## postgresql-16-pgmp

arbitrary precision integers and rationals for PostgreSQL 16

pgmp is a PostgreSQL extension to add the GMP data types and functions
directly into the database.

## postgresql-16-numeral

numeral datatypes for PostgreSQL

This module provides numeric data types for PostgreSQL that use
numerals (words instead of digits) for input and output.

## libstd-rust-1.66

Rust standard libraries

Rust is a curly-brace, block-structured expression language. It
visually resembles the C language family, but differs significantly
in syntactic and semantic details. Its design is oriented toward
concerns of "programming in the large", that is, of creating and
maintaining boundaries - both abstract and operational - that
preserve large-system integrity, availability and concurrency.

## r-cran-spatstat.linnet

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.

## sch-rnd-export-vector

Export formats: vector graphics

Common vector graphic export formats: ps, eps, svg.

## r-cran-spatstat.explore

GNU R exploratory data analysis for the 'spatstat' family

Functionality for exploratory data analysis and nonparametric analysis
of spatial data, mainly spatial point patterns, in the 'spatstat' family
of packages. (Excludes analysis of spatial data on a linear network,
which is covered by the separate package 'spatstat.linnet'.) Methods
include quadrat counts, K-functions and their simulation envelopes,
nearest neighbour distance and empty space statistics, Fry plots, pair
correlation function, kernel smoothed intensity, relative risk
estimation with cross-validated bandwidth selection, mark correlation
functions, segregation indices, mark dependence diagnostics, and kernel
estimates of covariate effects. 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.