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r-depthtools
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depthTools is a package that implements different statistical tools for the description and analysis of gene expression data based on the concept of data depth, namely, the scale curves for visualizing the dispersion of one or various groups of samples (e.g. types of tumors), a rank test to decide whether two groups of samples come from a single distribution and two methods of supervised classification techniques, the DS and TAD methods. All these techniques are based on the Modified Band Depth, which is a recent notion of depth with a low computational cost, what renders it very appropriate for high dimensional data such as gene expression data.
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2023-06-16 |
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r-depth.plot
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Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).
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2023-06-16 |
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r-depend.truncation
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Estimation and testing methods for dependently truncated data. Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>, and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>. Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>. A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>. Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.
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2023-06-16 |
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r-dequer
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Queues, stacks, and 'deques' are list-like, abstract data types. These are meant to be very cheap to "grow", or insert new objects into. A typical use case involves storing data in a list in a streaming fashion, when you do not necessarily know how may elements need to be stored. Unlike R's lists, the new data structures provided here are not necessarily stored contiguously, making insertions and deletions at the front/end of the structure much faster. The underlying implementation is new and uses a head/tail doubly linked list; thus, we do not rely on R's environments or hashing. To avoid unnecessary data copying, most operations on these data structures are performed via side-effects.
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2023-06-16 |
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r-dinamic
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This function implements the DiNAMIC procedure for assessing the statistical significance of recurrent DNA copy number aberrations (Bioinformatics (2011) 27(5) 678 - 685).
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2023-06-16 |
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r-diffee
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This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.
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2023-06-16 |
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r-denstrip
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Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
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2023-06-16 |
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r-dime
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public |
A robust differential identification method that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) to analyze ChIP-seq (high-throughput genomic)data comparing two samples allowing for flexible modeling of data.
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2023-06-16 |
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r-directstandardisation
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Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset.
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2023-06-16 |
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r-directedclustering
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Allows the computation of clustering coefficients for directed and weighted networks by using different approaches. It allows to compute clustering coefficients that are not present in 'igraph' package. A description of clustering coefficients can be found in "Directed clustering in weighted networks: a new perspective", Clemente, G.P., Grassi, R. (2017), <doi:10.1016/j.chaos.2017.12.007>.
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2023-06-16 |
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r-diceeval
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Estimation, validation and prediction of models of different types : linear models, additive models, MARS,PolyMARS and Kriging.
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2023-06-16 |
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r-discretemtp
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Multiple testing procedures for discrete test statistics, that use the known discrete null distribution of the p-values for simultaneous inference.
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2023-06-16 |
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r-docopulae
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A direct approach to optimal designs for copula models based on the Fisher information. Provides flexible functions for building joint PDFs, evaluating the Fisher information and finding optimal designs. It includes an extensible solution to summation and integration called 'nint', functions for transforming, plotting and comparing designs, as well as a set of tools for common low-level tasks.
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2023-06-16 |
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r-delt
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We implement methods for estimating multivariate densities. We include a discretized kernel estimator, an adaptive histogram (a greedy histogram and a CART-histogram), stagewise minimization, and bootstrap aggregation.
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2023-06-16 |
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r-dga
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Performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.
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2023-06-16 |
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r-desir
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Functions for (1) ranking, selecting, and prioritising genes, proteins, and metabolites from high dimensional biology experiments, (2) multivariate hit calling in high content screens, and (3) combining data from diverse sources.
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2023-06-16 |
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r-deplogo
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Plots dependency logos from a set of input sequences.
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2023-06-16 |
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r-denoiseq
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Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of a bottom-up model for PCR amplification. This model is developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.
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2023-06-16 |
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r-demogr
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Construction and analysis of matrix population models in R.
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2023-06-16 |
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r-deepnn
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Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.
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2023-06-16 |
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r-dglars
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Differential geometric least angle regression method for fitting sparse generalized linear models. In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below.
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2023-06-16 |
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r-detpack
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Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function 'det.construct' translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function 'det.query' is available. The functions 'det1' and 'det2' provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by 'det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.
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2023-06-16 |
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r-dfcomb
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Phase I/II adaptive dose-finding design for combination studies where toxicity rates are supposed to increase with both agents.
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2023-06-16 |
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r-devoid
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Provides a non-drawing graphic device for benchmarking purpose. In order to properly benchmark graphic drawing code it is necessary to factor out the device implementation itself so that results are not related to the specific graphics device used during benchmarking. The 'devoid' package implements a graphic device that accepts all the required calls from R's graphic engine but performs no action. Apart from benchmarking it is unlikely that this device has any practical use.
