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r-exporkit
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An R-interface to the Fortran package Expokit.
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2025-04-22 |
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r-expm
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Computation of the matrix exponential, logarithm, sqrt, and related quantities, using traditional and modern methods.
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2025-04-22 |
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r-expint
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The exponential integrals E_1(x), E_2(x), E_n(x) and Ei(x), and the incomplete gamma function G(a, x) defined for negative values of its first argument. The package also gives easy access to the underlying C routines through an API; see the package vignette for details. A test package included in sub-directory example_API provides an implementation. C routines derived from the GNU Scientific Library <https://www.gnu.org/software/gsl/>.
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2025-04-22 |
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r-experiment
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Provides various statistical methods for designing and analyzing randomized experiments. One functionality of the package is the implementation of randomized-block and matched-pair designs based on possibly multivariate pre-treatment covariates. The package also provides the tools to analyze various randomized experiments including cluster randomized experiments, two-stage randomized experiments, randomized experiments with noncompliance, and randomized experiments with missing data.
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2025-04-22 |
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r-exif
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Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time the picture was taken on, from JPEG images. Incorporates the 'easyexif' (https://github.com/mayanklahiri/easyexif) library.
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2025-04-22 |
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r-exdex
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Performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) <doi:10.1007/s10687-015-0221-5> and Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>. Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) <doi:10.1111/1467-9868.00401>). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) <doi:10.1007/s10687-007-0034-2>, the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and a similar approach of Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3> that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided.
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2025-04-22 |
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r-excursions
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Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.
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2025-04-22 |
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r-exactranktests
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Computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel.
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2025-04-22 |
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r-ewgof
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Contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.
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2025-04-22 |
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r-exactloglintest
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Monte Carlo and MCMC goodness of fit tests for log-linear models
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2025-04-22 |
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r-evdbayes
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Provides functions for the Bayesian analysis of extreme value models, using Markov chain Monte Carlo methods. Allows the construction of both uninformative and informed prior distributions for common statistical models applied to extreme event data, including the generalized extreme value distribution.
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2025-04-22 |
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r-etm
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The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multi-state model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011 <doi:10.18637/jss.v038.i04>). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 <doi:10.1007/s00180-010-0200-x>).
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2025-04-22 |
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r-evd
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Extends simulation, distribution, quantile and density functions to univariate and multivariate parametric extreme value distributions, and provides fitting functions which calculate maximum likelihood estimates for univariate and bivariate maxima models, and for univariate and bivariate threshold models.
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2025-04-22 |
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r-esther
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Our method is a variable selection method to select active components in sparse linear mixed models in order to estimate the heritability. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability.
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2025-04-22 |
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r-estudy2
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An implementation of a most commonly used event study methodology, including both parametric and nonparametric tests. It contains variety aspects of the rate of return estimation (the core calculation is done in C++), as well as three classical for event study market models: mean adjusted returns, market adjusted returns and single-index market models. There are 6 parametric and 6 nonparametric tests provided, which examine cross-sectional daily abnormal return (see the documentation of the functions for more information). Parametric tests include tests proposed by Brown and Warner (1980) <DOI:10.1016/0304-405X(80)90002-1>, Brown and Warner (1985) <DOI:10.1016/0304-405X(85)90042-X>, Boehmer et al. (1991) <DOI:10.1016/0304-405X(91)90032-F>, Patell (1976) <DOI:10.2307/2490543>, and Lamb (1995) <DOI:10.2307/253695>. Nonparametric tests covered in estudy2 are tests described in Corrado and Zivney (1992) <DOI:10.2307/2331331>, McConnell and Muscarella (1985) <DOI:10.1016/0304-405X(85)90006-6>, Boehmer et al. (1991) <DOI:10.1016/0304-405X(91)90032-F>, Cowan (1992) <DOI:10.1007/BF00939016>, Corrado (1989) <DOI:10.1016/0304-405X(89)90064-0>, Campbell and Wasley (1993) <DOI:10.1016/0304-405X(93)90025-7>, Savickas (2003) <DOI:10.1111/1475-6803.00052>, Kolari and Pynnonen (2010) <DOI:10.1093/rfs/hhq072>. Furthermore, tests for the cumulative abnormal returns proposed by Brown and Warner (1985) <DOI:10.1016/0304-405X(85)90042-X> and Lamb (1995) <DOI:10.2307/253695> are included.
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2025-04-22 |
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r-estimatr
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Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.
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2025-04-22 |
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r-esshist
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Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.
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2025-04-22 |
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r-esreg
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Simultaneous modeling of the quantile and the expected shortfall of a response variable given a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.
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2025-04-22 |
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r-esmprep
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Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in 'esmprep' are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data.
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2025-04-22 |
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r-erboost
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Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.
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2025-04-22 |
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r-erm
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Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.
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2025-04-22 |
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r-equalden.hd
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The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.
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2025-04-22 |
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r-epiilmct
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Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. <doi:10.18637/jss.v098.i10>.
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2025-04-22 |
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r-equalcovs
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Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.
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2025-04-22 |
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r-epiilm
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Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).
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2025-04-22 |
