forked from spatstat/spatstat
-
Notifications
You must be signed in to change notification settings - Fork 0
/
DESCRIPTION
224 lines (224 loc) · 6.3 KB
/
DESCRIPTION
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
Package: spatstat
Version: 1.58-2.018
Date: 2019-02-08
Title: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Author: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>,
Rolf Turner <r.turner@auckland.ac.nz>
and Ege Rubak <rubak@math.aau.dk>,
with substantial contributions of code by
Kasper Klitgaard Berthelsen;
Ottmar Cronie;
Yongtao Guan;
Ute Hahn;
Abdollah Jalilian;
Marie-Colette van Lieshout;
Greg McSwiggan;
Tuomas Rajala;
Suman Rakshit;
Dominic Schuhmacher;
Rasmus Waagepetersen;
and Hangsheng Wang.
Additional contributions
by M. Adepeju;
C. Anderson;
Q.W. Ang;
R. Arellano;
J. Astrom;
M. Austenfeld;
S. Azaele;
M. Baddeley;
C. Beale;
M. Bell;
R. Bernhardt;
T. Bendtsen;
A. Bevan;
B. Biggerstaff;
A. Bilgrau;
L. Bischof;
C. Biscio;
R. Bivand;
J.M. Blanco Moreno;
F. Bonneu;
J. Burgos;
S. Byers;
Y.M. Chang;
J.B. Chen;
I. Chernayavsky;
Y.C. Chin;
B. Christensen;
J.-F. Coeurjolly;
K. Colyvas;
H. Commenges;
R. Constantine;
R. Corria Ainslie;
R. Cotton;
M. de la Cruz;
P. Dalgaard;
M. D'Antuono;
S. Das;
T. Davies;
P.J. Diggle;
P. Donnelly;
I. Dryden;
S. Eglen;
A. El-Gabbas;
B. Fandohan;
O. Flores;
E.D. Ford;
P. Forbes;
S. Frank;
J. Franklin;
N. Funwi-Gabga;
O. Garcia;
A. Gault;
J. Geldmann;
M. Genton;
S. Ghalandarayeshi;
J. Gilbey;
J. Goldstick;
P. Grabarnik;
C. Graf;
U. Hahn;
A. Hardegen;
M.B. Hansen;
M. Hazelton;
J. Heikkinen;
M. Hering;
M. Herrmann;
M. Hesselbarth;
P. Hewson;
H. Heydarian;
K. Hingee;
K. Hornik;
P. Hunziker;
J. Hywood;
R. Ihaka;
C. Icos;
A. Jammalamadaka;
R. John-Chandran;
D. Johnson;
M. Khanmohammadi;
R. Klaver;
P. Kovesi;
L. Kozmian-Ledward;
M. Kuhn;
J. Laake;
R.A. Lamb;
F. Lavancier;
T. Lawrence;
T. Lazauskas;
J. Lee;
G.P. Leser;
A. Li;
H.T. Li;
G. Limitsios;
A. Lister;
B. Madin;
M. Maechler;
J. Marcus;
K. Marchikanti;
R. Mark;
J. Mateu;
P. McCullagh;
U. Mehlig;
F. Mestre;
S. Meyer;
X.C. Mi;
L. De Middeleer;
R.K. Milne;
E. Miranda;
J. Moller;
I. Moncada;
M. Moradi;
V. Morera Pujol;
E. Mudrak;
G.M. Nair;
N. Najari;
N. Nava;
L.S. Nielsen;
F. Nunes;
J.R. Nyengaard;
J. Oehlschlaegel;
T. Onkelinx;
S. O'Riordan;
E. Parilov;
J. Picka;
N. Picard;
M. Porter;
S. Protsiv;
A. Raftery;
S. Rakshit;
B. Ramage;
P. Ramon;
X. Raynaud;
N. Read;
M. Reiter;
I. Renner;
T.O. Richardson;
B.D. Ripley;
E. Rosenbaum;
B. Rowlingson;
J. Rudokas;
J. Rudge;
C. Ryan;
F. Safavimanesh;
A. Sarkka;
C. Schank;
K. Schladitz;
S. Schutte;
B.T. Scott;
O. Semboli;
F. Semecurbe;
V. Shcherbakov;
G.C. Shen;
P. Shi;
H.-J. Ship;
T.L. Silva;
I.-M. Sintorn;
Y. Song;
M. Spiess;
M. Stevenson;
K. Stucki;
M. Sumner;
P. Surovy;
B. Taylor;
T. Thorarinsdottir;
L. Torres;
B. Turlach;
T. Tvedebrink;
K. Ummer;
M. Uppala;
A. van Burgel;
T. Verbeke;
M. Vihtakari;
A. Villers;
F. Vinatier;
S. Voss;
S. Wagner;
H. Wang;
H. Wendrock;
J. Wild;
C. Witthoft;
S. Wong;
M. Woringer;
M.E. Zamboni
and
A. Zeileis.
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>
Depends: R (>= 3.3.0), spatstat.data (>= 1.4-0), stats, graphics, grDevices, utils, methods, nlme, rpart
Imports: spatstat.utils (>= 1.13-0), mgcv, Matrix, deldir (>= 0.0-21), abind, tensor, polyclip (>= 1.5-0), goftest
Suggests: sm, maptools, gsl, locfit, spatial, rpanel, tkrplot, RandomFields (>= 3.1.24.1), RandomFieldsUtils(>= 0.3.3.1), fftwtools (>= 0.9-8)
Remotes: spatstat/spatstat.utils, spatstat/spatstat.data
Description: Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images.
Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference.
Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks.
Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods.
A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
License: GPL (>= 2)
URL: http://www.spatstat.org
LazyData: true
NeedsCompilation: yes
ByteCompile: true
BugReports: https://github.com/spatstat/spatstat/issues