k3ylabs/cosoco-image-dataset huggingface dataset:
Package: r-cran-maxlik (1.5-2-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=maxLik
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-misctools (>= 0.6-8), r-cran-sandwich, r-cran-generics
Description:  GNU R maximum likelihood estimation This GNU R package provides tools for maximum likelihood estimation.
Package: r-cran-maxstat (0.7-25-2build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=maxstat
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-exactranktests (>= 0.8-23), r-cran-mvtnorm (>= 0.5-10), libc6 (>= 2.2.5)
Description:  GNU R maximally selected rank statistics This GNU R package provides functions to investigate maximally selected rank statistics with several p-value approximations.
Package: r-cran-mclust (5.4.9-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mclust
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3
Description:  Gaussian Mixture Modelling for Model-Based Clustering This GNU R package supports Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation. . Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
Package: r-cran-mclustcomp (0.3.3-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mclustcomp
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo, libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9)
Description:  Measures for Comparing Clusters Given a set of data points, a clustering is defined as a disjoint partition where each pair of sets in a partition has no overlapping elements. This package provides 25 methods that play a role somewhat similar to distance or metric that measures similarity of two clusterings - or partitions. For a more detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.
Package: r-cran-mcmc (0.9-7-1build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mcmc
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, libc6 (>= 2.4)
Description:  GNU R package for Markov Chain Monte Carlo simulations Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC). Users specify the distribution by an R function that evaluates the log unnormalized density. Algorithms are random walk Metropolis algorithm (function metrop), simulated tempering (function temper), and morphometric random walk Metropolis (Johnson and Geyer, Annals of Statistics, 2012, function morph.metrop), which achieves geometric ergodicity by change of variable.
Package: r-cran-mcmcpack (1.6-0-1)
Section: universe/math
Homepage: https://cran.r-project.org/package=MCMCpack
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-coda (>= 0.11-3), r-cran-mass, r-cran-lattice, r-cran-mcmc, r-cran-quantreg, libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 11)
Description:  R routines for Markov chain Monte Carlo model estimation This is a set of routines for GNU R that implement various statistical and econometric models using Markov chain Monte Carlo (MCMC) estimation, which allows "solving" models that would otherwise be intractable with traditional techniques, particularly problems in Bayesian statistics (where one or more "priors" are used as part of the estimation procedure, instead of an assumption of ignorance about the "true" point estimates), although MCMC can also be used to solve frequentist statistical problems with uninformative priors.  MCMC techniques are also preferable over direct estimation in the presence of missing data. . Currently implemented are a number of ecological inference (EI) routines (for estimating individual-level attributes or behavior from aggregate data, such as electoral returns or census results), as well as models for traditional linear panel and cross-sectional data, some visualization routines for EI diagnostics, two item-response theory (or ideal-point estimation) models, metric, ordinal, and mixed-response factor analysis, and models for Gaussian (linear) and Poisson regression, logistic regression (or logit), and binary and ordinal-response probit models. . The suggested packages (r-cran-bayesm, -eco, and -mnp) contain additional models that may also be useful for those interested in this package.
Package: r-cran-mda (0.5-2-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mda
Depends: r-base-core (>= 4.0.2-1), r-api-4.0, r-cran-class, libc6 (>= 2.29)
Description:  GNU R mixture and flexible discriminant analysis This package provides GNU R mixture and flexible discriminant analysis, multivariate adaptive regression splines (MARS), BRUTO, ...
Package: r-cran-medadherence (1.03-6)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/src/contrib/Archive/medAdherence/
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, libc6 (>= 2.14)
Description:  GNU R Medication Adherence: Commonly Used Definitions Adherence is defined as "the extent to which a person's behavior coincides with medical or health advice", which is very important, for both clinical researchers and physicians, to identify the treatment effect of a specific medication(s). . A variety of measures have been developed to calculate the medication adherence. Definitions and methods to address adherence differ greatly in public literature. Choosing which definition should be determined by overall study goals.  This package provides the functions to calculate medication adherence based on commonly used definitions.
Package: r-cran-mediana (1.0.8-3build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=Mediana
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-mass, r-cran-mvtnorm, r-cran-survival
Description:  clinical trial simulations Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.
Package: r-cran-memoise (2.0.1-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=memoise
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-rlang (>= 0.4.10), r-cran-cachem
Description:  Memoise functions Cache the results of a function so that when you call it again with the same arguments it returns the pre-computed value.
