软件包：r-cran-spatstat.linnet（3.1-5-1）
r-cran-spatstat.linnet 的相关链接
Debian 的资源：
下载源码包 r-cran-spatstat.linnet：
- [r-cran-spatstat.linnet_3.1-5-1.dsc]
- [r-cran-spatstat.linnet_3.1-5.orig.tar.gz]
- [r-cran-spatstat.linnet_3.1-5-1.debian.tar.xz]
维护小组：
外部的资源：
- 主页 [cran.r-project.org]
相似软件包：
linear networks functionality of the 'spatstat' family of GNU R
Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K- functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. 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 function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. 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. Random point patterns on a network can be generated using a variety of models.
其他与 r-cran-spatstat.linnet 有关的软件包
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- dep: libc6 (>= 2.27)
- GNU C 语言运行库：共享库
同时作为一个虚包由这些包填实： libc6-udeb
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- dep: r-api-4.0
- 本虚包由这些包填实： r-base-core
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- dep: r-cran-matrix
- GNU R package of classes for dense and sparse matrices
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- dep: r-cran-spatstat.data (>= 3.0-4)
- datasets for the package r-cran-spatstat
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- dep: r-cran-spatstat.explore (>= 3.2-7)
- GNU R exploratory data analysis for the 'spatstat' family
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- dep: r-cran-spatstat.geom (>= 3.2-9)
- GNU R geometrical functionality of the 'spatstat' package
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- dep: r-cran-spatstat.model (>= 3.2-11)
- GNU R parametric statistical modelling for the 'spatstat' family
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- dep: r-cran-spatstat.random (>= 3.2-3)
- Random Generation Functionality for the 'spatstat' Family
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- dep: r-cran-spatstat.sparse (>= 3.0-3)
- GNU R sparse three-dimensional arrays and linear algebra utilities
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- dep: r-cran-spatstat.utils (>= 3.0-4)
- GNU R utility functions for r-cran-spatstat
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- rec: r-cran-goftest
- GNU R Classical Goodness-of-Fit Tests for Univariate Distributions
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- rec: r-cran-locfit
- GNU R local regression, likelihood and density estimation
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- rec: r-cran-spatstat (>= 3.0)
- GNU R Spatial Point Pattern analysis, model-fitting, simulation, tests