k3ylabs/cosoco-image-dataset huggingface dataset:
Package: r-cran-sparr (2.2-15-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=sparr
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-spatstat (>= 2.0-0), r-cran-spatstat.utils, r-cran-spatstat.geom, r-cran-spatstat.core, r-cran-doparallel, r-cran-foreach, r-cran-misc3d
Description:  GNU R spatial and spatiotemporal relative risk Provides functions to estimate kernel-smoothed spatial and spatio- temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.
Package: r-cran-sparsem (1.81-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=SparseM
Depends: libc6 (>= 2.29), r-base-core (>= 4.0.4-1build1), r-api-4.0
Description:  GNU R package for basic linear algebra for sparse matrices The SparseM package implements basic linear algebra for sparse matrices.
Package: r-cran-sparsesvd (0.2-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=sparsesvd
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-matrix, libc6 (>= 2.7)
Description:  GNU R sparse truncated singular value decomposition Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix. Currently, only sparse real matrices in Matrix package format are supported.
Package: r-cran-spatial (7.3-15-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spatial
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, libc6 (>= 2.4)
Description:  GNU R package for spatial statistics The spatial package provides functions and datasets to support chapter 15 on 'Spatial Statistic' in the book 'Modern Applied Statistics with S' (4th edition) by W.N. Venables and B.D. Ripley. The following URL provides more details about the book: URL: http://www.stats.ox.ac.uk/pub/MASS4
Package: r-cran-spatialreg (1.2-1+dfsg-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spatialreg
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-spdata, r-cran-matrix, r-cran-sf, r-cran-spdep, r-cran-expm, r-cran-coda, r-cran-mass, r-cran-boot, r-cran-learnbayes, r-cran-nlme, r-cran-gmodels, libblas3 | libblas.so.3, libc6 (>= 2.4)
Description:  GNU R spatial regression analysis A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep', 'sphet' and 'spse'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross- sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. 'spatialreg' >= 1.1-* correspond to 'spdep' >= 1.1-1, in which the model fitting functions are deprecated and pass through to 'spatialreg', but will mask those in 'spatialreg'. From versions 1.2-*, the functions will be made defunct in 'spdep'.
Package: r-cran-spatstat.core (2.3-2-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spatstat.core
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-spatstat.data (>= 2.1-0), r-cran-spatstat.geom (>= 2.3-0), r-cran-nlme, r-cran-rpart, r-cran-spatstat.utils (>= 2.2-0), r-cran-spatstat.sparse (>= 2.0-0), r-cran-mgcv, r-cran-matrix, r-cran-abind, r-cran-tensor, r-cran-goftest (>= 1.2-2), libc6 (>= 2.29), libstdc++6 (>= 5)
Description:  core functionality of the 'spatstat' family of GNU R packages Functionality for data analysis and modelling 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'.) Exploratory 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. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. 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.
Package: r-cran-spatstat.data (2.1-2-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spatstat.data
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-spatstat.utils (>= 2.1-0), r-cran-matrix
Description:  datasets for the package r-cran-spatstat This package contains all the datasets for the 'r-cran-spatstat' package which was separated by upstream and thus is provided as a separate package also in Debian.
Package: r-cran-spatstat.geom (2.3-2-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spatstat.geom
Depends: r-base-core (>= 4.1.2-1ubuntu2), r-api-4.0, r-cran-spatstat.data (>= 2.0-0), r-cran-spatstat.utils (>= 2.2-0), r-cran-deldir (>= 1.0-2), r-cran-polyclip (>= 1.10-0), libc6 (>= 2.4)
Description:  GNU R geometrical functionality of the 'spatstat' package This is a subset of the original 'spatstat' package, containing the user- level code from 'spatstat' which performs geometrical operations, except for the geometry of linear networks.
Package: r-cran-spatstat.linnet (2.3-1-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spatstat.linnet
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-spatstat.data (>= 2.1-0), r-cran-spatstat.geom (>= 2.3-0), r-cran-spatstat.core (>= 2.3-0), r-cran-spatstat.utils (>= 2.2-0), r-cran-matrix, r-cran-spatstat.sparse (>= 2.0), libc6 (>= 2.4)
Description:  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.
