zero-inflated count models, 179–81