607431a

Package: r-cran-surveillance
Description-md5: c0b0bb231313eb8bbfdc0a5bb8199755
Description-en: GNU R package for the Modeling and Monitoring of Epidemic Phenomena
 Statistical methods for the modeling and monitoring of time series of
 counts, proportions and categorical data, as well as for the modeling of
 continuous-time point processes of epidemic phenomena.
 .
 The monitoring methods focus on aberration detection in count data time
 series from public health surveillance of communicable diseases, but
 applications could just as well originate from environmetrics,
 reliability engineering, econometrics, or social sciences. The package
 implements many typical outbreak detection procedures such as the
 (improved) Farrington algorithm, or the negative binomial GLR-CUSUM
 method of Höhle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel
 CUSUM approach combining logistic and multinomial logistic modeling is
 also included. The package contains several real-world data sets, the
 ability to simulate outbreak data, and to visualize the results of the
 monitoring in a temporal, spatial or spatio-temporal fashion. A recent
 overview of the available monitoring procedures is given by Salmon et al.
 (2016) <doi:10.18637/jss.v070.i10>.
 .
 For the retrospective analysis of epidemic spread, the package provides
 three endemic-epidemic modeling frameworks with tools for visualization,
 likelihood inference, and simulation. hhh4() estimates models for
 (multivariate) count time series following Paul and Held (2011)
 <doi:10.1002/sim.4177> and Meyer and Held (2014)
 <doi:10.1214/14-AOAS743>. twinSIR() models the
 susceptible-infectious-recovered (SIR) event history of a fixed
 population, e.g, epidemics across farms or networks, as a multivariate
 point process as proposed by Höhle (2009) <doi:10.1002/bimj.200900050>.
 twinstim() estimates self-exciting point process models for a
 spatio-temporal point pattern of infective events, e.g., time-stamped
 geo-referenced surveillance data, as proposed by Meyer et al. (2012)
 <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the
 implemented space-time modeling frameworks for epidemic phenomena is
 given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.
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607241a
Package: r-cran-spatstat.explore
Description-md5: 990c0195b40e3ea9c083c70db3723b07
Description-en: 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.

Package: r-cran-spatstat.geom
Description-md5: a328a2dac45aa3ecdc80c1912af6f167
Description-en: 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'.)

Package: r-cran-spatstat.linnet
Description-md5: 37bacb35d645e220b44f647e9e7fd1b2
Description-en: 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.

Package: r-cran-spatstat.model
Description-md5: b367ba52fa77187da92b04ed93f5a3ef
Description-en: Parametric Statistical Modelling and Inference for the 'spatstat' Family
 Functionality for parametric statistical modelling and inference for 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'.) Supports parametric modelling,
 formal statistical inference, and model validation. Parametric models include
 Poisson point processes, Cox point processes, Neyman-Scott cluster processes,
 Gibbs point processes and determinantal point processes. Models can
 be fitted to data using maximum likelihood, maximum pseudolikelihood,
 maximum composite likelihood and the method of minimum contrast. Fitted models
 can be simulated and predicted. Formal inference includes hypothesis tests
 (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test,
 Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test,
 segregation test, ANOVA tests of fitted models, adjusted composite likelihood
 ratio test, envelope tests, Dao-Genton test, balanced independent two-stage
 test), confidence intervals for parameters, and prediction intervals for
 point counts. Model validation techniques include leverage, influence, partial
 residuals, added variable plots, diagnostic plots, pseudoscore residual plots,
 model compensators and Q-Q plots.

Package: r-cran-spatstat.random
Description-md5: 9b2339118d58ad20ee62896c2a4f4b9b
Description-en: 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
 including simple sequential inhibition, Matern inhibition models, Neyman-Scott
 cluster processes (using direct, Brix-Kendall, or hybrid algorithms),
 log-Gaussian Cox processes, product shot noise cluster processes and Gibbs
 point processes (using Metropolis-Hastings birth-death-shift algorithm,
 alternating Gibbs sampler, or coupling-from-the-past perfect simulation).
 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'.

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607234a
Package: r-cran-spatstat
Description-md5: f0ae19dab41356d2a833917d12078ce8
Description-en: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
 Comprehensive open-source toolbox for analysing Spatial Point Patterns.
 Focused mainly on two-dimensional point patterns, including multitype/marked
 points, in any spatial region. Also supports three-dimensional point patterns,
 space-time point patterns in any number of dimensions, point patterns
 on a linear network, and patterns of other geometrical objects.
 Supports spatial covariate data such as pixel images. Contains over 3000
 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,
 pixel images, tessellations, and linear networks. 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.

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606829a

Package: r-cran-rtdists
Description-md5: 47ddabe750824e3ab87f92a16168e628
Description-en: GNU R response time distributions
 Provides response time distributions (density/PDF,
 distribution function/CDF, quantile function, and random
 generation): (a) Ratcliff diffusion model (Ratcliff &
 McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C
 code by Andreas and Jochen Voss and (b) linear ballistic
 accumulator (LBA; Brown & Heathcote, 2008,
 <doi:10.1016/j.cogpsych.2007.12.002>) with different
 distributions underlying the drift rate.
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606604a
Package: r-cran-rmpfr
Description-md5: e2784fa42d97ec6cdfe1c32f9b19408a
Description-en: R MPFR - Multiple Precision Floating-Point Reliable
 Arithmetic (via S4 classes and methods) for
 arbitrary precision floating point numbers, including transcendental
 ("special") functions.  To this end, the package interfaces to
 the 'LGPL' licensed 'MPFR' (Multiple Precision Floating-Point Reliable) Library
 which itself is based on the 'GMP' (GNU Multiple Precision) Library.

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605982a
Package: r-cran-qpdf
Description-md5: 4773ff9f60de7f9516adebbc90922689
Description-en: Split, Combine and Compress PDF Files
 Content-preserving transformations transformations of PDF files such as split,
 combine, and compress. This package interfaces directly to the 'qpdf'
 C++ library <https://qpdf.sourceforge.io/> and does not require any command
 line utilities. Note that 'qpdf' does not read actual content from PDF files:
 to extract text and data you need the 'pdftools' package.

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605793a
Package: r-cran-propclust
Description-md5: b6bb379544904659a5cb691dc9714be1
Description-en: Propensity Clustering and Decomposition
 Implementation of propensity clustering and decomposition as described
 in Ranola et al. (2013) <doi:10.1186/1752-0509-7-21>. Propensity
 decomposition can be viewed on the one hand as a generalization of the
 eigenvector-based approximation of correlation networks, and on the
 other hand as a generalization of random multigraph models and conformity-
 based decompositions.

