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r-eshrink
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Computes shrinkage estimators for regression problems. Selects penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution.
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2023-06-16 |
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r-esvis
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A variety of methods are provided to estimate and visualize distributional differences in terms of effect sizes. Particular emphasis is upon evaluating differences between two or more distributions across the entire scale, rather than at a single point (e.g., differences in means). For example, Probability-Probability (PP) plots display the difference between two or more distributions, matched by their empirical CDFs (see Ho and Reardon, 2012; <doi:10.3102/1076998611411918>), allowing for examinations of where on the scale distributional differences are largest or smallest. The area under the PP curve (AUC) is an effect-size metric, corresponding to the probability that a randomly selected observation from the x-axis distribution will have a higher value than a randomly selected observation from the y-axis distribution. Binned effect size plots are also available, in which the distributions are split into bins (set by the user) and separate effect sizes (Cohen's d) are produced for each bin - again providing a means to evaluate the consistency (or lack thereof) of the difference between two or more distributions at different points on the scale. Evaluation of empirical CDFs is also provided, with built-in arguments for providing annotations to help evaluate distributional differences at specific points (e.g., semi-transparent shading). All function take a consistent argument structure. Calculation of specific effect sizes is also possible. The following effect sizes are estimable: (a) Cohen's d, (b) Hedges' g, (c) percentage above a cut, (d) transformed (normalized) percentage above a cut, (e) area under the PP curve, and (f) the V statistic (see Ho, 2009; <doi:10.3102/1076998609332755>), which essentially transforms the area under the curve to standard deviation units. By default, effect sizes are calculated for all possible pairwise comparisons, but a reference group (distribution) can be specified.
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2023-06-16 |
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r-gaipe
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GAIPE implements graphical representation of accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling. Sample sizes suggested by RMSEA with AIPE method and power analysis approach can also be obtained separately using the provided functions.
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2023-06-16 |
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r-g3viz
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R interface for 'g3-lollipop' JavaScript library. Visualize genetic mutation data using an interactive lollipop diagram in RStudio or your browser.
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2023-06-16 |
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r-functools
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Extends functional programming in R by providing support to the usual higher order functional suspects (Map, Reduce, Filter, etc.).
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2023-06-16 |
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r-frt
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Perform full randomization tests.
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2023-06-16 |
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r-frambgrowth
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Generation of theoretical size distributions of framboidal or sunflower pyrite. The growth mechanisms used are surface and supply controlled and dependent or independent of size. The algorithms are fully described in the published work in Mineralogy and Petrology journal: "Theoretical growth of framboidal and sunflower pyrite using the R-package frambgrowth" The authors Merinero, R., and Cardenes, V. (2018). <DOI:10.1007/s00710-017-0535-x>.
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2023-06-16 |
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r-fptdapprox
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Efficient approximation of first-passage-time densities for diffusion processes based on the First-Passage-Time Location (FPTL) function.
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2023-06-16 |
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r-fixedtimeevents
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Distribution functions and test for over-representation of short distances in the Liland distribution. Simulation functions are included for comparison.
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2023-06-16 |
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r-fints
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R companion to Tsay (2005) Analysis of Financial Time Series, second edition (Wiley). Includes data sets, functions and script files required to work some of the examples. Version 0.3-x includes R objects for all data files used in the text and script files to recreate most of the analyses in chapters 1-3 and 9 plus parts of chapters 4 and 11.
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2023-06-16 |
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r-fincovregularization
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Estimation and regularization for covariance matrix of asset returns. For covariance matrix estimation, three major types of factor models are included: macroeconomic factor model, fundamental factor model and statistical factor model. For covariance matrix regularization, four regularized estimators are included: banding, tapering, hard-thresholding and soft- thresholding. The tuning parameters of these regularized estimators are selected via cross-validation.
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2023-06-16 |
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r-files
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Functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for viewing the details (size, time created, time modified, etc.) of a folder's top level contents.
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2023-06-16 |
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r-fgpt
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A permutation technique to explore and control for spatial autocorrelation. This package contains low level functions for performing permutations and calculating statistics as well as higher level functions. Higher level functions are an easy to use function for performing spatially restricted permutation tests and summarize and plot results.
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2023-06-16 |
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r-fastgraph
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Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.
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2023-06-16 |
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r-factorial2x2
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Used for the design and analysis of a 2x2 factorial trial for a time-to-event endpoint. Performs power calculations and significance testing. Important reference papers include Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609> Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507> Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2019) <https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al%20Factorial.pdf>.
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2023-06-16 |
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r-excon
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Interactive tools to explore topographic-like data sets. Such data sets take the form of a matrix in which the rows and columns provide location/frequency information, and the matrix elements contain altitude/response information. Such data is found in cartography, 2D spectroscopy and chemometrics. The functions in this package create interactive web pages showing the contoured data, possibly with slices from the original matrix parallel to each dimension. The interactive behavior is created using the 'D3.js' 'JavaScript' library by Mike Bostock.
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2023-06-16 |
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r-evmix
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The usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the 'evd' package is provided, so that users can safely interchange most code.
