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r-dummies
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Expands factors, characters and other eligible classes into dummy/indicator variables.
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2023-06-16 |
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r-emsnm
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It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.
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2023-06-16 |
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r-electivity
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Provides all electivity algorithms (including Vanderploeg and Scavia electivity) that were examined in Lechowicz (1982) <doi:10.1007/BF00349007>, plus the example data that were provided for moth resource utilisation.
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2023-06-16 |
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r-eila
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Implementation of Efficient Inference of Local Ancestry using fused quantile regression and k-means classifier
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2023-06-16 |
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r-eigenprcomp
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Computes confidence intervals for the proportion explained by the first 1,2,k principal components, and computes confidence intervals for each eigenvalue. Both computations are done via nonparametric bootstrap.
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2023-06-16 |
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r-endogmnp
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endogMNP is an R package that fits a Bayesian multinomial probit model with endogenous selection, which is sometimes called an endogenous switching model. This can be used to model discrete choice data when respondents select themselves into one of several groups. This package is based on the MNP package by Kosuke Imai and David A. van Dyk. This package modifies their code.
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2023-06-16 |
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r-emplik
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Empirical likelihood ratio tests for means/quantiles/hazards from possibly censored and/or truncated data. Now does regression too. This version contains some C code.
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2023-06-16 |
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r-econdemand
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Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.
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2023-06-16 |
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r-ebgenotyping
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Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.
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2023-06-16 |
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r-dynamac
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While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).
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2023-06-16 |
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r-dst
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Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.
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2023-06-16 |
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r-dropr
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Drop out analysis for psychologists in a R based web application. Shiny is used to visualize and analyze drop outs tailored to the methods of online survey methodology. Concept and app presented at the SCIP Conference in Long Beach, California.
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2023-06-16 |
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r-emt
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The package provides functions to carry out a Goodness-of-fit test for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. A p-value can be calculated using different distance measures between observed and expected frequencies. A Monte Carlo method is provided to make the package capable of solving high-dimensional problems.
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2023-06-16 |
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r-emmli
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Fit models of modularity to morphological landmarks. Perform model selection on results. Fit models with a single within-module correlation or with separate within-module correlations fitted to each module.
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2023-06-16 |
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r-elliptic
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A suite of elliptic and related functions including Weierstrass and Jacobi forms. Also includes various tools for manipulating and visualizing complex functions.
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2023-06-16 |
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r-elections
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This includes a dataset on the outcomes of the USA presidential elections since 1920, and various predictors, as used in <http://vanderwalresearch.com/blog/15-elections>.
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2023-06-16 |
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r-egst
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Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) <doi:10.1101/674226>.
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2023-06-16 |
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r-energy
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E-statistics (energy) tests and statistics for multivariate and univariate inference, including distance correlation, one-sample, two-sample, and multi-sample tests for comparing multivariate distributions, are implemented. Measuring and testing multivariate independence based on distance correlation, partial distance correlation, multivariate goodness-of-fit tests, k-groups and hierarchical clustering based on energy distance, testing for multivariate normality, distance components (disco) for non-parametric analysis of structured data, and other energy statistics/methods are implemented.
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2023-06-16 |
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r-empichar
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Evaluates the empirical characteristic function of univariate and multivariate samples. This package uses 'RcppArmadillo' for fast evaluation. It is also possible to export the code to be used in other packages at 'C++' level.
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2023-06-16 |
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r-ecotoxicology
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Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis. Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book. Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included. Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard. Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc.
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2023-06-16 |
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r-echarts2shiny
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Embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (<http://echarts.baidu.com/>). Current version supports line chart, bar chart, pie chart, scatter plot, gauge, word cloud, radar chart, tree map, and heat map.
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2023-06-16 |
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r-ebsnp
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Genotyping and SNP calling tool for single-sample next generation sequencing data analysis using an empirical Bayes method.
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2023-06-16 |
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r-dub
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Provides an operator for assigning nested components of a list to names via a concise pattern matching syntax. This is especially convenient for assigning individual names to the multiple values that a function may return in the form of a list, and for extracting deeply nested list components.
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2023-06-16 |
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r-dtp
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Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) <doi:10.1007/s00181-012-0553-9>) in which they extended the (Hansen (1999) <doi: 10.1016/S0304-4076(99)00025-1>) original static panel threshold estimation and the Caner and (Hansen (2004) <doi:10.1017/S0266466604205011>) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used.
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2023-06-16 |
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r-dtaxg
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To calculate the sensitivity and specificity in the absence of gold standard using the Bayesian method. The Bayesian method can be referenced at Haiyan Gu and Qiguang Chen (1999) <doi:10.3969/j.issn.1002-3674.1999.04.004>.
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2023-06-16 |
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r-dwlm
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This linear model solution is useful when both predictor and response have associated uncertainty. The doubly weights linear model solution is invariant on which quantity is used as predictor or response. Based on the results by Reed(1989) <doi:10.1119/1.15963> and Ripley & Thompson(1987) <doi:10.1039/AN9871200377>.
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2023-06-16 |
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r-draw
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A set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings. Compatible with and based on the R 'grid' package.
