python3-pyproject-api
API to interact with Python pyproject.toml-based projects
pyproject-api aims to abstract away interaction with pyproject.toml style
projects in a flexible way.
libiv-unidraw2t64
Application Frameworks layered on Unidraw
InterViews is written in C++ and portable to several different Unix
platforms.
Applications written with the InterViews library can be configured to
provide a special "Look and Feel", such as SGI-Motif and normal Motif.
The package includes the unidraw dynamic library, along with
extensions for frame and graph editing needed by the ivtools vector
graphics editors.
python3-ubelt
Python Utility Belt Library
Ubelt is a small library of robust, tested, documented, and simple
functions that extend the Python standard library. It has a flat
API that all behaves similarly on Windows, Mac, and Linux (up to
some small unavoidable differences). Almost every function in ubelt
was written with a doctest.
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.
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.
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.