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r-rblpapi
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public |
An R Interface to 'Bloomberg' is provided via the 'Blp API'.
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2024-01-16 |
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r-rbibutils
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public |
Read and write 'Bibtex' files. Convert between bibliography formats, including 'Bibtex', 'Biblatex', 'PubMed', 'Endnote', and 'Bibentry'. Includes a port of the 'bibutils' utilities by Chris Putnam <https://sourceforge.net/projects/bibutils/>. Supports all bibliography formats and character encodings implemented in 'bibutils'.
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2024-01-16 |
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r-rbf
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public |
A robust backfitting algorithm for additive models based on (robust) local polynomial kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and it computes predictions for points outside the training set if desired. See Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> and Martinez and Salibian-Barrera (2021) <doi:10.21105/joss.02992> for details.
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2024-01-16 |
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r-rbeast
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public |
Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.
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2024-01-16 |
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r-raptr
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public |
Biodiversity is in crisis. The overarching aim of conservation is to preserve biodiversity patterns and processes. To this end, protected areas are established to buffer species and preserve biodiversity processes. But resources are limited and so protected areas must be cost-effective. This package contains tools to generate plans for protected areas (prioritizations), using spatially explicit targets for biodiversity patterns and processes. To obtain solutions in a feasible amount of time, this package uses the commercial 'Gurobi' software (obtained from <https://www.gurobi.com/>). For more information on using this package, see Hanson et al. (2018) <doi:10.1111/2041-210X.12862>.
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2024-01-16 |
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r-rbart
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public |
A model of the form Y = f(x) + s(x) Z is fit where functions f and s are modeled with ensembles of trees and Z is standard normal. This model is developed in the paper 'Heteroscedastic BART Via Multiplicative Regression Trees' (Pratola, Chipman, George, and McCulloch, 2019, <arXiv:1709.07542v2>). BART refers to Bayesian Additive Regression Trees. See the R-package 'BART'. The predictor vector x may be high dimensional. A Markov Chain Monte Carlo (MCMC) algorithm provides Bayesian posterior uncertainty for both f and s. The MCMC uses the recent innovations in Efficient Metropolis--Hastings proposal mechanisms for Bayesian regression tree models (Pratola, 2015, Bayesian Analysis, <doi:10.1214/16-BA999>).
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2024-01-16 |
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r-rbacon
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public |
An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information on accumulation rates and their variability. See Blaauw & Christen (2011).
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2024-01-16 |
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r-raverage
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public |
Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.
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2024-01-16 |
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r-rasterkernelestimates
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public |
Performs kernel based estimates on in-memory raster images from the raster package. These kernel estimates include local means variances, modes, and quantiles. All results are in the form of raster images, preserving original resolution and projection attributes.
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2024-01-16 |
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r-randomlca
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public |
Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.
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2024-01-16 |
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r-raschsampler
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public |
MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
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2024-01-16 |
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r-rare
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public |
Implementation of an alternating direction method of multipliers algorithm for fitting a linear model with tree-based lasso regularization, which is proposed in Algorithm 1 of Yan and Bien (2018) <arXiv:1803.06675>. The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free.
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2024-01-16 |
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r-rappdirs
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public |
An easy way to determine which directories on the users computer you should use to save data, caches and logs. A port of Python's 'Appdirs' (<https://github.com/ActiveState/appdirs>) to R.
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2024-01-16 |
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r-rapiserialize
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public |
Access to the internal R serialization code is provided for use by other packages at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package. This packages builds on the Rhpc package by Ei-ji Nakama and Junji Nakano which also includes a (partial) copy of the file src/main/serialize.c from R itself. The R Core group is the original author of the serialization code made available by this package.
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2024-01-16 |
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r-rankcluster
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public |
Implementation of a model-based clustering algorithm for ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). Multivariate rankings as well as partial rankings are taken into account. This algorithm is based on an extension of the Insertion Sorting Rank (ISR) model for ranking data, which is a meaningful and effective model parametrized by a position parameter (the modal ranking, quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity of the rank population is modelled by a mixture of ISR, whereas conditional independence assumption is considered for multivariate rankings.