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605354a
Package: r-cran-partitions
Description-md5: dce417f1bc8ca62ec9850073d150b17e
Description-en: Additive partitions of integers
 Enumerates the partitions, unequal partitions, and restricted
 partitions of an integer; the three corresponding partition functions
 are also given.  Set partitions and now compositions are included.

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605346a
Package: r-cran-paramhelpers
Description-md5: e2e5209cdb4210875294d4bc7fcf4fb7
Description-en: Helpers for Parameters in Black-Box Optimization, Tuning and Machine Learning
 Functions for parameter descriptions and operations in black-box optimization,
 tuning and machine learning. Parameters can be described (type, constraints,
 defaults, etc.), combined to parameter sets and can in general be
 programmed on. A useful OptPath object (archive) to log function evaluations
 is also provided.

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605335a
Package: r-cran-parallelmap
Description-md5: d53466a710ea6362181229d908eecd3b
Description-en: GNU R unified interface to parallelization back-ends
 Unified parallelization framework for multiple back-end,
 designed for internal package and interactive usage.
 The main operation is a parallel "map" over lists.
 Supports local, multicore, mpi and BatchJobs mode.
 Allows "tagging" of the parallel operation
 with a level name that can be later selected by the user to
 switch on parallel execution for exactly this operation.

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604971a
Package: r-cran-msm
Description-md5: 2885ffdca218ecbc0a057a1828ea7f19
Description-en: Multi-State Markov and Hidden Markov Models in Continuous Time
 Functions for fitting continuous-time Markov and hidden Markov multi-state
 models to longitudinal data. Designed for processes observed at arbitrary
 times in continuous time (panel data) but some other observation schemes
 are supported. Both Markov transition rates and the hidden Markov output
 process can be modelled in terms of covariates, which may be constant or
 piecewise-constant in time.

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604191a
Package: r-cran-keyring
Description-md5: aa34657d5579802ccbaaa3bfe1ff2fa6
Description-en: access the system credential store from GNU R
 Platform independent 'API' to access the operating system's
 credential store. Currently supports: 'Keychain' on 'macOS', Credential
 Store on 'Windows', the Secret Service 'API' on 'Linux', and a simple,
 platform independent store implemented with environment variables.
 Additional storage back-ends can be added easily.

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603931a
Package: r-cran-hunspell
Description-md5: 1d0725009df8d2a97c0da198b937d199
Description-en: GNU R support for high-performance stemmer, tokenizer, and spell checker
 Low level spell checker and morphological analyzer based on the
 famous 'hunspell' library <https://hunspell.github.io>. The package can analyze
 or check individual words as well as parse text, LaTeX, html or xml documents.
 For a more user-friendly interface use the 'spelling' package which builds on
 this package to automate checking of files, documentation and vignettes in all
 common formats.

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603891a
Package: r-cran-hexbin
Description-md5: 52653e09d4608123045028176d9e0bda
Description-en: Hexagonal Binning Routines
 Binning and plotting functions for hexagonal bins.

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603883a
Package: r-cran-hdf5r
Description-md5: 02f3f5d6d81c6bcf9790c84557d9874d
Description-en: GNU R interface to the 'HDF5' binary data format
 'HDF5' is a data model, library and file format for storing
 and managing large amounts of data. This package provides a nearly
 feature complete, object oriented  wrapper for the 'HDF5' API
 <https://support.hdfgroup.org/HDF5/doc/RM/RM_H5Front.html> using R6 classes.
 Additionally, functionality is added so that 'HDF5' objects behave very
 similar to their corresponding R counterparts.

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603807a
Package: r-cran-gsignal
Description-md5: 0a30f4e32f90e4049c198008897b97ff
Description-en: Signal Processing
 R implementation of the 'Octave' package 'signal', containing
 a variety of signal processing tools, such as signal generation and
 measurement, correlation and convolution, filtering, filter design,
 filter analysis and conversion, power spectrum analysis, system
 identification, decimation and sample rate change, and windowing.

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603759a
Package: r-cran-gower
Description-md5: 57ed8f8f61d304424680d1d58e2f782d
Description-en: GNU R Gower's Distance
 Compute Gower's distance (or similarity) coefficient between records.
 Compute the top-n matches between records. Core algorithms are executed
 in parallel on systems supporting OpenMP.

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603750a
Package: r-cran-gmm
Description-md5: 97331c8154ef0d07104cd49408715cb7
Description-en: GNU R generalized method of moments and generalized empirical likelihood
 This GNU R package is a complete suite to estimate models based on
 moment conditions. It includes the two step Generalized method of
 moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and
 continuous updated estimator (Hansen, Eaton and Yaron 1996;
 <doi:10.2307/1392442>) and several methods that belong to the
 Generalized Empirical Likelihood family of estimators (Smith 1997;
 <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997;
 <doi:10.1214/aos/1069362388>, Newey and Smith 2004;
 <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005
 <doi:10.1111/j.1468-0262.2005.00601.x>).

Package: r-cran-gmp
Description-md5: 3e721cb341f2009e89687ae64246a08d
Description-en: Multiple Precision Arithmetic
 Multiple Precision Arithmetic (big integers and rationals,
 prime number tests, matrix computation), "arithmetic without limitations"
 using the C library GMP (GNU Multiple Precision Arithmetic).

Package: r-cran-gnm
Description-md5: dc709c8fc5bf1aa58c6b11e6bc9c6e74
Description-en: Generalized Nonlinear Models
 Functions to specify and fit generalized nonlinear models, including models
 with multiplicative interaction terms such as the UNIDIFF model
 from sociology and the AMMI model from crop science, and many others.
 Over-parameterized representations of models are used throughout;
 functions are provided for inference on estimable parameter combinations,
 as well as standard methods for diagnostics etc.

Package: r-cran-goftest
Description-md5: 578a8b7f40182e6aa2c43bb1175b8a0d
Description-en: GNU R Classical Goodness-of-Fit Tests for Univariate Distributions
 This R package provides Cramer-Von Mises and Anderson-Darling tests of
 goodness-of-fit for continuous univariate distributions, using efficient
 algorithms.

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603713a
Package: r-cran-glasso
Description-md5: b875e678f9f8b0c2b403c02bb7d7e29b
Description-en: GNU R graphical lasso: estimation of Gaussian graphical models
 Estimation of a sparse inverse covariance matrix using a lasso (L1)
 penalty. Facilities are provided for estimates along a path of values
 for the regularization parameter.