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2023-06-16 |
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r-dosearch
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Identification of causal effects from arbitrary observational and experimental probability distributions via do-calculus and standard probability manipulations using a search-based algorithm. Allows for the presence of mechanisms related to selection bias (Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>), transportability (Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>), missing data (Mohan, K. and Pearl, J. and Tian., J. (2013) <http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf>) and arbitrary combinations of these.
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2023-06-16 |
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r-digitalpcr
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The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.
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2023-06-16 |
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r-df2json
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It handles numerics, characters, factors, and logicals.
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2023-06-16 |
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r-designgg
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The package provides R scripts for designing genetical genomics experiments.
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2023-06-16 |
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r-diptest
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Compute Hartigan's dip test statistic for unimodality / multimodality and provide a test with simulation based p-values, where the original public code has been corrected.
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2023-06-16 |
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r-dicekriging
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Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.
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2023-06-16 |
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r-dicedesign
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Space-Filling Designs and Uniformity Criteria.
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2023-06-16 |
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r-diffpriv
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An implementation of major general-purpose mechanisms for privatizing statistics, models, and machine learners, within the framework of differential privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms include the Laplace mechanism for releasing numeric aggregates, and the exponential mechanism for releasing set elements. A sensitivity sampler (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target non-private function sensitivity; combined with the generic mechanisms, it permits turn-key privatization of arbitrary programs.
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2023-06-16 |
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r-dils
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Combine multiple-relationship networks into a single weighted network. The approach is similar to factor analysis in the that contribution from each constituent network varies so as to maximize the information gleaned from the multiple-relationship networks. This implementation uses Principal Component Analysis calculated using 'prcomp' with bootstrap subsampling. Missing links are imputed using the method of Chen et al. (2012).
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2023-06-16 |
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r-diffobj
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Generate a colorized diff of two R objects for an intuitive visualization of their differences.
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2023-06-16 |
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r-detr
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DetLTS, DetMM (and DetS) Algorithms for Deterministic, Robust Linear Regression.
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2023-06-16 |
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r-deldir
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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.
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2023-06-16 |
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r-dpp
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This MCMC method takes a data numeric vector (Y) and assigns the elements of Y to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.
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2023-06-16 |
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r-distcomp
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Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using 'opencpu', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R 'survival' package. (That is, the underlying Cox model code is derived from that in the R 'survival' package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future. Web applications are provided (via 'shiny') for the implemented methods to help in designing and deploying the computations.
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2023-06-16 |
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r-bigreadr
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Read large text files by splitting them in smaller files. Package bigreadr also provides some convenient wrappers of fread/fwrite.
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2023-06-16 |
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r-desirability
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S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).
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2023-06-16 |
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r-designglmm
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Use simulated annealing to find optimal designs for Poisson regression models with blocks.
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2023-06-16 |
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r-dosefinding
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The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology.
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2023-06-16 |
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r-distances
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Provides tools for constructing, manipulating and using distance metrics.
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2023-06-16 |
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r-dissutils
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This package has extensible C++ code for computing dissimilarities between vectors. It also has a number of C++ functions for assembling collections of dissimilarities. In particular, it lets you find a matrix of dissimilarities between the rows of two input matrices. There are also functions for finding the nearest neighbors of each row of a matrix, either within the matrix itself or within another matrix.
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2023-06-16 |
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r-dhh
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The density, cumulative distribution, quantiles, and i.i.d random variables of a heavy-headed distribution. For more information, please see the vignette.
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2023-06-16 |
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r-detector
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Allows users to quickly and easily detect data containing Personally Identifiable Information (PII) through convenience functions.
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2023-06-16 |
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r-describer
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Allows users to quickly and easily describe data using common descriptive statistics.
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2023-06-16 |
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r-descomponer
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Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.
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2023-06-16 |
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r-densratio
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Density ratio estimation. The estimated density ratio function can be used in many applications such as anomaly detection, change-point detection, covariate shift adaptation. The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>), RuLSIF (Yamada et al. (2011) <doi:10.1162/NECO_a_00442>), and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>).
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2023-06-16 |
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r-diffusr
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Implementation of network diffusion algorithms such as heat diffusion or Markov random walks. Network diffusion algorithms generally spread information in the form of node weights along the edges of a graph to other nodes. These weights can for example be interpreted as temperature, an initial amount of water, the activation of neurons in the brain, or the location of a random surfer in the internet. The information (node weights) is iteratively propagated to other nodes until a equilibrium state or stop criterion occurs.
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2023-06-16 |
