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r-fastmatch
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Package providing a fast match() replacement for cases that require repeated look-ups. It is slightly faster that R's built-in match() function on first match against a table, but extremely fast on any subsequent lookup as it keeps the hash table in memory.
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2023-06-16 |
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r-fastdigest
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Provides an R interface to Bob Jenkin's streaming, non-cryptographic 'SpookyHash' hash algorithm for use in digest-based comparisons of R objects. 'fastdigest' plugs directly into R's internal serialization machinery, allowing digests of all R objects the serialize() function supports, including reference-style objects via custom hooks. Speed is high and scales linearly by object size; memory usage is constant and negligible.
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2023-06-16 |
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r-falcon
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This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing. Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
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2023-06-16 |
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r-fenmlm
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Efficient estimation of maximum likelihood models with multiple fixed-effects. Standard-errors can easily and flexibly be clustered and estimations exported.
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2023-06-16 |
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r-fdrtool
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Estimates both tail area-based false discovery rates (Fdr) as well as local false discovery rates (fdr) for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). The proportion of null values and the parameters of the null distribution are adaptively estimated from the data. In addition, the package contains functions for non-parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold.
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2023-06-16 |
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r-fasttime
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Fast functions for timestamp manipulation that avoid system calls and take shortcuts to facilitate operations on very large data.
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2023-06-16 |
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r-fastpcs
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The FastPCS algorithm of Vakili and Schmitt (2014) <doi:10.1016/j.csda.2013.07.021> for robust estimation of multivariate location and scatter and multivariate outliers detection.
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2023-06-16 |
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r-fastlzerospikeinference
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Estimate spike times from calcium imaging data using an L0 penalty.
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2023-06-16 |
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r-flam
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Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.
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2023-06-16 |
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r-fastrcs
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The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.
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2023-06-16 |
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r-fishical
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FisHiCal integrates Hi-C and FISH data, offering a modular and easy-to-use tool for chromosomal spatial analysis.
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2023-06-16 |
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r-fire
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The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets. The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. <doi:10.1038/s41467-018-07234-6>.
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2023-06-16 |
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r-fastm
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Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.
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2023-06-16 |
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r-fcros
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A fold change rank based method is presented to search for genes with changing expression and to detect recurrent chromosomal copy number aberrations. This method may be useful for high-throughput biological data (micro-array, sequencing, ...). Probabilities are associated with genes or probes in the data set and there is no problem of multiple tests when using this method. For array-based comparative genomic hybridization data, segmentation results are obtained by merging the significant probes detected.
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2023-06-16 |
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r-fasthcs
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The FastHCS algorithm of Schmitt and Vakili (2014) <doi:10.1007/s11222-015-9602-5> for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools.
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2023-06-16 |
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r-fastglm
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Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard against convergence issues.
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2023-06-16 |
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r-famle
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Estimate parameters of univariate probability distributions with maximum likelihood and Bayesian methods.
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2023-06-16 |
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r-fftwtools
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Provides a wrapper for several 'FFTW' functions. This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The 'FFT' functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.
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2023-06-16 |
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r-fastghquad
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Fast, numerically-stable Gauss-Hermite quadrature rules and utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A. (1994) <doi:10.2307/2337136> for a reference on these methods.
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2023-06-16 |
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r-xyz
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High dimensional interaction search by brute force requires a quadratic computational cost in the number of variables. The xyz algorithm provably finds strong interactions in almost linear time. For details of the algorithm see: G. Thanei, N. Meinshausen and R. Shah (2016). The xyz algorithm for fast interaction search in high-dimensional data <https://arxiv.org/pdf/1610.05108v1.pdf>.
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2023-06-16 |
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r-xslt
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An extension for the 'xml2' package to transform XML documents by applying an 'xslt' style-sheet.
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2023-06-16 |
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r-wtest
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Perform the calculation of W-test, diagnostic checking, calculate minor allele frequency (MAF) and odds ratio.