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2023-06-16 |
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r-evir
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Functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions.
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2023-06-16 |
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r-estsimpdmp
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This package deals with the estimation of the jump rate for piecewise-deterministic Markov processes (PDMPs), from only one observation of the process within a long time. The main functions provide an estimate of this function. The state space may be discrete or continuous. The associated paper has been published in Scandinavian Journal of Statistics and is given in references. Other functions provide a method to simulate random variables from their (conditional) hazard rate, and then to simulate PDMPs.
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2023-06-16 |
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r-errors
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Support for measurement errors in R vectors, matrices and arrays: automatic uncertainty propagation and reporting. Documentation about 'errors' is provided in the paper by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a vignette; see 'citation("errors")' for details.
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2023-06-16 |
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r-esc
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Implementation of the web-based 'Practical Meta-Analysis Effect Size Calculator' from David B. Wilson (<http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php>) in R. Based on the input, the effect size can be returned as standardized mean difference, Cohen's f, Hedges' g, Pearson's r or Fisher's transformation z, odds ratio or log odds, or eta squared effect size.
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2023-06-16 |
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r-esg
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The package presents a "Scenarios" class containing general parameters, risk parameters and projection results. Risk parameters are gathered together into a ParamsScenarios sub-object. The general process for using this package is to set all needed parameters in a Scenarios object, use the customPathsGeneration method to proceed to the projection, then use xxx_PriceDistribution() methods to get asset prices.
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2023-06-16 |
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r-errorizer
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Provides a function to convert existing R functions into "errorized" versions with added logging and handling functionality when encountering errors or warnings. The errorize function accepts an existing R function as its first argument and returns a R function with the exact same arguments and functionality. However, if an error or warning occurs when running that "errorized" R function, it will save a .Rds file to the current working directory with the relevant objects and information required to immediately recreate the error.
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2023-06-16 |
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r-ew
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Edgeworth Expansion calculation.
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2023-06-16 |
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r-exsic
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The package provides tools for botanists, plant taxonomists, curators of plant genebanks and perhaps other biological collections.
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2023-06-16 |
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r-exactmeta
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Perform exact fixed effect meta analysis for rare events data without the need of artificial continuity correction.
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2023-06-16 |
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r-esdesign
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Software of 'esDesign' is developed to implement the adaptive enrichment designs with sample size re-estimation. In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) <doi:10.1177/1740774513503739>, the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995), and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR.
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2023-06-16 |
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r-ergmharris
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Data for use with the Sage Introduction to Exponential Random Graph Modeling text by Jenine K. Harris. Network data set consists of 1283 local health departments and the communication links among them along with several attributes.
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2023-06-16 |
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r-epanetreader
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Reads water network simulation data in 'Epanet' text-based '.inp' and '.rpt' formats into R. Also reads results from 'Epanet-msx'. Provides basic summary information and plots. The README file has a quick introduction. See <http://www2.epa.gov/water-research/epanet> for more information on the Epanet software for modeling hydraulic and water quality behavior of water piping systems.
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2023-06-16 |
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r-erpr
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This package is dedicated to the analysis of event-related potentials (ERPs). Event-related potentials are the measured brain responses associated with a specific sensory, cognitive, or motor event and are obtained from electroencephalographic (EEG) signal. The erpR package contains a series of functions for importing ERP data, computing traditional ERP measures, exploratory ERP analyses and plotting.
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2023-06-16 |
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r-equivnoninf
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Making available in R the complete set of programs accompanying S. Wellek's (2010) monograph ''Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition'' (Chapman&Hall/CRC).
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2023-06-16 |
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r-eqs2lavaan
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Transitioning from EQS to R for structural equation modeling (SEM) is made easier with a set of functions to convert .out files into R code. The EQS output can be converted into lavaan syntax and run in the R environment. Other functions parse descriptive statistics and the covariance matrix from an EQS .out file. A heat map plot of a covariance matrix is also included.
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2023-06-16 |
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r-equate
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Contains methods for observed-score linking and equating under the single-group, equivalent-groups, and nonequivalent-groups with anchor test(s) designs. Equating types include identity, mean, linear, general linear, equipercentile, circle-arc, and composites of these. Equating methods include synthetic, nominal weights, Tucker, Levine observed score, Levine true score, Braun/Holland, frequency estimation, and chained equating. Plotting and summary methods, and methods for multivariate presmoothing and bootstrap error estimation are also provided.
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2023-06-16 |
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r-energyonlinecpm
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Provides a function for distribution free control chart based on the change point model, for multivariate statistical process control. The main constituent of the chart is the energy test that focuses on the discrepancy between empirical characteristic functions of two random vectors. This new control chart highlights in three aspects. Firstly, it is distribution free, requiring no knowledge of the random processes. Secondly, this control chart can monitor mean and variance simultaneously. Thirdly it is devised for multivariate time series which is more practical in real data application. Fourthly, it is designed for online detection (Phase II), which is central for real time surveillance of stream data. For more information please refer to O. Okhrin and Y.F. Xu (2017) <https://github.com/YafeiXu/working_paper/raw/master/CPM102.pdf>.