Package: r-cran-glmnet
Description-md5: 3540cbed28fd611fe9733912b4c8302a
Description-en: Lasso and Elastic-Net Regularized Generalized Linear Models
 Extremely efficient procedures for fitting the entire lasso or elastic-net
 regularization path for linear regression, logistic and multinomial
 regression models, Poisson regression and the Cox model. Two recent
 additions are the multiple-response Gaussian, and the grouped multinomial.
 The algorithm uses cyclical coordinate descent in a path-wise fashion, as
 described in the paper Introduction to Glmnet.

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603705a
Package: r-cran-git2r
Description-md5: 2ad51e7a679949256e9d8b7ec8c83bb7
Description-en: GNU R access to Git repositories
 This GNU R package provides an interface to the libgit2 library, which is
 a pure C implementation of the Git core methods. Provides access to Git
 repositories to extract data and running some basic Git commands.

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603649a
Package: r-cran-ggplot.multistats
Description-md5: 21b19d1a0dba99a83ac061edeae1a8a7
Description-en: GNU R multiple summary statistics for binned stats/geometries
 Provides the ggplot binning layer stat_summaries_hex(),
 which functions similar to its singular form,
 but allows the use of multiple statistics per bin.
 Those statistics can be mapped to multiple bin aesthetics.

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603610a
Package: r-cran-geometry
Description-md5: 1f05fff45c88ae5839d9ccd8d6f1f9d7
Description-en: Mesh Generation and Surface Tessellation
 Makes the 'Qhull' library <http://www.qhull.org> available in R, in a similar
 manner as in Octave and MATLAB. Qhull computes convex hulls, Delaunay
 triangulations, halfspace intersections about a point, Voronoi diagrams,
 furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams.
 It runs in 2D, 3D, 4D, and higher dimensions. It implements the Quickhull
 algorithm for computing the convex hull. Qhull does not support constrained
 Delaunay triangulations, or mesh generation of non-convex objects, but the
 package does include some R functions that allow for this.

Package: r-cran-geosphere
Description-md5: cd11ae36d417d8dd1592f10ce5c46b1a
Description-en: Spherical Trigonometry
 Spherical trigonometry for geographic applications. That is, compute distances
 and related measures for angular (longitude/latitude) locations.

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603581a
Package: r-cran-genie
Description-md5: 40ea4e5ad201be38c311b3afe61ab7dd
Description-en: Fast, Robust, and Outlier Resistant Hierarchical Clustering
 Implements a basic version of the hierarchical clustering algorithm 'Genie'
 which links two point groups in such a way that an inequity measure
 (namely, the Gini index) of the cluster sizes does not significantly
 increase above a given threshold. This method most often outperforms many
 other data segmentation approaches in terms of clustering quality as tested
 on a wide range of benchmark datasets. At the same time, Genie retains
 the high speed of the single linkage approach, therefore it is also suitable
 for analysing larger data sets. For more details see (Gagolewski et al.
 2016 <DOI:10.1016/j.ins.2016.05.003>). For a faster and more
 feature-rich implementation, see the 'genieclust' package (Gagolewski, 2021
 <DOI:10.1016/j.softx.2021.100722>).

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603556a
Package: r-cran-gdtools
Description-md5: f8c483fc907e30b702decb92a9bf54e5
Description-en: GNU R utilities for graphical rendering
 This package provides useful tools for writing vector graphics devices
 in GNU R.

Package: r-cran-gee
Description-md5: 3c1919a7979fde3f0926abea664039c8
Description-en: Generalized Estimation Equation Solver
 R package which provide a function to solve a Generalized Estimation
 Equation Model.

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603511a
Package: r-cran-gam
Description-md5: 97917079df78a8cade60b851b2cbafb8
Description-en: Generalized Additive Models for R
 Functions for fitting and working with generalized additive models,
 as described in chapter 7 of “Statistical Models in S” (Chambers and
 Hastie (eds), 1991), and “Generalized Additive Models” (Hastie and
 Tibshirani, 1990).

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603448a
Package: r-cran-freetypeharfbuzz
Description-md5: 425da01d63837f256908c7329f7b6032
Description-en: GNU R deterministic computation of text box metrics
 Unlike other tools that dynamically link to the 'Cairo'
 stack, 'freetypeharfbuzz' is statically linked to specific
 versions of the 'FreeType' and 'harfbuzz' libraries (2.9 and 1.7.6
 respectively). This ensures deterministic computation of text box
 extents for situations where reproducible results are crucial (for
 instance unit tests of graphics).
 .
 Package 'freetypeharfbuzz’ was removed from the CRAN repository.
 The code was obtained from the archive.

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603441a
Package: r-cran-fpc
Description-md5: c6256855695264ff0fe58768fc1652d5
Description-en: GNU R flexible procedures for clustering
 Various methods for clustering and cluster validation. Fixed point
 clustering. Linear regression clustering. Clustering by merging Gaussian
 mixture components. Symmetric and asymmetric discriminant projections
 for visualisation of the separation of groupings. Cluster validation
 statistics for distance based clustering including corrected Rand index.
 Cluster-wise cluster stability assessment. Methods for estimation of the
 number of clusters: Calinski-Harabasz, Tibshirani and Walther's
 prediction strength, Fang and Wang's bootstrap stability.
 Gaussian/multinomial mixture fitting for mixed continuous/categorical
 variables. Variable-wise statistics for cluster interpretation. DBSCAN
 clustering. Interface functions for many clustering methods implemented
 in R, including estimating the number of clusters with kmeans, pam and
 clara. Modality diagnosis for Gaussian mixtures.

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603314a
Package: r-cran-filelock
Description-md5: 3ef1a42b4644be51e785a1416187387d
Description-en: Portable File Locking
 Place an exclusive or shared lock on a file. It uses 'LockFile' on Windows
 and 'fcntl' locks on Unix-like systems.

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603300a
Package: r-cran-fdrtool
Description-md5: b4146e0cbe03f165ad07991a8de5ffb4
Description-en: GNU R estimation of false discovery dates and higher criticism
 Estimates both tail area-based false discovery rates (Fdr) as well as
 local false discovery rates (fdr) for a variety of null models (p-values,
 z-scores, correlation coefficients, t-scores). The proportion of
 null values and the parameters of the null distribution are adaptively
 estimated from the data. In addition, the package contains functions for
 non-parametric density estimation (Grenander estimator), for monotone
 regression (isotonic regression and antitonic regression with weights),
 for computing the greatest convex minorant (GCM) and the least concave
 majorant (LCM), for the half-normal and correlation distributions, and
 for computing empirical higher criticism (HC) scores and the
 corresponding decision threshold.