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2023-06-16 |
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r-wskm
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Entropy weighted k-means (ewkm) is a weighted subspace clustering algorithm that is well suited to very high dimensional data. Weights are calculated as the importance of a variable with regard to cluster membership. The two-level variable weighting clustering algorithm tw-k-means (twkm) introduces two types of weights, the weights on individual variables and the weights on variable groups, and they are calculated during the clustering process. The feature group weighted k-means (fgkm) extends this concept by grouping features and weighting the group in addition to weighting individual features.
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2023-06-16 |
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r-yaimpute
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Performs nearest neighbor-based imputation using one or more alternative approaches to processing multivariate data. These include methods based on canonical correlation analysis, canonical correspondence analysis, and a multivariate adaptation of the random forest classification and regression techniques of Leo Breiman and Adele Cutler. Additional methods are also offered. The package includes functions for comparing the results from running alternative techniques, detecting imputation targets that are notably distant from reference observations, detecting and correcting for bias, bootstrapping and building ensemble imputations, and mapping results.
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2023-06-16 |
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r-xnomial
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Tests whether a set of counts fit a given expected ratio. For example, a genetic cross might be expected to produce four types in the relative frequencies of 9:3:3:1. To see whether a set of observed counts fits this expectation, one can examine all possible outcomes with xmulti() or a random sample of them with xmonte() and find the probability of an observation deviating from the expectation by at least as much as the observed. As a measure of deviation from the expected, one can use the log-likelihood ratio, the multinomial probability, or the classic chi-square statistic. A histogram of the test statistic can also be plotted and compared with the asymptotic curve.
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2023-06-16 |
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r-wrswor
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A collection of implementations of classical and novel algorithms for weighted sampling without replacement.
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2023-06-16 |
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r-weibullr
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Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586) <DOI:10.1002/0471725234>, William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986) <DOI:10.1002/9781118351994>.
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2023-06-16 |
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r-wwr
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Calculate the (weighted) win loss statistics including the win ratio, win difference and win product and their variances, with which the p-values are also calculated. The variance estimation is based on Luo et al. (2015) <doi:10.1111/biom.12225> and Luo et al. (2017) <doi:10.1002/sim.7284>. This package also calculates general win loss statistics with user-specified win loss function with variance estimation based on Bebu and Lachin (2016) <doi:10.1093/biostatistics/kxv032>. This version corrected an error when outputting confidence interval for win difference.
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2023-06-16 |
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r-zic
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Provides MCMC algorithms for the analysis of zero-inflated count models. The case of stochastic search variable selection (SVS) is also considered. All MCMC samplers are coded in C++ for improved efficiency. A data set considering the demand for health care is provided.
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2023-06-16 |
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r-xtensor
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The 'xtensor' C++ library for numerical analysis with multi-dimensional array expressions is provided as a header-only C++14 library. It offers an extensible expression system enabling lazy broadcasting; an API following the idioms of the C++ standard library; and tools to manipulate array expressions and build upon 'xtensor'.
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2023-06-16 |
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r-xbrl
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Functions to extract business financial information from an Extensible Business Reporting Language ('XBRL') instance file and the associated collection of files that defines its 'Discoverable' Taxonomy Set ('DTS').
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2023-06-16 |
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r-wsrf
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A parallel implementation of Weighted Subspace Random Forest. The Weighted Subspace Random Forest algorithm was proposed in the International Journal of Data Warehousing and Mining by Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye (2012) <DOI:10.4018/jdwm.2012040103>. The algorithm can classify very high-dimensional data with random forests built using small subspaces. A novel variable weighting method is used for variable subspace selection in place of the traditional random variable sampling.This new approach is particularly useful in building models from high-dimensional data.
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2023-06-16 |
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r-wfe
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Provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) "When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?", available at <https://imai.fas.harvard.edu/research/FEmatch.html>.
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2023-06-16 |
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r-wordcloud
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Functionality to create pretty word clouds, visualize differences and similarity between documents, and avoid over-plotting in scatter plots with text.
