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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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2025-04-22 |
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r-err
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Messages should provide users with readable information about R objects without flooding their console. 'cc()' concatenates vector and data frame values into a grammatically correct string using commas, an ellipsis and conjunction. 'cn()' allows the user to define a string which varies based on a count. 'co()' combines the two to produce a customizable object aware string. The package further facilitates this process by providing five 'sprintf'-like types such as '%n' for the length of an object and '%o' for its name as well as wrappers for pasting objects and issuing errors, warnings and messages.
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-eply
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Evaluate a function over a data frame of expressions.
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-epimdr
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Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) <https://www.springer.com/gp/book/9783319974866>. The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