Package: r-cran-ff
Description-md5: 3edf45cff5a0982f26286714f488e0c5
Description-en: Memory-Efficient Storage of Large Data on Disk and Fast Access Functions
 The ff package provides data structures that are stored on disk
 but behave (almost) as if they were in RAM by transparently mapping only
 a section (pagesize) in main memory - the effective virtual memory consumption
 per ff object. ff supports R's standard atomic data types 'double',
 'logical', 'raw' and 'integer' and non-standard atomic types boolean
 (1 bit), quad (2 bit unsigned), nibble (4 bit unsigned), byte (1 byte signed
 with NAs), ubyte (1 byte unsigned), short (2 byte signed with NAs), ushort
 (2 byte unsigned), single (4 byte float with NAs). For example 'quad' allows
 efficient storage of genomic data as an 'A','T','G','C' factor. The unsigned
 types support 'circular' arithmetic. There is also support for close-to-atomic
 types 'factor', 'ordered', 'POSIXct', 'Date' and custom close-to-atomic types.
 ff not only has native C-support for vectors, matrices and arrays with
 flexible dimorder (major column-order, major row-order and generalizations for
 arrays). There is also a ffdf class not unlike data.frames and import/export
 filters for csv files. ff objects store raw data in binary flat files in
 native encoding, and complement this with metadata stored in R as physical
 and virtual attributes. ff objects have well-defined hybrid copying semantics,
 which gives rise to certain performance improvements through virtualization.
 ff objects can be stored and reopened across R sessions. ff files can be
 shared by multiple ff R objects (using different data en/de-coding schemes)
 in the same process or from multiple R processes to exploit parallelism.
 A wide choice of finalizer options allows to work with 'permanent'
 files as well as creating/removing 'temporary' ff files completely transparent
 to the user. On certain OS/Filesystem combinations, creating the ff files
 works without notable delay thanks to using sparse file allocation.
 Several access optimization techniques such as Hybrid Index Preprocessing
 and Virtualization are implemented to achieve good performance even with
 large datasets, for example virtual matrix transpose without touching a single
 byte on disk. Further, to reduce disk I/O, 'logicals' and non-standard data
 types get stored native and compact on binary flat files i.e. logicals take up
 exactly 2 bits to represent TRUE, FALSE and NA. Beyond basic access functions,
 the ff package also provides compatibility functions that facilitate writing
 code for ff and ram objects and support for batch processing on ff objects
 (e.g. as.ram, as.ff, ffapply). ff interfaces closely with functionality from
 package 'bit': chunked looping, fast bit operations and coercions between
 different objects that can store subscript information ('bit', 'bitwhich', ff
 'boolean', ri range index, hi hybrid index). This allows to work interactively
 with selections of large datasets and quickly modify selection criteria.
 Further high-performance enhancements can be made available upon request.

Package: r-cran-fftw
Description-md5: 839b7fe09d61cdc9b3f631cc232a9053
Description-en: GNU R fast FFT and DCT Based on the FFTW Library
 This GNU R package provides a simple and efficient wrapper around the
 fastest  Fourier transform in the west (FFTW) library
 <http://www.fftw.org/>.

Package: r-cran-fftwtools
Description-md5: 29cb6f6fdc7961f1f8cb560804c7c44b
Description-en: Wrapper for FFTW3
 Provides a wrapper for several FFTW functions. This package provides access to
 the two-dimensional FFT, the multivariate FFT, and the one-dimensional real to
 complex FFT using the FFTW3 library. The package includes the functions fftw()
 and mvfftw() which are designed to mimic the functionality of the R functions
 fft() and mvfft(). The FFT functions have a parameter that allows them to not
 return the redundant complex conjugate when the input is real data.

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Package: r-cran-fastmatch
Description-md5: 71d07bfdee2c114304eb1acadf8ee19f
Description-en: GNU R package for fast match replacement for repeated look-ups
 This R package provides a fast match replacement for cases that
 require repeated look-ups. Extremely fast after the first lookup
 match as it keeps the hash table in memory.

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Package: python3-fastcluster
Description-md5: 6c6213f1ce213becdc440deefb803999
Description-en: Fast hierarchical clustering routines for Python 3
 This library provides Python functions for hierarchical
 clustering. It generates hierarchical clusters from distance matrices
 or from vector data.
 .
 Part of this module is intended to replace the functions
 .
     linkage, single, complete, average, weighted, centroid, median, ward
 .
 in the module scipy.cluster.hierarchy with the same functionality but
 much faster algorithms. Moreover, the function 'linkage_vector'
 provides memory-efficient clustering for vector data.
 .
 The interface is very similar to MATLAB's Statistics Toolbox API to
 make code easier to port from MATLAB to Python/Numpy. The core
 implementation of this library is in C++ for efficiency.
 .
 This package provides the package's Python 3 interface.

Package: r-cran-fastcluster
Description-md5: 2db457eebec7bb41db0f6f450fef99f6
Description-en: Fast hierarchical clustering routines for GNU R
 Fastcluster implements fast hierarchical, agglomerative clustering
 routines. Part of the functionality is designed as drop-in replacement
 for existing routines: “linkage” in the SciPy package
 “scipy.cluster.hierarchy”, “hclust” in R's “stats” package, and the
 “flashClust” package. It provides the same functionality with the
 benefit of a much faster implementation. Moreover, there are
 memory-saving routines for clustering of vector data, which go beyond
 what the existing packages provide. For information on how to install
 the Python files, see the file INSTALL in the source distribution.

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Package: r-cran-fail
Description-md5: b9ebbdbd80fcd69afdb9c5ce8bdee396
Description-en: GNU R File Abstraction Interface Layer (FAIL) mimicking a key-value store
 This package provides a File Abstraction Interface Layer (FAIL)
 mimicking a key-value store.  It is a more comfortable interface to work
 with a directory of R data or source files.

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Package: r-cran-expint
Description-md5: bfd9bd860ea68e08df3c7872a32e415f
Description-en: GNU R exponential integral and incomplete Gamma function
 The exponential integrals E_1(x), E_2(x), E_n(x) and
 Ei(x), and the incomplete gamma function G(a, x) defined for
 negative values of its first argument. The package also gives easy
 access to the underlying C routines through an API; see the package
 vignette for details. A test package included in sub-directory
 example_API provides an implementation. C routines derived from the
 GNU Scientific Library <https://www.gnu.org/software/gsl/>.

Package: r-cran-expm
Description-md5: 262d15100188c3184eaa78518455f8d9
Description-en: GNU R Computation of the matrix exponential and related quantities
 The matrix exponential GNU R package enables computation of the matrix
 exponential and related quantities.