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2024-01-16 |
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r-rapidjsonr
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public |
Provides JSON parsing capability through the 'Rapidjson' 'C++' header-only library.
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2024-01-16 |
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r-rann
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public |
Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L2 (Euclidean) metric. Please see package 'RANN.L1' for the same functionality using the L1 (Manhattan, taxicab) metric.
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2024-01-16 |
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r-rapidatetime
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public |
Access to the C-level R date and 'datetime' code is provided for C-level API use by other packages via registration of native functions. Client packages simply include a single header 'RApiDatetime.h' provided by this package, and also 'import' it. The R Core group is the original author of the code made available with slight modifications by this package.
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2024-01-16 |
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r-ranger
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public |
A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed.
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2024-01-16 |
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r-rankaggreg
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public |
Performs aggregation of ordered lists based on the ranks using several different algorithms: Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a brute force algorithm (for small problems).
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2024-01-16 |
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r-randomforest
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None |
Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.
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2024-01-16 |
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r-randtoolbox
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public |
Provides (1) pseudo random generators - general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm (<doi:10.1007/978-3-540-74496-2_36>) and WELL (<doi:10.1145/1132973.1132974>) generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some generator tests - the gap test, the serial test, the poker test, see, e.g., Gentle (2003) <doi:10.1007/b97336>. Take a look at the Distribution task view of types and tests of random number generators. The package can be provided without the 'rngWELL' dependency on demand. Package in Memoriam of Diethelm and Barbara Wuertz.
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2024-01-16 |
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r-quickmatch
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public |
Provides functions for constructing near-optimal generalized full matching. Generalized full matching is an extension of the original full matching method to situations with more intricate study designs. The package is made with large data sets in mind and derives matches more than an order of magnitude quicker than other methods.
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2024-01-16 |
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r-randomforestsrc
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public |
Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes. Extreme random forests and randomized splitting. Suite of imputation methods for missing data. Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance. Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.
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2024-01-16 |
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r-randomizr
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public |
Generates random assignments for common experimental designs and random samples for common sampling designs.
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2024-01-16 |
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r-ramsvm
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public |
Provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning. C. Zhang, Y. Liu, J. Wang and H. Zhu. (2016) <doi:10.1080/10618600.2015.1043010>.
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2024-01-16 |
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r-ramcmc
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public |
Function for adapting the shape of the random walk Metropolis proposal as specified by robust adaptive Metropolis algorithm by Vihola (2012) <doi:10.1007/s11222-011-9269-5>. The package also includes fast functions for rank-one Cholesky update and downdate. These functions can be used directly from R or the corresponding C++ header files can be easily linked to other R packages.
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2024-01-16 |
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r-ramp
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public |
Provides an efficient procedure for fitting the entire solution path for high-dimensional regularized quadratic generalized linear models with interactions effects under the strong or weak heredity constraint.
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2024-01-16 |
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r-qwraps2
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public |
A collection of (wrapper) functions the creator found useful for quickly placing data summaries and formatted regression results into '.Rnw' or '.Rmd' files. Functions for generating commonly used graphics, such as receiver operating curves or Bland-Altman plots, are also provided by 'qwraps2'. 'qwraps2' is a updated version of a package 'qwraps'. The original version 'qwraps' was never submitted to CRAN but can be found at <https://github.com/dewittpe/qwraps/>. The implementation and limited scope of the functions within 'qwraps2' <https://github.com/dewittpe/qwraps2/> is fundamentally different from 'qwraps'.
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2024-01-16 |
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r-radviz
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public |
An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Ankerst *et al.* (1996) (<https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.1811>) for original implementation, see Di Caro *et al* (2012) (<https://link.springer.com/chapter/10.1007/978-3-642-13672-6_13>) for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.
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2024-01-16 |
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r-r2pmml
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public |
R wrapper for the JPMML-R library <https://github.com/jpmml/jpmml-r>, which converts R models to Predictive Model Markup Language (PMML).
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2024-01-16 |
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r-r2bayesx
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public |
An R interface to estimate structured additive regression (STAR) models with 'BayesX'.