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2023-06-16 |
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r-ar
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In mathematics, 'rejection sampling' is a basic technique used to generate observations from a distribution. It is also commonly called 'the Acceptance-Rejection method' or 'Accept-Reject algorithm' and is a type of Monte Carlo method. 'Acceptance-Rejection method' is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package 'AR' is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package 'AR' is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method.
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2023-06-16 |
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r-drillr
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Provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.
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2023-06-16 |
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r-deepr
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Tests for, and describe differences in event count profiles in groups of reconstructed cophylogenies
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2023-06-16 |
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r-dsample
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Two discretization-based Monte Carlo algorithms, namely the Fu-Wang algorithm and the Wang-Lee algorithm, are provided for random sample generation from a high dimensional distribution of complex structure. The normalizing constant of the target distribution needs not to be known.
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2023-06-16 |
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r-dtsg
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Basic time series functionalities such as listing of missing values, application of arbitrary aggregation as well as rolling window functions and automatic detection of periodicity. As it is mainly based on 'data.table', it is fast and - in combination with the 'R6' package - offers reference semantics. In addition to its native R6 interface, it provides an S3 interface inclusive an S3 wrapper method generator for those who prefer the latter.
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2023-06-16 |
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r-ebdbnet
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Infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.
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2023-06-16 |
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r-dtda.cif
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Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) <doi:10.2307/2669997>.
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2023-06-16 |
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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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2023-06-16 |
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r-downsize
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Toggles the test and production versions of a large workflow.
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2023-06-16 |
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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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2023-06-16 |
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r-doex
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Contains several one-way heteroscedastic ANOVA tests such as Alexander-Govern test by Alexandern and Govern (1994) <doi:10.2307/1165140>, Alvandi et al. Generalized F test by Alvandi et al. (2012) <doi:10.1080/03610926.2011.573160>, Approximate F test by Asiribo and Gurland (1990) <doi:10.1080/03610929008830427>, Box F test by Box (1954) <doi:10.1214/aoms/1177728786>, Brown-Forsythe test by Brown and Forsythe (1974) <do:10.2307/1267501>, B2 test by Ozdemir and Kurt (2006) <http://sjam.selcuk.edu.tr/sjam/article/view/174>, Cochran F test by Cochran (1937) <https://www.jstor.org/stable/pdf/2984123.pdf>, Fiducial Approach test by Li et al. (2011) <doi:10.1016/j.csda.2010.12.009>, Generalized F test by Weerahandi (1995) <doi:10.2307/2532947>, Johansen F test by Johansen (1980) <doi:10.1093/biomet/67.1.85>, Modified Brown-Forsythe test by Mehrotra (1997) <doi:10.1080/03610919708813431>, Modified Welch test by Hartung et al.(2002) <doi:10.1007/s00362-002-0097-8>, One-Stage test by Chen and Chen (1998) <doi:10.1080/03610919808813501>, One-Stage Range test by Chen and Chen (2000) <doi:10.1080/01966324.2000.10737505>, Parametric Bootstrap test by Krishnamoorhty et al.(2007) <doi:10.1016/j.csda.2006.09.039>, Permutation F test by Berry and Mielke (2002) <doi:10.2466/pr0.2002.90.2.495>, Scott-Smith test by Scott and Smith (1971) <doi:10.2307/2346757>, Welch test by Welch(1951) <doi:10.2307/2332579>, and Welch-Aspin test by Aspin (1948) <doi:10.1093/biomet/35.1-2.88>. These tests are used to test the equality of group means under unequal variance. Furthermore, a modified version of Generalized F-test is improved to test the equality of non-normal group means under unequal variances and a revised version of Generalized F-test is given to test the equality of non-normal group means caused by skewness.
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2023-06-16 |
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r-dodge
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A variety of sampling plans are able to be compared using evaluations of their operating characteristics (OC), average outgoing quality (OQ), average total inspection (ATI) etc.
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2023-06-16 |
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r-dynpred
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The dynpred package contains functions for dynamic prediction in survival analysis.
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2023-06-16 |
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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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2023-06-16 |
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r-dobad
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Provides Frequentist (EM) and Bayesian (MCMC) Methods for Inference of Birth-Death-Immigration Markov Chains.
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2023-06-16 |
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r-docstring
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Provides the ability to display something analogous to Python's docstrings within R. By allowing the user to document their functions as comments at the beginning of their function without requiring putting the function into a package we allow more users to easily provide documentation for their functions. The documentation can be viewed just like any other help files for functions provided by packages as well.
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2023-06-16 |
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r-discreterv
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Create, manipulate, transform, and simulate from discrete random variables. The syntax is modeled after that which is used in mathematical statistics and probability courses, but with powerful support for more advanced probability calculations. This includes the creation of joint random variables, and the derivation and manipulation of their conditional and marginal distributions.
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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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r-ecp
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Implements various procedures for finding multiple change-points. Two methods make use of dynamic programming and pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change- points as well as other summary information.
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2023-06-16 |
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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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2023-06-16 |
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r-dnaseqtest
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Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and often impose further restrictions on the parameters.
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2023-06-16 |
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r-dmwr
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This package includes functions and data accompanying the book "Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
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2023-06-16 |