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Package: r-cran-evd
Description-md5: f3949a6dbdcae7a1a93dc6f68ae6d95f
Description-en: GNU R Functions for extreme value distributions
 Extends simulation, distribution, quantile and density functions to
 univariate and multivariate parametric extreme value distributions, and
 provides fitting functions which calculate maximum likelihood estimates
 for univariate and bivariate maxima models, and for univariate and
 bivariate threshold models.

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Package: r-cran-erm
Description-md5: 50c3bc7255e7aec80c55d3e26b38834f
Description-en: GNU R package for 'extended Rasch modelling'
 eRm fits Rasch models (RM), linear logistic test models (LLTM),
 rating scale model (RSM), linear rating scale models (LRSM), partial
 credit models (PCM), and linear partial credit models (LPCM). Missing
 values are allowed in the data matrix. Additional features are the ML
 estimation of the person parameters, Andersen's LR-test,
 item-specific Wald test, itemfit and personfit statistics including
 infit and outfit measures, various ICC and related plots, automated
 stepwise item elimination, simulation module for various binary data
 matrices. An eRm platform is provided at R-forge (see URL).

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Package: r-cran-emoa
Description-md5: 1ce6a40ae8079362cfdccb5dd5604a4c
Description-en: GNU R evolutionary multiobjective optimization algorithms
 This GNU R package contains a collection of building blocks for
 the design and analysis of evolutionary multiobjective optimization
 algorithms.

Package: r-cran-energy
Description-md5: 64d77157ff18a3ad6cd16c78fac81606
Description-en: GNU R package for energy statistics for distribution comparison
 The energy package implements E-statistics (energy) tests and
 statistics for comparing distributions: multivariate normality,
 multivariate distance components and k-sample test for equal
 distributions, hierarchical clustering by e-distances, multivariate
 independence tests, distance correlation, goodness-of-fit
 tests. Energy-statistics concept based on a generalization of
 Newton's potential energy is due to Gabor J. Szekely.

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Package: r-cran-eipack
Description-md5: 19c81e4d692f804959e1cb431baba560
Description-en: Ecological Inference and Higher-Dimension Data Management
 Provides methods for analyzing R by C ecological contingency tables using
 the extreme case analysis, ecological regression, and Multinomial-Dirichlet
 ecological inference models. Also provides tools for manipulating
 higher-dimension data objects.

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Package: r-cran-earth
Description-md5: c5035f37e2435d831bc7bd704f723f16
Description-en: GNU R multivariate adaptive regression splines
 This GNU R package builds regression models using the techniques in
 Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression
 Splines" <doi:10.1214/aos/1176347963>.
 (The term "MARS" is trademarked and thus not used in the name of the
 package.)
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Package: r-cran-dotcall64
Description-md5: 00c76602d0a49b506e7d292758399b27
Description-en: Enhanced Foreign Function Interface Supporting Long Vectors
 Provides .C64(), which is an enhanced version of .C() and .Fortran() from the
 foreign function interface. .C64() supports long vectors, arguments of type
 64-bit integer, and provides a mechanism to avoid unnecessary copies of
 read-only and write-only arguments. This makes it a convenient and fast
 interface to C/C++ and Fortran code.

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Package: r-cran-distr
Description-md5: 92f83f4033911f044b4abc79fe905690
Description-en: GNU R object oriented implementation of distributions
 This GNU R package is an object oriented implementation of distributions
 S4-classes and methods for distributions.

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Package: r-cran-diptest
Description-md5: eada11fa49b08d2a3c66d41f40a8f7fd
Description-en: Hartigan's Dip Test Statistic for Unimodality - Corrected
 Compute Hartigan's dip test statistic for unimodality /
 multimodality and provide a test with simulation based p-values,  where
 the original public code has been corrected.

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Package: r-cran-dicedesign
Description-md5: 68d8f9d8dd268bdb6d550fc1e42edf52
Description-en: GNU R designs of computer experiments
 This GNU R package provides Space-Filling Designs and space-filling
 criteria (distance-based and uniformity-based), with emphasis to
 computer experiments; <doi:10.18637/jss.v065.i11>.

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Package: r-cran-deoptim
Description-md5: e8c90aa4184edd1791af23b1afca46b6
Description-en: GNU R global optimization by differential evolution
 This GNU R package implements the differential evolution algorithm for
 global optimization of a real-valued function of a real-valued parameter
 vector.

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Package: r-cran-deal
Description-md5: 0c447366842daba69e6f36b2ebdc0948
Description-en: Learning Bayesian Networks with Mixed Variables
 Bayesian networks with continuous and/or discrete variables
 can be learned and compared from data.

Package: r-cran-decor
Description-md5: e9d371dbe43ce0d8af17a18cc9b60759
Description-en: GNU R retrieve code decorations
 Retrieves code comment decorations for C++
 languages of the form '\\ [[xyz]]', which are used for automated
 wrapping of C++ functions.

Package: r-cran-deldir
Description-md5: f2d3d857735a5484ea3165abef563757
Description-en: GNU R Delaunay Triangulation and Dirichlet (Voronoi) Tessellation
 This GNU R package calculates the Delaunay triangulation and the Dirichlet or
 Voronoi tessellation (with respect to the entire plane) of a planar point set.
 This package is used in Spatial Point Pattern analysis, model-fitting and
 simulation as it is done for instance in the package r-cran-spatstat.

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Package: r-cran-dbscan
Description-md5: 3f9159f3ac447dcd75844b1151f8593c
Description-en: Density Based Clustering of Applications with Noise (DBSCAN)
 Density Based Clustering of Applications with Noise (DBSCAN) and
 Related Algorithms provides a fast reimplementation of several density-
 based algorithms of the DBSCAN family for spatial data. Includes
 the clustering algorithms DBSCAN (density-based spatial clustering
 of applications with noise) and HDBSCAN (hierarchical DBSCAN), the
 ordering algorithm OPTICS (ordering points to identify the
 clustering structure), and the outlier detection algorithm LOF
 (local outlier factor). The implementations use the kd-tree data
 structure (from library ANN) for faster k-nearest neighbor search.
 An R interface to fast kNN and fixed-radius NN search is also
 provided. Hahsler, Piekenbrock and Doran (2019)
 <doi:10.18637/jss.v091.i01>.

Package: r-cran-ddalpha
Description-md5: af0318f522e07cf8f483caac93d01df1
Description-en: Depth-Based Classification and Calculation of Data Depth
 Contains procedures for depth-based supervised learning, which are
 entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler
 and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training
 data sample is transformed by a statistical depth function to a compact
 low-dimensional space, where the final classification is done. It also offers
 an extension to functional data and routines for calculating certain
 notions of statistical depth functions. 50 multivariate and 5 functional
 classification problems are included. (Pokotylo, Mozharovskyi and Dyckerhoff,
 2019 <doi:10.18637/jss.v091.i05>).