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2023-06-16 |
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r-epitools
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Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
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2023-06-16 |
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r-calibrator
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Performs Bayesian calibration of computer models as per Kennedy and O'Hagan 2001. The package includes routines to find the hyperparameters and parameters; see the help page for stage1() for a worked example using the toy dataset. A tutorial is provided in the calex.Rnw vignette; and a suite of especially simple one dimensional examples appears in inst/doc/one.dim/.
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2023-06-16 |
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r-epxtor
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Import data from 'Epidata' XML files '.epx' and convert it to R data structures.
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2023-06-16 |
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r-ercv
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Provides a methodology simple and trustworthy for the analysis of extreme values and multiple threshold tests for a generalized Pareto distribution, together with an automatic threshold selection algorithm. See del Castillo, J, Daoudi, J and Lockhart, R (2014) <doi:10.1111/sjos.12037>.
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2023-06-16 |
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r-equaltestmi
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Functions for examining measurement invariance via equivalence testing along with adjusted RMSEA(root mean square error of approximation; Steiger & Lind, 1980) cutoff values. In particular, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts.
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2023-06-16 |
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r-eplot
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Aim: Adjust the graphical parameters to create nicer longitudinal series plots. The default set of graphical parameters is very general, and can be improved upon when we are interested in plotting data points observed over time. Functions facilitate plotting those kind of series, univariate plots, bivariate plots (with vertical axis on both left and right hand sides), multivariate plots and plots which allow to examine whether a new observation is 'unusual' via construction and visualization of prediction intervals around it.
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2023-06-16 |
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r-episcan
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Searching genomic interactions with linear/logistic regression in a high-dimensional dataset is a time-consuming task. This package provides some efficient ways to scan epistasis in genome-wide interaction studies (GWIS). Both case-control status (binary outcome) and quantitative phenotype (continuous outcome) are supported (the main references: 1. Kam-Thong, T., D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, et al. (2011). <doi:10.1038/ejhg.2010.196>. 2. Kam-Thong, T., B. Pütz, N. Karbalai, B. Müller-Myhsok, and K. Borgwardt. (2011). <doi:10.1093/bioinformatics/btr218>.)
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2023-06-16 |
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r-epir
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Tools for the analysis of epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.
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2023-06-16 |
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r-epidisplay
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Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at '<http://medipe.psu.ac.th/epicalc>'.
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2023-06-16 |
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r-epibasix
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Contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity). Appropriate print and summary statements are also written to facilitate interpretation wherever possible. Source code is commented throughout to facilitate modification. The target audience includes advanced undergraduate and graduate students in epidemiology or biostatistics courses, and clinical researchers.
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2023-06-16 |
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r-epsiwal
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Implements the conditional estimation procedure of Lee, Sun, Sun and Taylor (2016) <doi:10.1214/15-AOS1371>. This procedure allows hypothesis testing on the mean of a normal random vector subject to linear constraints.
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2023-06-16 |
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r-epistemicgametheory
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Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality.
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2023-06-16 |
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r-equivump
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Implementation of uniformly most powerful invariant equivalence tests for one- and two-sample problems (paired and unpaired) as described in Wellek (2010, ISBN:978-1-4398-0818-4). Also one-sided alternatives (non-inferiority and non-superiority tests) are supported. Basically a variant of a t-test with (relaxed) null and alternative hypotheses exchanged.
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2023-06-16 |
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r-elhmc
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A tool to draw samples from a Empirical Likelihood Bayesian posterior of parameters using Hamiltonian Monte Carlo.
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2023-06-16 |
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r-appliedpredictivemodeling
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A few functions and several data set for the Springer book 'Applied Predictive Modeling'.
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2023-06-16 |
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r-eq5d
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EQ-5D is a popular health related quality of life instrument used in the clinical and economic evaluation of health care. Developed by the EuroQol group <https://www.euroqol.org>, the instrument consists of two components: health state description and evaluation. For the description component a subject self-rates their health in terms of five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression using either a three-level (EQ-5D-3L, <https://www.euroqol.org/eq-5d-instruments/eq-5d-3l-about>) or a five-level (EQ-5D-5L, <https://www.euroqol.org/eq-5d-instruments/eq-5d-5l-about>) scale. Frequently the scores on these five dimensions are converted to a single utility index using country specific value sets, which can be used in the clinical and economic evaluation of health care as well as in population health surveys. The eq5d package provides methods to calculate index scores from a subject's dimension scores. 19 TTO and 9 VAS EQ-5D-3L value sets including those for countries in Szende et al (2007) <doi:10.1007/1-4020-5511-0> and Szende et al (2014) <doi:10.1007/978-94-007-7596-1>, 15 EQ-5D-5L EQ-VT value sets from the EuroQol website, and the EQ-5D-5L crosswalk value sets developed by van Hout et al. (2012) <doi:10.1016/j.jval.2012.02.008> are included. Additionally, a shiny web tool is included to enable the calculation and visualisation of EQ-5D index values via a web browser using EQ-5D dimension scores stored in CSV or Excel files.
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2023-06-16 |
