Showing 9 of total 9 results (show query)
spatstat
spatstat.linnet:Linear Networks Functionality of the 'spatstat' Family
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.
Maintained by Adrian Baddeley. Last updated 2 months ago.
density-estimationheat-equationkernel-density-estimationnetwork-analysispoint-processesspatial-data-analysisstatistical-analysisstatistical-inferencestatistical-models
142.8 match 6 stars 9.64 score 35 scripts 43 dependentsspatstat
spatstat.geom:Geometrical Functionality of the 'spatstat' Family
Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)
Maintained by Adrian Baddeley. Last updated 2 days ago.
classes-and-objectsdistance-calculationgeometrygeometry-processingimagesmensurationplottingpoint-patternsspatial-dataspatial-data-analysis
7.5 match 7 stars 12.11 score 241 scripts 227 dependents
kisungyou
Rdimtools:Dimension Reduction and Estimation Methods
We provide linear and nonlinear dimension reduction techniques. Intrinsic dimension estimation methods for exploratory analysis are also provided. For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.
Maintained by Kisung You. Last updated 2 years ago.
dimension-estimationdimension-reductionmanifold-learningsubspace-learningopenblascppopenmp
3.0 match 52 stars 8.37 score 186 scripts 8 dependents
insightsengineering
formatters:ASCII Formatting for Values and Tables
We provide a framework for rendering complex tables to ASCII, and a set of formatters for transforming values or sets of values into ASCII-ready display strings.
Maintained by Joe Zhu. Last updated 2 months ago.
1.6 match 17 stars 10.19 score 22 scripts 20 dependentsinsightsengineering
rlistings:Clinical Trial Style Data Readout Listings
Listings are often part of the submission of clinical trial data in regulatory settings. We provide a framework for the specific formatting features often used when displaying large datasets in that context.
Maintained by Joe Zhu. Last updated 2 months ago.
1.3 match 28 stars 10.49 score 44 scripts 9 dependents
moradii
stlnpp:Spatio-Temporal Analysis of Point Patterns on Linear Networks
Statistical analysis of spatio-temporal point processes on linear networks. This packages provides tools to visualise and analyse spatio-temporal point patterns on linear networks using first- and second-order summary statistics.
Maintained by Mehdi Moradi. Last updated 2 months ago.
intensityk-functionlinear-networkpair-correlationpoint-pattern-analysisspatio-temporal-analysis
2.3 match 13 stars 4.77 score 1 dependents
baddstats
spatstat.gui:Interactive Graphics Functions for the 'spatstat' Package
Extension to the 'spatstat' package, containing interactive graphics capabilities.
Maintained by Adrian Baddeley. Last updated 8 months ago.
2.3 match 2.00 score 1 scripts
