(geography/R-spatstat.linnet) import R-spatstat.linnet-2.3.0

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.

3 files changed, 54 insertions, 0 deletions

diff --git a/geography/R-spatstat.linnet/DESCR b/geography/R-spatstat.linnet/DESCR
new file mode 100644
index 00000000000..fb67c8a02e5
--- /dev/null
+++ b/geography/R-spatstat.linnet/DESCR
@@ -0,0 +1,29 @@
+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.
diff --git a/geography/R-spatstat.linnet/Makefile b/geography/R-spatstat.linnet/Makefile
new file mode 100644
index 00000000000..7483abef8e9
--- /dev/null
+++ b/geography/R-spatstat.linnet/Makefile
@@ -0,0 +1,19 @@
+# $NetBSD: Makefile,v 1.1 2021/09/20 10:45:32 mef Exp $
+
+R_PKGNAME=	spatstat.linnet
+R_PKGVER=	2.3-0
+CATEGORIES=	geography
+
+MAINTAINER=	pkgsrc-users@NetBSD.org
+COMMENT=	Linear Networks Functionality of the 'spatstat' Family
+LICENSE=	gnu-gpl-v2 OR gnu-gpl-v3
+
+DEPENDS+=	R-spatstat.core>=2.3.0:../../geography/R-spatstat.core
+DEPENDS+=	R-spatstat.data>=2.1.0:../../geography/R-spatstat.data
+DEPENDS+=	R-spatstat.geom>=2.2.2:../../geography/R-spatstat.geom
+DEPENDS+=	R-spatstat.utils>=2.2.0:../../geography/R-spatstat.utils
+
+# Packages suggested but not available: 'locfit', 'spatstat'
+
+.include "../../math/R/Makefile.extension"
+.include "../../mk/bsd.pkg.mk"
diff --git a/geography/R-spatstat.linnet/distinfo b/geography/R-spatstat.linnet/distinfo
new file mode 100644
index 00000000000..08ebfa98708
--- /dev/null
+++ b/geography/R-spatstat.linnet/distinfo
@@ -0,0 +1,6 @@
+$NetBSD: distinfo,v 1.1 2021/09/20 10:45:32 mef Exp $
+
+SHA1 (R/spatstat.linnet_2.3-0.tar.gz) = 5fe09ad83d9f8a457655cab64bdb5bbec82905b2
+RMD160 (R/spatstat.linnet_2.3-0.tar.gz) = 38d7aea409d2da1f9d56b2421f3502f41c9b358d
+SHA512 (R/spatstat.linnet_2.3-0.tar.gz) = d5b3d1d393b10e06f2d5adfba264a15d72cc24a044a088fc3c109881964b85657e985fcbf707d8edbfc60cbdb1f93debf5ee17bc233a17dd9c8fc7f4e5303e0f
+Size (R/spatstat.linnet_2.3-0.tar.gz) = 245724 bytes