Package: r-cran-mertools (0.5.2-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=merTools
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-arm, r-cran-lme4 (>= 1.1-11), r-cran-dplyr, r-cran-mvtnorm, r-cran-foreach, r-cran-shiny, r-cran-abind, r-cran-ggplot2, r-cran-blme, r-cran-broom.mixed
Description:  GNU R tools for analyzing mixed effect regression models Provides methods for extracting results from mixed-effect model objects fit with the 'lme4' package. Allows construction of prediction intervals efficiently from large scale linear and generalized linear mixed-effects models.
Package: r-cran-metafor (3.0-2-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=metafor
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-matrix, r-cran-nlme, r-cran-mathjaxr, r-cran-pbapply
Description:  Meta-Analysis Package for R A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, GOSH plots). For meta-analyses of binomial and person- time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted.
Package: r-cran-metamix (0.3-2build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=metaMix
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-data.table (>= 1.9.2), r-cran-matrix, r-cran-gtools, r-cran-rmpi, r-cran-ggplot2, libc6 (>= 2.2.5), libstdc++6 (>= 4.9), libopenmpi-dev
Description:  GNU R bayesian mixture analysis for metagenomic community profiling Resolves complex metagenomic mixtures by analysing deep sequencing data, using a mixture model based approach. The use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species more likely to contribute to the mixture.
Package: r-cran-metap (1.7-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=metap
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-lattice, r-cran-rdpack (>= 0.7), r-cran-tfisher, r-cran-mutoss, r-cran-mathjaxr (>= 0.8-3)
Description:  Meta-Analysis of Significance Values The canonical way to perform meta-analysis involves using effect sizes. When they are not available this package provides a number of methods for meta-analysis of significance values including the methods of Edgington, Fisher, Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results; and a routine for graphical display.
Package: r-cran-metrics (0.1.4-2build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=Metrics
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0
Description:  GNU R evaluation metrics for machine learning An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.
Package: r-cran-mets (1.2.9+dfsg-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mets
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-timereg (>= 1.9.4), r-cran-lava (>= 1.6.6), r-cran-mvtnorm, r-cran-numderiv, r-cran-rcpp, r-cran-survival (>= 2.43-1), r-cran-rcpparmadillo, libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9)
Description:  GNU R analysis of multivariate event times Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and  bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Also contains two-stage binomial modelling that can do pairwise odds-ratio dependence modelling based marginal logistic regression models. This is an alternative to the alternating logistic regression approach (ALR).
Package: r-cran-mfilter (0.1.5-2build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mFilter
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0
Description:  GNU R package providing miscellaneous time series filters The package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series. The routines are commonly used in economics and finance, however they should also be interest to other areas. Currently, Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and trigonometric regression filters are included in the package.
Package: r-cran-mgcv (1.8-39-1)
Section: universe/math
Homepage: https://cran.r-project.org/package=mgcv
Depends: r-base-core (>= 4.1.2-1ubuntu2), r-api-4.0, r-cran-nlme (>= 3.1-64), r-cran-matrix, libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), liblapack3 | liblapack.so.3
Description:  GNU R package for multiple parameter smoothing estimation This package provides functions for generalised additive models (GAMs) and other generalized ridge regression problems with multiple smoothing parameter selection by GCV or UBRE. It includes an implementation (not a clone) of gam(). . This package is part of the set of packages that are 'recommended' by R Core and shipped with upstream source releases of R itself.
Package: r-cran-mi (1.0-8build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mi
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-matrix, r-cran-arm (>= 1.4-11)
Description:  GNU R package for Missing Data Imputation and Model Checking -- mi The mi R package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
Package: r-cran-mice (3.14.0-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mice
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-generics, r-cran-lattice, r-cran-rcpp, r-cran-rlang, r-cran-tidyr, r-cran-withr, r-cran-cpp11, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2)
Description:  GNU R multivariate imputation by chained equations Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
Package: r-cran-mime (0.12-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mime
Depends: r-base-core (>= 4.1.1-2), r-api-4.0
Description:  R package which maps filenames to MIME Types Guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems.
Package: r-cran-minerva (1.5.10-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=minerva
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9)
Description:  Maximal Information-Based Nonparametric Exploration Wrapper for 'minepy' implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of 'minepy' can be found at <http://minepy.readthedocs.io/en/latest>.
Package: r-cran-miniui (0.1.1.1-3build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=miniUI
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-shiny (>= 0.13), r-cran-htmltools (>= 0.3)
Description:  Shiny UI Widgets for Small Screens Provides UI widget and layout functions for writing Shiny apps that work well on small screens. Designed to work especially well for creating Shiny Gadgets.
Package: r-cran-minpack.lm (1.2-1-5)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=minpack.lm
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, libblas3 | libblas.so.3, libc6 (>= 2.4), libminpack1
Description:  GNU R Levenberg-Marquardt nonlinear least-squares algorithm found in MINPACK The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds. The implementation can be used via nls- like calls using the nlsLM function.
