607915a

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>.
.
607725a
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'.

.
607718a
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.

.
606445a
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.

.
605792a
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.

.
605781a
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.

.
604627a
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.

.
603349a
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.

.
603112a

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.)
.
602857a
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.

.
602845a
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.

.
602762a
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.

.
602727a
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.

.
602610a
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.

.
602547a
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.

.
602521a
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.

.
602466a
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.

.
602437a
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.

.
602378a
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.

.
602371a
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.

.
602362a
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.

.
602341a
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.

.
602301a
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).

.
602296a
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.

.
602284a
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.

.
602266a
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.

.
602257a
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.

.
602248a
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.

.
602207a
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.

.
602171a
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.

.
602102a
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.

.
591679c
Package: libqgispython3.44.11
.
591671c
Package: libqgisgrass8-3.44.11
.
591663c
Package: libqgis-server3.44.11
.
591655c
Package: libqgis-native3.44.11
.
591647c
Package: libqgis-gui3.44.11
.
591622c
Package: libqgis-core3.44.11
.
591614c
Package: libqgis-app3.44.11
.
591606c
Package: libqgis-analysis3.44.11
.
591598c
Package: libqgis-3d3.44.11
.
589477a
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.

.
589368a
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
 .
 You can reuse your favorite Python packages such as NumPy, SciPy and Cython
 to extend PyTorch when needed.
 .
 This is the CPU-only version of PyTorch (Shared Objects).

.
444667a
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.
 .
 mimeTex is a standalone program that doesn't use TeX in any way, a kind
 of lightweight alternative to MathML

.
219733a

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.
.
216962a
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.

.
187233,187234c
 actions-proto-go is a Go library for protobuf as used by Gitea actions.
.
187231c
Description-md5: 67cf9082b24a37610660e6546d5cad96
.
179824a
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.

.
46957,46962d
