Abstract
A software package for fitting and assessing multi-dimensional point process models using the R statistical computing environment is described. Methods of residual analysis based on random thinning are discussed and implemented. Features of the software are demonstrated using data on wildfire occurrences in Los Angeles County, California and earthquake occurrences in Northern California.
Original language | English (US) |
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Pages (from-to) | 1-27 |
Number of pages | 27 |
Journal | Journal of Statistical Software |
Volume | 8 |
State | Published - 2003 |
Externally published | Yes |
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ASJC Scopus subject areas
- Software
- Statistics and Probability
Cite this
Multi-dimensional point process models in R. / Peng, Roger.
In: Journal of Statistical Software, Vol. 8, 2003, p. 1-27.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Multi-dimensional point process models in R
AU - Peng, Roger
PY - 2003
Y1 - 2003
N2 - A software package for fitting and assessing multi-dimensional point process models using the R statistical computing environment is described. Methods of residual analysis based on random thinning are discussed and implemented. Features of the software are demonstrated using data on wildfire occurrences in Los Angeles County, California and earthquake occurrences in Northern California.
AB - A software package for fitting and assessing multi-dimensional point process models using the R statistical computing environment is described. Methods of residual analysis based on random thinning are discussed and implemented. Features of the software are demonstrated using data on wildfire occurrences in Los Angeles County, California and earthquake occurrences in Northern California.
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UR - http://www.scopus.com/inward/citedby.url?scp=4544297737&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:4544297737
VL - 8
SP - 1
EP - 27
JO - Journal of Statistical Software
JF - Journal of Statistical Software
SN - 1548-7660
ER -