r-cran-spatstat 2.3-4-1 source package in Ubuntu
Changelog
r-cran-spatstat (2.3-4-1) unstable; urgency=medium * New upstream version * Standards-Version: 4.6.1 (routine-update) * dh-update-R to update Build-Depends (routine-update) -- Andreas Tille <email address hidden> Wed, 25 May 2022 13:04:29 +0200
Upload details
- Uploaded by:
- Debian R Packages Maintainers
- Uploaded to:
- Sid
- Original maintainer:
- Debian R Packages Maintainers
- Architectures:
- any
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| r-cran-spatstat_2.3-4-1.dsc | 2.2 KiB | d629ccacf11c34b23ff5e908b577dd6da3996451b45f8651b694040849449658 |
| r-cran-spatstat_2.3-4.orig.tar.gz | 3.4 MiB | 4ea0f8d70b926b92bf4a06521f985a0bb6d573619f5d526957c87860ccb999da |
| r-cran-spatstat_2.3-4-1.debian.tar.xz | 4.6 KiB | b73d779856cdbc88f023cc5abec982ccd960fcaf7277766f50a2723f64071905 |
Available diffs
- diff from 2.3-3-1 to 2.3-4-1 (8.3 KiB)
No changes file available.
Binary packages built by this source
- r-cran-spatstat: GNU R Spatial Point Pattern analysis, model-fitting, simulation, tests
A GNU R package for analysing spatial data, mainly Spatial Point Patterns,
including multitype/marked points and spatial covariates, in any
two-dimensional spatial region. Contains 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, and pixel images. Point process
models can be fitted to point pattern data. Cluster type models are fitted
by the method of minimum contrast. Very general Gibbs point process models
can be fitted to point pattern data using a function ppm similar to lm or glm.
Models may include dependence on covariates, interpoint interaction and
dependence on marks. Fitted models can be simulated automatically. Also
provides facilities for formal inference (such as chi-squared tests) and model
diagnostics (including simulation envelopes, residuals, residual plots and Q-Q
plots).
