conda-forge / packages

Package Name Access Summary Updated
r-tseries public Time series analysis and computational finance. 2019-08-20
r-rrcov public Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point. 2019-08-20
r-ddalpha public 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. 2019-08-20
r-hmisc public Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, and recoding variables. 2019-08-20
r-fields public For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research. See the Fields URL for a vignette on using this package and some background on spatial statistics. 2019-08-20
r-vegan public Ordination methods, diversity analysis and other functions for community and vegetation ecologists. 2019-08-20
r-quantreg public Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also included. 2019-08-20
r-randomfieldsutils public Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used. Furthermore, it includes the Struve functions. 2019-08-20
r-iso public Linear order and unimodal order (univariate) isotonic regression; bivariate isotonic regression with linear order on both variables. 2019-08-20
r-logspline public Contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e. the Kooperberg and Stone (1992) algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997) <DOI:10.1214/aos/1031594728>. 2019-08-20
r-lsei public It contains functions that solve least squares linear regression problems under linear equality/inequality constraints. Functions for solving quadratic programming problems are also available, which transform such problems into least squares ones first. It is developed based on the 'Fortran' program of Lawson and Hanson (1974, 1995), which is public domain and available at <http 2019-08-20
r-catboost public CatBoost is a machine learning algorithm that uses gradient boosting on decision trees. It is available as an open source library. 2019-08-20
mpi4py public Python bindings for MPI 2019-08-20
catboost public Gradient boosting on decision trees library 2019-08-20
soupsieve public A modern CSS selector implementation for BeautifulSoup 2019-08-20
r-amap public Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions). 2019-08-20
r-ash public David Scott's ASH routines ported from S-PLUS to R. 2019-08-20
r-fastcluster public This is a two-in-one package which provides interfaces to both R and Python. It 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. 2019-08-20
r-classint public Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes. 2019-08-20
cmake public CMake is an extensible, open-source system that manages the build process 2019-08-20
r-sm public This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press. 2019-08-20
r-statmod public A collection of algorithms and functions to aid statistical modeling. Includes growth curve comparisons, limiting dilution analysis (aka ELDA), mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models. Includes advanced generalized linear model functions that implement secure convergence, dispersion modeling and Tweedie power-law families. 2019-08-20
r-spam public Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). 2019-08-20
r-qap public Implements heuristics for the Quadratic Assignment Problem (QAP). Currently only a simulated annealing heuristic is available. 2019-08-20
r-pan public Multiple imputation for multivariate panel or clustered data. 2019-08-20
r-flashclust public Fast implementation of hierarchical clustering 2019-08-20
r-mvtnorm public Computes multivariate normal and t probabilities, quantiles, random deviates and densities. 2019-08-20
r-lars public No Summary 2019-08-20
r-nlme public Fit and compare Gaussian linear and nonlinear mixed-effects models. 2019-08-20
r-cluster public Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data". 2019-08-20
r-minqa public No Summary 2019-08-20
r-kernsmooth public No Summary 2019-08-20
r-mnormt public No Summary 2019-08-20
r-sparsem public Some basic linear algebra functionality for sparse matrices is provided: including Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products. 2019-08-20
r-stanheaders public The C++ header files of the Stan project are provided by this package, but it contains no R code or function documentation. There is a shared object containing part of the 'CVODES' library, but it is not accessible from R. 'StanHeaders' is only useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models. 2019-08-20
r-robustbase public "Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006. 2019-08-20
r-quadprog public This package contains routines and documentation for solving quadratic programming problems. 2019-08-20
r-lmtest public A collection of tests, data sets, and examples for diagnostic checking in linear regression models. Furthermore, some generic tools for inference in parametric models are provided. 2019-08-20
r-dotcall64 public An alternative version of .C() and .Fortran() supporting long vectors and 64-bit integer type arguments. The provided interface .C64() features mechanisms the avoid unnecessary copies of read-only or write-only arguments. This makes it a convenient and fast interface to C/C++ and Fortran code. 2019-08-20
r-acepack public No Summary 2019-08-20
r-nleqslv public Solve a system of nonlinear equations using a Broyden or a Newton method with a choice of global strategies such as line search and trust region. There are options for using a numerical or user supplied Jacobian, for specifying a banded numerical Jacobian and for allowing a singular or ill-conditioned Jacobian. 2019-08-20
r-expm public Computation of the matrix exponential, logarithm, sqrt, and related quantities. 2019-08-20
r-leaps public No Summary 2019-08-20
r-deldir public Calculates the Delaunay triangulation and the Dirichlet or Voronoi tessellation (with respect to the entire plane) of a planar point set. Plots triangulations and tessellations in various ways. Clips tessellations to sub-windows. Calculates perimeters of tessellations. Summarises information about the tiles of the tessellation. 2019-08-20
r-splus2r public Currently there are many functions in S-PLUS that are missing in R. To facilitate the conversion of S-PLUS packages to R packages, this package provides some missing S-PLUS functionality in R. 2019-08-20
r-ttr public Functions and data to construct technical trading rules with R. 2019-08-20
r-polspline public Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors. 2019-08-20
r-randomforest public Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>. 2019-08-20
r-ucminf public An algorithm for general-purpose unconstrained non-linear optimization. The algorithm is of quasi-Newton type with BFGS updating of the inverse Hessian and soft line search with a trust region type monitoring of the input to the line search algorithm. The interface of 'ucminf' is designed for easy interchange with 'optim'. 2019-08-20
r-nnls public An R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS). Also allows the combination of non-negative and non-positive constraints. 2019-08-20
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