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r-dsl
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An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.
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2025-04-22 |
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r-drmdel
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Dual empirical likelihood (DEL) inference under semiparametric density ratio models (DRM) in the presence of multiple samples, including population cumulative distribution function estimation, quantile estimation and comparison, density estimation, composite hypothesis testing for DRM parameters which encompasses testing for changes in population distribution functions as a special case, etc.
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2025-04-22 |
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r-drimpute
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R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events.
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2025-04-22 |
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r-driftbursthypothesis
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Calculates the T-Statistic for the drift burst hypothesis from the working paper Christensen, Oomen and Reno (2018) <DOI:10.2139/ssrn.2842535>. The authors' MATLAB code is available upon request, see: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2842535>.
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2025-04-22 |
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r-disclapmix
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Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).
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2025-04-22 |
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r-blockmodeling
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This is primarily meant as an implementation of generalized blockmodeling for valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted: Žiberna (2007)<doi:10.1016/j.socnet.2006.04.002>, Žiberna (2008)<doi:10.1080/00222500701790207>, Žiberna (2014)<doi:10.1016/j.socnet.2014.04.002>.
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2025-04-22 |
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r-arulescba
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Provides a function to build an association rule-based classifier for data frames, and to classify incoming data frames using such a classifier.
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2025-04-22 |
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r-dragular
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Move elements between containers in 'Shiny' without explicitly using 'JavaScript'. It can be used to build custom inputs or to change the positions of user interface elements like plots or tables.
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2025-04-22 |
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r-dragonking
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Statistical tests and test statistics to identify events in a dataset that are dragon kings (DKs). The statistical methods in this package were reviewed in Wheatley & Sornette (2015) <doi:10.2139/ssrn.2645709>.
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2025-04-22 |
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r-dr
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Functions, methods, and datasets for fitting dimension reduction regression, using slicing (methods SAVE and SIR), Principal Hessian Directions (phd, using residuals and the response), and an iterative IRE. Partial methods, that condition on categorical predictors are also available. A variety of tests, and stepwise deletion of predictors, is also included. Also included is code for computing permutation tests of dimension. Adding additional methods of estimating dimension is straightforward. For documentation, see the vignette in the package. With version 3.0.4, the arguments for dr.step have been modified.
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2025-04-22 |
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r-dqshiny
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Provides highly customizable modules to enhance your shiny apps. Includes layout independent collapsible boxes and value boxes, a very fast autocomplete input, rhandsontable extensions for filtering and paging and much more.
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2025-04-22 |
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r-dprint
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Provides a generalized method for printing tabular data within the R environment in order to make the process of presenting high quality tabular output seamless for the user. Output is directed to the R graphics device so that tables can be exported to any file format supported by the graphics device. Utilizes a formula interface to specify the contents of tables often found in manuscripts or business reports. In addition, formula interface provides inline formatting of the numeric cells of a table and renaming column labels.
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2025-04-22 |
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r-dplrcon
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The concordance method is a non-parametric method based on bootstrapping that is used to test the hypothesis that two subsets of time series are similar in terms of mean, variance or both. This method was developed to address a concern within dendroclimatology that young trees may produce a differing climate response to older more established trees. Details of this method are available in Pirie, M. (2013). The Climate of New Zealand reconstructed from kauri tree rings: Enhancement through the use of novel statistical methodology. PhD. Dissertation, School of Environment and Department of Statistics, University of Auckland, New Zealand. This package also produces a figure with 3 panels, each panel is for a different climate variable. An example of this figure in included in "On the influence of tree size on the climate - growth relationship of New Zealand kauri (Agathis australis): insights from annual, monthly and daily growth patterns. J Wunder, AM Fowler, ER Cook, M Pirie, SPJ McCloskey. Trees 27 (4), 937-948". For further R functions for loading your own dendroclimatology datasets and performing dendrochronology analysis refer to the R package "dplR: Dendrochronology Program Library in R". The concordance procedure is intended to add to the standard dendrochronology techniques provided in "dplR".
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2025-04-22 |
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r-downsize
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Toggles the test and production versions of a large workflow.
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2025-04-22 |
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r-dovalidation
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Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.
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2025-04-22 |
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r-double.truncation
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Likelihood-based inference methods with doubly-truncated data are developed under various models. Nonparametric models are based on Efron and Petrosian (1999) <doi:10.1080/01621459.1999.10474187> and Emura, Konno, and Michimae (2015) <doi:10.1007/s10985-014-9297-5>. Parametric models from the special exponential family (SEF) are based on Hu and Emura (2015) <doi:10.1007/s00180-015-0564-z> and Emura, Hu and Konno (2017) <doi:10.1007/s00362-015-0730-y>.
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2025-04-22 |
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r-doubleexpseq
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Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.
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2025-04-22 |
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r-doublecone
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Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details.
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2025-04-22 |
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r-dotenv
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Load configuration from a '.env' file, that is in the current working directory, into environment variables.
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2025-04-22 |
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r-dotdot
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Use '..' on the right hand side of the ':=' operator as a shorthand for the left hand side, so that 'var := f(..) + ..' is equivalent to 'var <- f(var) + var'. This permits the user to be explicit about growing an object or overwriting it using its previous value, avoids repeating a variable name, and saves keystrokes, time, visual space and cognitive load.
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2025-04-22 |
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r-dostats
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A small package containing helper utilities for creating function for computing statistics.
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2025-04-22 |
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r-dos
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Contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1.
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2025-04-22 |
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r-dorng
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Provides functions to perform reproducible parallel foreach loops, using independent random streams as generated by L'Ecuyer's combined multiple-recursive generator [L'Ecuyer (1999), <DOI:10.1287/opre.47.1.159>]. It enables to easily convert standard %dopar% loops into fully reproducible loops, independently of the number of workers, the task scheduling strategy, or the chosen parallel environment and associated foreach backend.
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2025-04-22 |
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r-domino
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A wrapper on top of the 'Domino Command-Line Client'. It lets you run 'Domino' commands (e.g., "run", "upload", "download") directly from your R environment. Under the hood, it uses R's system function to run the 'Domino' executable, which must be installed as a prerequisite. 'Domino' is a service that makes it easy to run your code on scalable hardware, with integrated version control and collaboration features designed for analytical workflows (see <http://www.dominodatalab.com> for more information).
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2025-04-22 |
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r-dominanceanalysis
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Dominance analysis is a method that allows to compare the relative importance of predictors in multiple regression models: ordinary least squares, generalized linear models and hierarchical linear models. The main principles and methods of dominance analysis are described in Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129> for ordinary least squares regression. Subsequently, the extensions for multivariate regression, logistic regression and hierarchical linear models were described in Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>, Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>, respectively.
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2025-04-22 |