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2024-01-16 |
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r-qz
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public |
Generalized eigenvalues and eigenvectors use QZ decomposition (generalized Schur decomposition). The decomposition needs an N-by-N non-symmetric matrix A or paired matrices (A,B) with eigenvalues reordering mechanism. The decomposition functions are mainly based Fortran subroutines in complex*16 and double precision of LAPACK library (version 3.10.0 or later).
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2024-01-16 |
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r-quotedargs
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public |
A facility for writing functions that quote their arguments, may sometimes evaluate them in the environment where they were quoted, and may pass them as quoted to other functions.
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2024-01-16 |
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r-quanteda.textstats
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public |
Textual statistics functions formerly in the 'quanteda' package. Textual statistics for characterizing and comparing textual data. Includes functions for measuring term and document frequency, the co-occurrence of words, similarity and distance between features and documents, feature entropy, keyword occurrence, readability, and lexical diversity. These functions extend the 'quanteda' package and are specially designed for sparse textual data.
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2024-01-16 |
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r-quanteda.textmodels
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public |
Scaling models and classifiers for sparse matrix objects representing textual data in the form of a document-feature matrix. Includes original implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>, 'Wordscores' model, the Perry and 'Benoit' (2017) <arXiv:1710.08963> class affinity scaling model, and the 'Slapin' and 'Proksch' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish' model, as well as methods for correspondence analysis, latent semantic analysis, and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.
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2024-01-16 |
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r-quantregranger
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public |
This is the implementation of quantile regression forests for the fast random forest package 'ranger'.
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2024-01-16 |
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r-quantregforest
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public |
Quantile Regression Forests is a tree-based ensemble method for estimation of conditional quantiles. It is particularly well suited for high-dimensional data. Predictor variables of mixed classes can be handled. The package is dependent on the package 'randomForest', written by Andy Liaw.
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2024-01-16 |
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r-quantreg
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None |
Estimation and inference methods for models for conditional quantile functions: 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 now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press, <doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression, CRC Press, <doi:10.1201/9781315120256>.
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2024-01-16 |
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r-quanteda.textplots
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public |
Plotting functions for visualising textual data. Extends 'quanteda' and related packages with plot methods designed specifically for text data, textual statistics, and models fit to textual data. Plot types include word clouds, lexical dispersion plots, scaling plots, network visualisations, and word 'keyness' plots.
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2024-01-16 |
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r-quanteda
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public |
A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and n-grams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.
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2024-01-16 |
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r-qtools
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public |
Functions for unconditional and conditional quantiles. These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, restricted quantile regression, directional quantile classification, and quantile ratio regression. A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.
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2024-01-16 |
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r-qualv
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public |
Qualitative methods for the validation of dynamic models. It contains (i) an orthogonal set of deviance measures for absolute, relative and ordinal scale and (ii) approaches accounting for time shifts. The first approach transforms time to take time delays and speed differences into account. The second divides the time series into interval units according to their main features and finds the longest common subsequence (LCS) using a dynamic programming algorithm.
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2024-01-16 |
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r-quadprog
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None |
This package contains routines and documentation for solving quadratic programming problems.
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2024-01-16 |
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r-qs
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public |
Provides functions for quickly writing and reading any R object to and from disk.
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2024-01-16 |
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r-qrng
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public |
Functionality for generating (randomized) quasi-random numbers in high dimensions.
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2024-01-16 |
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r-qtlrel
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public |
This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
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2024-01-16 |
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r-qtlmt
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public |
Provides tools for joint analysis of multiple traits in a backcross (BC) or recombinant inbred lines (RIL) population. It can be used to select an optimal subset of traits for multiple-trait mapping, analyze multiple traits via the SURE model, which can associate different QTL with different traits, and perform multiple-trait composite multiple-interval mapping.
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2024-01-16 |
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r-qrmtools
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public |
Functions and data sets for reproducing selected results from the book "Quantitative Risk Management: Concepts, Techniques and Tools". Furthermore, new developments and auxiliary functions for Quantitative Risk Management practice.
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2024-01-16 |
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r-qtl
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public |
Analysis of experimental crosses to identify genes (called quantitative trait loci, QTLs) contributing to variation in quantitative traits. Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.
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2024-01-16 |