Package: r-cran-ddrtree
Description-md5: fd4d4af375f7cf16fa58cb8bde3e3548
Description-en: GNU R learning principal graphs with DDRTree
 Provides an implementation of the framework of reversed graph embedding
 (RGE) which projects data into a reduced dimensional space while
 constructs a principal tree which passes through the middle of the data
 simultaneously. DDRTree shows superiority to alternatives (Wishbone,
 DPT) for inferring the ordering as well as the intrinsic structure of
 the single cell genomics data. In general, it could be used to
 reconstruct the temporal progression as well as bifurcation structure of
 any datatype.

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Package: r-cran-ctmcd
Description-md5: f2220a0fcf5b2f708021b6eee1996d6f
Description-en: GNU R estimating the parameters of a continuous-time Markov chain from
 This GNU R package provides estimating the parameters of a continuous-
 time Markov chain from Discrete-Time Data Functions for estimating
 Markov generator matrices from discrete-time observations. The
 implemented approaches comprise diagonal adjustment, weighted adjustment
 and quasi-optimization of matrix logarithm based candidate solutions, an
 expectation-maximization algorithm as well as a Gibbs sampler.

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Package: r-cran-contfrac
Description-md5: c6593cfe997a9e894c37e796365ec993
Description-en: GNU R package providing various utilities for evaluating continued fractions
 The contfrac package is a GNU R package providing various utilities for
 evaluating continued fractions.

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Package: r-cran-clue
Description-md5: ed3ae930880ac6ca4dade4a291a056a7
Description-en: GNU R cluster ensembles
 Cluster ensembles are collections of individual solutions to a given
 clustering problem which are useful or necessary to consider in a wide
 range of applications.  The R package~\pkg{clue} provides an
 extensible computational environment for creating and analyzing
 cluster ensembles, with basic data structures for representing
 partitions and hierarchies, and facilities for computing on these,
 including methods for measuring proximity and obtaining consensus and
 'secondary' clusterings.

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Package: r-cran-classint
Description-md5: 7e760a6ebbc0bc78e58955b9a42045f5
Description-en: GNU R Choose Univariate Class Intervals
 This package provides selected commonly used methods for choosing
 univariate class intervals for mapping or other graphics purposes
 for GNU R.

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Package: r-cran-circular
Description-md5: f9dc471b068c9b5aa07b221fcb5d2af4
Description-en: GNU R Circular Statistics
 Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao
 Jammalamadaka and A. SenGupta, World Scientific.

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Package: r-cran-catools
Description-md5: 75e47f2b987c1e092a227ae32f5db274
Description-en: GNU R package providing various utility functions
 The packages features several basic utility functions including:
 moving (rolling, running) window statistic functions, read/write for GIF
 and ENVI binary files, fast calculation of AUC, LogitBoost classifier, base64
 encoder/decoder, round-off error free sum and cumsum, etc.
 .
 This package of R programming tools for plotting data was written by
 Jarek Tuszynski.

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602418a
Package: r-cran-calculus
Description-md5: f19cfde618142ba518c18aca6aec8397
Description-en: High Dimensional Numerical and Symbolic Calculus
 Efficient C++ optimized functions for numerical and symbolic calculus as
 described in Guidotti (2022). It includes basic arithmetic, tensor calculus,
 Einstein summing convention, fast computation of the Levi-Civita symbol and
 generalized Kronecker delta, Taylor series expansion, multivariate Hermite
 polynomials, high-order derivatives, ordinary differential equations,
 differential operators (Gradient, Jacobian, Hessian, Divergence, Curl,
 Laplacian) and numerical integration in arbitrary orthogonal coordinate
 systems: cartesian, polar, spherical, cylindrical, parabolic or user defined
 by custom scale factors.

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Package: r-cran-brglm
Description-md5: 340c38cf9522c3d017fd5ca41007e4c6
Description-en: GNU R package for bias reduction in binomial-response GLMs
 Fit generalized linear models with binomial responses using either an
 adjusted-score approach to bias reduction or maximum penalized
 likelihood where penalization is by Jeffreys invariant prior. These
 procedures return estimates with improved frequentist properties
 (bias, mean squared error) that are always finite even in cases where
 the maximum likelihood estimates are infinite (data separation).
 Fitting takes place by fitting generalized linear models on
 iteratively updated pseudo-data. The interface is essentially the same
 as 'glm'.  More flexibility is provided by the fact that custom
 pseudo-data representations can be specified and used for model
 fitting. Functions are provided for the construction of confidence
 intervals for the reduced-bias estimates.

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Package: r-cran-bradleyterry2
Description-md5: 393bad89736fec31b56f123bc57d29de
Description-en: GNU R package for using Bradley-Terry models
 Specify and fit the Bradley-Terry model, including structured versions
 in which the parameters are related to explanatory variables through a
 linear predictor and versions with contest-specific effects, such as a
 home advantage.

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602343a
Package: r-cran-blockmodeling
Description-md5: 332ce708874a031a391ed5d12bab89db
Description-en: Generalized and classical blockmodeling of valued networks
 This R package is primarly meant as an implementation of Generalized
 blockmodeling for valued networks. In addition, measurese of similarity or
 dissimilarity based on structural equivalence and regular equivalence (REGE
 algorithm) can be computed and partitioned matrices can be ploted.

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Package: r-cran-bitops
Description-md5: c14b326da205c671a6adfbed4dc0b2f8
Description-en: GNU R package implementing bitwise operations
 Functions for bitwise operations on integer vectors.
 .
 This package of R programming tools for plotting data was written by
 Steve Dutky <sdutky@terpalum.umd.edu> with parts by Martin Maechler.

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Package: r-cran-bios2cor
Description-md5: 10485229e65564a9eee652af9ea00957
Description-en: GNU R from Biological Sequences and Simulations to Correlation
 Analysis Utilities for computation and analysis of
 correlation/covariation in multiple sequence alignments and in
 side chain motions during molecular dynamics simulations. Features
 include the computation of correlation/covariation scores using a
 variety of scoring functions between either sequence positions in
 alignments or side chain dihedral angles in molecular dynamics
 simulations and utilities to analyze the correlation/covariation
 matrix through a variety of tools including network representation
 and principal components analysis. In addition, several utility
 functions are based on the R graphical environment to provide
 friendly tools for help in data interpretation. Examples of
 sequence covariation analysis are provided in: (1) Pele J, Moreau
 M, Abdi H, Rodien P, Castel H, Chabbert M (2014)
 <doi:10.1002/prot.24570> and (2) Taddese B, Deniaud M, Garnier A,
 Tiss A, Guissouma H, Abdi H, Henrion D, Chabbert M (2018) <doi:
 10.1371/journal.pcbi.1006209>. An example of side chain correlated
 motion analysis is provided in: Taddese B, Garnier A, Abdi H,
 Henrion D, Chabbert M (2020) <doi: 10.1038/s41598-020-72766-
 1>. This work was supported by the French National Research
 Agency (Grant number: ANR-11-BSV2-026) and by GENCI (Grant
 number: 100567).