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2023-06-16 |
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r-yakmor
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This is a simple wrapper for the yakmo K-Means library (developed by Naoki Yoshinaga, see http://www.tkl.iis.u-tokyo.ac.jp/~ynaga/yakmo/). It performs fast and robust (orthogonal) K-Means.
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2023-06-16 |
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r-xgboost
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Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.
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2023-06-16 |
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r-writexl
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Zero-dependency data frame to xlsx exporter based on 'libxlsxwriter'. Fast and no Java or Excel required.
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2023-06-16 |
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r-ypinterimtesting
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For any spending function specified by the user, this package provides corresponding boundaries for interim testing using the adaptively weighted log-rank test developed by Yang and Prentice (2010 <doi:10.1111/j.1541-0420.2009.01243.x>). The package uses a re-sampling method to obtain stopping boundaries at the interim looks.The output consists of stopping boundaries and observed values of the test statistics at the interim looks, along with nominal p-values defined as the probability of the test exceeding the specific observed test statistic value or critical value, regardless of the test behavior at other looks. The asymptotic validity of the stopping boundaries is established in Yang (2018 <doi:10.1002/sim.7958>).
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2023-06-16 |
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r-xptr
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There is limited native support for external pointers in the R interface. This package provides some basic tools to verify, create and modify 'externalptr' objects.
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2023-06-16 |
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r-wrassp
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A wrapper around Michel Scheffers's 'libassp' (<http://libassp.sourceforge.net/>). The 'libassp' (Advanced Speech Signal Processor) library aims at providing functionality for handling speech signal files in most common audio formats and for performing analyses common in phonetic science/speech science. This includes the calculation of formants, fundamental frequency, root mean square, auto correlation, a variety of spectral analyses, zero crossing rate, filtering etc. This wrapper provides R with a large subset of 'libassp's signal processing functions and provides them to the user in a (hopefully) user-friendly manner.
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2023-06-16 |
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r-wpkde
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Weighted Piecewise Kernel Density Estimation for large data.
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2023-06-16 |
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r-wicket
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Utilities to generate bounding boxes from 'WKT' (Well-Known Text) objects and R data types, validate 'WKT' objects and convert object types from the 'sp' package into 'WKT' representations.
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2023-06-16 |
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r-whopgenome
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Provides very fast access to whole genome, population scale variation data from VCF files and sequence data from FASTA-formatted files. It also reads in alignments from FASTA, Phylip, MAF and other file formats. Provides easy-to-use interfaces to genome annotation from UCSC and Bioconductor and gene ontology data from AmiGO and is capable to read, modify and write PLINK .PED-format pedigree files.
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2023-06-16 |
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r-wlreg
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Use various regression models for the analysis of win loss endpoints adjusting for non-binary and multivariate covariates.
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2023-06-16 |
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r-wingui
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Helps for interfacing with the operating system particularly for Windows.
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2023-06-16 |
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r-pullword
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R Interface to Pullword Service for natural language processing in Chinese. It enables users to extract valuable words from text by deep learning models. For more details please visit the official site (in Chinese) http://pullword.com/.
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2023-06-16 |
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r-prsr
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Tests periodicity in short time series using response surface regression.
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2023-06-16 |
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r-precisesums
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Most of the time floating point arithmetic does approximately the right thing. When adding sums or having products of numbers that greatly differ in magnitude, the floating point arithmetic may be incorrect. This package implements the Kahan (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum <doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from 'NumPy', See Castaldo (2008) <doi:10.1137/070679946> for a discussion of accuracy), and arbitrary precision sum (adapted from the fsum in 'Python' ; Shewchuk (1997) <http://www.cs.berkeley.edu/~jrs/papers/robustr.pdf>). In addition, products are changed to long double precision for accuracy, or changed into a log-sum for accuracy.
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2023-06-16 |
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r-pop.wolf
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Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C.
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2023-06-16 |
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r-pomaspu
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POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) <DOI:10.1002/gepi.22033>.
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2023-06-16 |