Package: r-cran-spatstat.random (2.1-0-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spatstat.random
Depends: r-base-core (>= 4.1.2-1ubuntu2), r-api-4.0, r-cran-spatstat.data (>= 2.1-0), r-cran-spatstat.geom (>= 2.3-0), r-cran-spatstat.utils (>= 2.2-0), libc6 (>= 2.35), libstdc++6 (>= 5)
Description:  Random Generation Functionality for the 'spatstat' Family Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes (simple sequential inhibition, Matern inhibition models, Matern cluster process, Neyman-Scott cluster processes, log- Gaussian Cox processes, product shot noise cluster processes) and simulation of Gibbs point processes (Metropolis-Hastings birth-death- shift algorithm, alternating Gibbs sampler). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'.
Package: r-cran-spatstat.sparse (2.1-0-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spatstat.sparse
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-matrix, r-cran-abind, r-cran-tensor, r-cran-spatstat.utils (>= 2.1-0)
Description:  GNU R sparse three-dimensional arrays and linear algebra utilities Defines sparse three-dimensional arrays and supports standard operations on them. The package also includes utility functions for matrix calculations that are common in statistics, such as quadratic forms.
Package: r-cran-spatstat.utils (2.3-0-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spatstat.utils
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0
Description:  GNU R utility functions for r-cran-spatstat This GNU R package contains utility functions for the 'spatstat' package which may also be useful for other purposes.
Package: r-cran-spatstat (2.3-0-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spatstat
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-spatstat.data (>= 2.1-0), r-cran-spatstat.geom (>= 2.3-0), r-cran-spatstat.core (>= 2.3-0), r-cran-spatstat.linnet (>= 2.3-0), r-cran-spatstat.utils (>= 2.2-0)
Description:  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).
Package: r-cran-spc (1:0.6.5-1)
Section: universe/science
Homepage: https://cran.r-project.org/package=spc
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, libc6 (>= 2.29), liblapack3 | liblapack.so.3
Description:  GNU R Statistical Process Control Evaluation of control charts by means of the zero-state, steady-state ARL (Average Run Length). Setting up control charts for given in-control ARL and plotting of the related figures. The control charts under consideration are one- and two-sided EWMA and CUSUM charts for monitoring the mean of normally distributed independent data. Now, the ARL calculation of EWMA-S^2 control charts is added. Other charts and parameters are in preparation. Further SPC areas will be covered as well (sampling plans, capability indices ...).
Package: r-cran-spdata (2.0.1-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spData
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-sp, r-cran-raster
Description:  GNU R datasets for spatial analysis Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep'). Unlike other spatial data packages such as 'rnaturalearth' and 'maps', it also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. Some of the datasets are designed to illustrate specific analysis techniques. cycle_hire and cycle_hire_osm, for example, is designed to illustrate point pattern analysis techniques.
Package: r-cran-spdep (1.2-2+dfsg-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spdep
Depends: r-base-core (>= 4.1.2-1ubuntu2), r-api-4.0, r-cran-sp (>= 1.0), r-cran-spdata (>= 0.2.6.0), r-cran-sf, r-cran-deldir, r-cran-boot (>= 1.3-1), r-cran-units, r-cran-s2, r-cran-e1071, libc6 (>= 2.35)
Description:  GNU R spatial dependence: weighting schemes, statistics and models A collection of functions to create spatial weights matrix objects from polygon contiguities, from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial autocorrelation, including global Moran's I, APLE, Geary's C, Hubert/Mantel general cross product statistic, Empirical Bayes estimates and Assunção/Reis Index, Getis/Ord G and multicoloured join count statistics, local Moran's I and Getis/Ord G, saddlepoint approximations and exact tests for global and local Moran's I; and functions for estimating spatial simultaneous autoregressive (SAR) lag and error models, impact measures for lag models, weighted and unweighted SAR and CAR spatial regression models, semi-parametric and Moran eigenvector spatial filtering, GM SAR error models, and generalized spatial two stage least squares models.
Package: r-cran-spelling (2.2-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spelling
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-commonmark, r-cran-xml2, r-cran-hunspell (>= 3.0), r-cran-knitr
Description:  tools for spell checking in GNU R Spell checking common document formats including latex, markdown, manual pages, and description files. Includes utilities to automate checking of documentation and vignettes as a unit test during 'R CMD check'. Both British and American English are supported out of the box and other languages can be added. In addition, packages may define a 'wordlist' to allow custom terminology without having to abuse punctuation.