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Package: r-cran-bio3d
Description-md5: 5150a63a38e956e286904b0c1ebe5442
Description-en: GNU R package for biological structure analysis
 The bio3d package contains utilities to process, organize and explore
 protein structure, sequence and dynamics data.  Features include the
 ability to read and write structure, sequence and dynamic trajectory
 data, perform atom summaries, atom selection, re-orientation,
 superposition, rigid core identification, clustering, torsion analysis,
 distance matrix analysis, structure and sequence conservation analysis,
 and principal component analysis (PCA).  In addition, various utility
 functions are provided to enable the statistical and graphical power of
 the R environment to work with biological sequence and structural data.

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Package: r-cran-bigmemory
Description-md5: 69712ba529cb54600ff9cd43a747ecfa
Description-en: Manage Massive Matrices with Shared Memory and Memory-Mapped Files
 Create, store, access, and manipulate massive matrices.
 Matrices are allocated to shared memory and may use memory-mapped
 files.  Packages 'biganalytics', 'bigtabulate', 'synchronicity', and
 'bigalgebra' provide advanced functionality.

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Package: r-cran-biasedurn
Description-md5: a00c9a9f90451ef8f85040dea0193ae0
Description-en: GNU R Biased Urn model distributions
 Statistical models of biased sampling in the form of univariate and
 multivariate noncentral hypergeometric distributions, including
 Wallenius' noncentral hypergeometric distribution and Fisher's
 noncentral hypergeometric distribution (also called extended
 hypergeometric distribution). See vignette("UrnTheory") for explanation
 of these distributions.

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Package: r-cran-bbmisc
Description-md5: ecd252e856a44a89d5a4a5c2151fc35a
Description-en: GNU R Miscellaneous helper functions for B. Bischl
 Miscellaneous helper functions for and from B. Bischl and some other
 guys at TU Dortmund, mainly for package development.

Package: r-cran-bdgraph
Description-md5: e1b471b28a428f7222b8d3a86d9ed7c7
Description-en: GNU R bayesian structure learning in graphical models
 Statistical tools for Bayesian structure learning in undirected
 graphical models for continuous, discrete, and mixed data. The package
 is implemented the recent improvements in the Bayesian graphical models
 literature, including Mohammadi and Wit (2015) <doi:10.1214/14-BA889>,
 Mohammadi and Wit (2019) <doi:10.18637/jss.v089.i03>.

Package: r-cran-bdsmatrix
Description-md5: a23ed1ba520e9eaf29aa3d5f5196cb2a
Description-en: GNU R routines for block diagonal symmetric matrices
 Provides an R class for block diagonal symmetric matrices, allowing them to be
 manipulated much like an ordinary matrix, but the total memory use can be much
 smaller.
 .
 Also provides a solver for such matrices, and generalized Cholesky
 decomposition routines for both symmetric and block diagonal symmetric
 matrices.

Package: r-cran-beeswarm
Description-md5: e801bfc85e06afe6bb0ef703fe4ecca1
Description-en: bee swarm plot, an alternative to stripchart
 Beeswarm is an add-on package for the R statistical environment. The bee swarm
 plot is a one-dimensional scatter plot like "stripchart", but with
 closely-packed, non-overlapping points.

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Package: r-cran-base64url
Description-md5: 924f00e315526d2e7c4a68507f6bad50
Description-en: Fast and URL-Safe Base64 Encoder and Decoder
 In contrast to RFC3548, the 62nd character ("+") is replaced with "-",
 the 63rd character ("/") is replaced with "_". Furthermore, the encoder
 does not fill the string with trailing "=". The resulting encoded strings
 comply to the regular expression pattern "[A-Za-z0-9_-]" and thus are safe
 to use in URLs or for file names. The package also comes with a simple base32
 encoder/decoder suited for case insensitive file systems.

Package: r-cran-batchjobs
Description-md5: 7a30860771eed972664b07421befc54a
Description-en: GNU R batch computing
 This GNU R package Provides Map, Reduce and Filter variants to generate
 jobs on batch computing systems like PBS/Torque, LSF, SLURM and Sun Grid
 Engine.  Multicore and SSH systems are also supported. For further
 details see the project web page.

Package: r-cran-bayesfm
Description-md5: 46d1c4a09a99a7f20a1099229096521d
Description-en: GNU R Bayesian inference for factor modeling
 Collection of procedures to perform Bayesian analysis on a variety
 of factor models. Currently, it includes: Bayesian Exploratory Factor
 Analysis (befa), an approach to dedicated factor analysis with stochastic
 search on the structure of the factor loading matrix. The number of latent
 factors, as well as the allocation of the manifest variables to the factors,
 are not fixed a priori but determined during MCMC sampling.
 More approaches will be included in future releases of this package.

Package: r-cran-bayesm
Description-md5: 6f649751db2fffd16683aa065ef0eeca
Description-en: GNU R package for Bayesian inference
 The bayesm package covers many important models used in marketing and
 micro-econometrics applications. The package includes:
 .
  * Bayes Regression (univariate or multivariate dep var)
  * Multinomial Logit (MNL) and Multinomial Probit (MNP)
  * Multivariate Probit,
  * Multivariate Mixtures of Normals
  * Hierarchical Linear Models with normal prior and covariates
  * Hierarchical Multinomial Logits with mixture of normals prior and
    covariates
  * Bayesian analysis of choice-based conjoint data
  * Bayesian treatment of linear instrumental variables models
  * Analyis of Multivariate Ordinal survey data with scale usage heterogeneity
    (as in Rossi et al, JASA (01)).
 .
 For further reference, consult the authors' book, _Bayesian Statistics and
 Marketing_ by Allenby, McCulloch and Rossi.

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Package: r-cran-askpass
Description-md5: c9e8bbb63c6ebce7143cf9dd2403e753
Description-en: safe password entry for GNU R, Git, and SSH
 Cross-platform utilities for prompting the user for credentials or a
 passphrase, for example to authenticate with a server or read a protected key.
 Includes native programs for MacOS and Windows, hence no 'tcltk' is required.
 Password entry can be invoked in two different ways: directly from R via the
 askpass() function, or indirectly as password-entry back-end for 'ssh-agent'
 or 'git-credential' via the SSH_ASKPASS and GIT_ASKPASS environment variables.
 Thereby the user can be prompted for credentials or a passphrase if needed
 when R calls out to git or ssh.