Package: r-cran-minqa (1.2.4-1build4)
Section: universe/gnu-r
Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-rcpp (>= 0.11.0)
Description:  GNU R package for quadratic optimisation without derivatives The minqa package implements derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by MJD Powell.
Package: r-cran-misc3d (0.9-1-1)
Section: universe/math
Homepage: https://cran.r-project.org/package=misc3d
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-rgl
Description:  GNU R collection of 3d plot functions and rgl-based isosurfaces This CRAN package provides a collection of miscellaneous 3d plots, including rgl-based isosurfaces: .  contour3d     Uses rgl to render isosurfaces, or three-dimensional                contours, computed by the marching cubes algorithm. .  image3d       Crude 3d analog of image() using rgl to plot points on a                three dimensional grid representing values in a three                dimensional array. Assumes high values are inside and                uses alpha blending to make outside points more                transparent. .  lines3d       Draws connected line segments in an rgl window. .  parametric3d  Plots a two-parameter surface in three dimensions in                rgl.  Based on Mathematica's Param3D .  slices3d      Uses tkrplot to create an interactive slice view of three or                four dimensional volume data, such as MRI data.
Package: r-cran-misctools (0.6-26-2build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=miscTools
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-digest
Description:  GNU R miscellaneous tools and utilities Many of them facilitate the work with matrices, e.g. inserting rows or columns, creating symmetric matrices, or checking for semidefiniteness. Other tools facilitate the work with regression models, e.g. extracting the standard errors, obtaining the number of (estimated) parameters, or calculating R-squared values.
Package: r-cran-mitml (0.4-3-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mitml
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-pan, r-cran-jomo, r-cran-haven
Description:  GNU R tools for multiple imputation in multilevel modeling Provides tools for multiple imputation of missing data in multilevel modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo', and several functions for visualization, data management and the analysis of multiply imputed data sets.
Package: r-cran-mitools (2.4-2build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mitools
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-dbi
Description:  GNU R tools for multiple imputation of missing data This GNU R package provides tools to perform analyses and combine results from multiple-imputation datasets.
Package: r-cran-mixsqp (0.3-43-2)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mixsqp
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-cran-irlba, r-cran-rcpp (>= 0.12.15), r-cran-rcpparmadillo, libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9)
Description:  Quadratic Programming for Estimation of Mixture Proportions Provides an optimization method based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function "KWDual" in the 'REBayes' package), and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large. This implements the "mix-SQP" algorithm, with some improvements, described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2020) <DOI:10.1080/10618600.2019.1689985>.
Package: r-cran-mixtools (1.2.0-1build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mixtools
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-kernlab, r-cran-mass, r-cran-segmented, r-cran-survival, libc6 (>= 2.4)
Description:  GNU R tools for analyzing finite mixture models The GNU R mixtools package is a collection of R functions for analyzing finite mixture models. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.
Package: r-cran-mlbench (2.1-3-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mlbench
Depends: r-base-core (>= 4.0.3-1), r-api-4.0
Description:  GNU R Machine Learning Benchmark Problems This GNU R package provices a collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.
Package: r-cran-mlmetrics (1.1.1-3build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=MLmetrics
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-rocr
Description:  GNU R machine learning evaluation metrics This package contains a collection of evaluation metrics for GNU R, including loss, score and utility functions, that measure regression, classification and ranking performance.
Package: r-cran-mlmrev (1.0-8-2build1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mlmRev
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-lme4
Description:  GNU R Examples from Multilevel Modelling Software Review This GNU R package provides data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.
Package: r-cran-mlr (2.19.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://cran.r-project.org/package=mlr
Depends: r-base-core (>= 4.1.1-2), r-api-4.0, r-cran-paramhelpers (>= 1.10), r-cran-backports (>= 1.1.0), r-cran-bbmisc (>= 1.11), r-cran-checkmate (>= 1.8.2), r-cran-data.table (>= 1.12.4), r-cran-ggplot2, r-cran-parallelmap (>= 1.3), r-cran-stringi, r-cran-survival, r-cran-xml
Description:  Machine learning in GNU R Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.
Package: r-cran-mnormt (2.0.2-1)
Section: universe/math
Homepage: https://cran.r-project.org/package=mnormt
Depends: libc6 (>= 2.29), r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-tmvnsim (>= 1.0-2)
Description:  GNU R package providing multivariate normal and t distribution This package provides functions for computing the density and the distribution function of, and for generating random vectors from the multivariate normal and multivariate t distributions.  It provides functions similar in scope to those of the package 'mvtnorm', but with some differences; one of these is that probabilities are computed via a non-Monte Carlo method.