Package: r-cran-splines2 (0.4.5-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=splines2
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 11)
Description:  GNU R regression spline functions and classes Constructs B-splines and its integral, monotone splines (M-splines) and its integral (I-splines), convex splines (C-splines), and their derivatives of given order. Piecewise constant basis is allowed for B-splines and M-splines. See De Boor (1978) <doi:10.1002/zamm.19800600129>, Ramsay (1988) <doi:10.1214/ss/1177012761>, and Meyer (2008) <doi:10.1214/08-AOAS167> for more information.
Package: r-cran-spp (1.16.0-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=spp
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-rcpp, r-bioc-rsamtools, r-cran-catools, r-cran-bh (>= 1.66), libbz2-1.0, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4)
Description:  GNU R ChIP-seq processing pipeline R package for anlaysis of ChIP-seq and other functional sequencing data  * Assess overall DNA-binding signals in the data and select appropriate    quality of tag alignment.  * Discard or restrict positions with abnormally high number of tags.  * Calculate genome-wide profiles of smoothed tag density and save them    in WIG files for viewing in other browsers.  * Calculate genome-wide profiles providing conservative statistical    estimates of fold enrichment ratios along the genome. These can be    exported for browser viewing, or thresholded to determine regions of    significant enrichment/depletion.  * Determine statistically significant point binding positions  * Assess whether the set of point binding positions detected at a    current sequencing depth meets saturation criteria, and if does not,    estimate what sequencing depth would be required to do so.
Package: r-cran-sqldf (0.4-11-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=sqldf
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-gsubfn (>= 0.6), r-cran-proto, r-cran-rsqlite, r-cran-dbi, r-cran-chron
Description:  manipulate GNU R data frames using SQL The sqldf() function is typically passed a single argument which is an SQL select statement where the table names are ordinary R data frame names.  sqldf() transparently sets up a database, imports the data frames into that database, performs the SQL select or other statement and returns the result using a heuristic to determine which class to assign to each column of the returned data frame.  The sqldf() or read.csv.sql() functions can also be used to read filtered files into R even if the original files are larger than R itself can handle. 'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported.
Package: r-cran-squarem (2021.1-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=SQUAREM
Depends: r-base-core (>= 4.0.3-1), r-api-4.0
Description:  Squared Extrapolation Methods for Accelerating EM-Like Monotone Algorithms Algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mapping such as the EM algorithm. It can be used to accelerate any smooth, linearly convergent acceleration scheme.  A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette("SQUAREM").
Package: r-cran-stable (1.1.4-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=stable
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-rmutil, r-cran-stabledist, libc6 (>= 2.4)
Description:  GNU R probability functions and generalized regression models This GNU R package provides probability functions and generalized regression models for stable distributions. . Density, distribution, quantile and hazard functions of a stable variate; generalized regression models for the parameters of a stable distribution. See the README for how to make equivalent calls to those of 'stabledist'. See github for Journal article.
Package: r-cran-stabledist (0.7-1-3)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=stabledist
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0
Description:  GNU R package for stable distribution functions This package provides density, probability and quantile functions, and random number generation for (skew) stable distributions, using the parametrizations of Nolan.
Package: r-cran-stablelearner (0.1-3+ds-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=stablelearner
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-mass, r-cran-e1071, r-cran-partykit, r-cran-party, r-cran-randomforest, r-cran-ranger
Description:  Stability Assessment of Statistical Learning Methods Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning.
Package: r-cran-stanheaders (2.21.0-7-1build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=StanHeaders
Depends: r-base-core (>= 4.1.2-1ubuntu2), r-api-4.0, r-cran-rcppparallel (>= 5.0.1), r-cran-rcppeigen
Description:  C++ Header Files for Stan for GNU R The C++ header files of the Stan project are provided by this package, but it contains no R code, vignettes, or function documentation. There is a shared object containing part of the 'CVODES' library, but it is not accessible from R. 'StanHeaders' is only useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
Package: r-cran-stars (0.5-5-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=stars
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-abind, r-cran-sf (>= 1.0-3), r-cran-classint (>= 0.4-1), r-cran-lwgeom, r-cran-rlang, r-cran-units
Description:  scalable, spatiotemporal tidy arrays for GNU R This package provides support for scalable, spatiotemporal tidy arrays in GNU R, using GDAL bindings. . Package stars provides infrastructure for data cubes, array data with labeled dimensions, with emphasis on arrays where some of the dimensions relate to time and/or space.
Package: r-cran-startupmsg (0.9.6-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=startupmsg
Depends: r-base-core (>= 4.1.2-1ubuntu2), r-api-4.0
Description:  GNU R utilities for start-up messages This GNU R package provides utilities to create or suppress start-up messages.