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Package: r-cran-apcluster
Description-md5: 840bdbe0cbb3f8c965e0ecc852eac124
Description-en: Affinity Propagation Clustering
 Implements Affinity Propagation clustering introduced by Frey and
 Dueck (2007) <DOI:10.1126/science.1136800>. The algorithms are largely
 analogous to the 'Matlab' code published by Frey and Dueck.
 The package further provides leveraged affinity propagation and an
 algorithm for exemplar-based agglomerative clustering that can also be
 used to join clusters obtained from affinity propagation. Various
 plotting functions are available for analyzing clustering results.

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Package: r-cran-actuar
Description-md5: 1269abecbc5217e3d1112b1f81d0c154
Description-en: GNU R actuarial functions and heavy tailed distributions
 Functions and data sets for actuarial science:
 modeling of loss distributions; risk theory and ruin theory;
 simulation of compound models, discrete mixtures and compound
 hierarchical models; credibility theory. Support for many additional
 probability distributions to model insurance loss amounts and loss
 frequency: 19 continuous heavy tailed distributions; the
 Poisson-inverse Gaussian discrete distribution; zero-truncated and
 zero-modified extensions of the standard discrete distributions.
 Support for phase-type distributions commonly used to compute ruin
 probabilities.

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Package: libqgispython3.44.11
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Package: libqgisgrass8-3.44.11
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Package: libqgis-server3.44.11
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Package: libqgis-native3.44.11
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Package: libqgis-gui3.44.11
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Package: libqgis-core3.44.11
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Package: libqgis-app3.44.11
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Package: libqgis-analysis3.44.11
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Package: libqgis-3d3.44.11
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Package: python3-torchvision
Description-md5: 721d4eb100852b56685353adbfea1933
Description-en: Datasets, Transforms and Models specific to Computer Vision
 The torchvision package consists of popular datasets, model architectures, and
 common image transformations for computer vision.

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Package: libtorch2.12
Description-md5: 2af9ff00ffc5dab05134d4e047aa0a15
Description-en: Tensors and Dynamic neural networks in Python (Shared Objects)
 PyTorch is a Python package that provides two high-level features:
 .
 (1) Tensor computation (like NumPy) with strong GPU acceleration
 (2) Deep neural networks built on a tape-based autograd system
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 You can reuse your favorite Python packages such as NumPy, SciPy and Cython
 to extend PyTorch when needed.
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 This is the CPU-only version of PyTorch (Shared Objects).

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Package: paleomix
Description-md5: 7cb3c4aa709e51b480a41a73961d7253
Description-en: pipelines and tools for the processing of ancient and modern HTS data
 The PALEOMIX pipelines are a set of pipelines and tools designed to aid
 the rapid processing of High-Throughput Sequencing (HTS) data: The BAM
 pipeline processes de-multiplexed reads from one or more samples,
 through sequence processing and alignment, to generate BAM alignment
 files useful in downstream analyses; the Phylogenetic pipeline carries
 out genotyping and phylogenetic inference on BAM alignment files, either
 produced using the BAM pipeline or generated elsewhere; and the Zonkey
 pipeline carries out a suite of analyses on low coverage equine
 alignments, in order to detect the presence of F1-hybrids in
 archaeological assemblages. In addition, PALEOMIX aids in metagenomic
 analysis of the extracts.
 .
 The pipelines have been designed with ancient DNA (aDNA) in mind, and
 includes several features especially useful for the analyses of ancient
 samples, but can all be for the processing of modern samples, in order
 to ensure consistent data processing.

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Package: mimetex
Description-md5: c0904eca2683f2e822c853f4b54e8ae2
Description-en: LaTeX math expressions to anti-aliased GIF images converter
 mimeTex parses LaTeX math expressions emitting directly GIF images
 without intermediate dvi-to-gif conversion.
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 mimeTex is a standalone program that doesn't use TeX in any way, a kind
 of lightweight alternative to MathML

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Package: mapdamage
Description-md5: 12a591215e4baf8b82a558bce253c33a
Description-en: tracking and quantifying damage patterns in ancient DNA sequences
 MapDamage is a computational framework written in Python and R, which
 tracks and quantifies DNA damage patterns among ancient DNA sequencing
 reads generated by Next-Generation Sequencing platforms.
 .
 MapDamage is developed at the Centre for GeoGenetics by the
 Orlando Group.

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Package: r-cran-gregmisc
Description-md5: 1e0d3b742d56ff44fd34d9c6952f02f2
Description-en: GNU R package with miscellaneous functions by Greg Warnes et al
 This package of miscellaneous functions for GNU R that written by
 Gregory Warnes with code contributions by Ben Bolker, Bendix
 Carstensen, Don MacQueen, William Venables, Marc Schwartz, Ben
 Bolker, Ian Wilson, Kjetil Halvorsen, Arni Magnusson, Lodewijk
 Bonebakker, and others.
 .
 Most of the function in the gregmisc library fall into six general areas:
 permutations and combinations, tools for linear models, plots, data
 manipulation, and fixed or extended versions of existing functions.
 .
 Note that as of version 2.0.8, gregmisc's code is now provided by the
 new packages gdata, gmodels, gplots and gtools which contain all the
 code formerly provided by gregmisc.  Unless any of your R code still
 loads the gregmisc package directly, and you thus need it to load the
 component packages, you can remove this package.
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Package: r-cran-gplots
Description-md5: cce2aa54adae41d5882d87f324c74d12
Description-en: GNU R package with tools for plotting data by Greg Warnes et al
 This package of R programming tools for plotting data was written by
 Gregory Warnes with code contributions by Ben Bolker and Thomas
 Lumley.
 .
 This package used to be part of the gregmisc bundle packaged for Debian
 as r-cran-gregmisc.

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 actions-proto-go is a Go library for protobuf as used by Gitea actions.
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Description-md5: 67cf9082b24a37610660e6546d5cad96
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Package: r-cran-gmodels
Description-md5: 136997ab5d1a30ab6beed422f75a10f5
Description-en: GNU R package with tools for model fitting by Greg Warnes et al
 This package of R programming tools for model fitting was written by
 Gregory Warnes with code contributions by Ben Bolker and Thomas
 Lumley.
 .
 This package used to be part of the gregmisc bundle packaged for Debian
 as r-cran-gregmisc.

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