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"name": "R-edgeR",
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"name": "R-EDISON",
"description": "Network reconstruction and changepoint detection"
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{
"name": "R-EDMeasure",
"description": "Energy-based dependence measures"
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"name": "R-effects",
"description": "Effect displays for linear, generalized linear and other models"
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{
"name": "R-effectsize",
"description": "Indices of effect size"
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{
"name": "R-effsize",
"description": "Efficient effect size computation"
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{
"name": "R-eFRED",
"description": "Fetch data from the Federal Reserve Economic Database"
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{
"name": "R-egg",
"description": "Miscellaneous functions to help customise ggplot2 objects"
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{
"name": "R-eha",
"description": "Event History Analysis"
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{
"name": "R-eicm",
"description": "Explicit Interaction Community Models"
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{
"name": "R-eigenmodel",
"description": "Semi-parametric factor and regression models for symmetric relational data"
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{
"name": "R-EigenR",
"description": "Complex Matrix Algebra with Eigen"
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{
"name": "R-eimpute",
"description": "Efficiently impute large scale incomplete matrix"
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{
"name": "R-einet",
"description": "Methods and utilities for causal emergence"
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{
"name": "R-einsum",
"description": "Einstein Summation"
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{
"name": "R-EIX",
"description": "Explain interactions in XGBoost"
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{
"name": "R-elasticnet",
"description": "Elastic net for sparse estimation and sparse PCA"
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{
"name": "R-elfDistr",
"description": "Kumaraswamy complementary Weibull geometric (Kw-CWG) probability distribution"
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{
"name": "R-elhmc",
"description": "Sampling from an Empirical Likelihood Bayesian posterior of parameters using Hamiltonian Monte Carlo"
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{
"name": "R-Elja",
"description": "Linear, logistic and generalized linear models regressions for the EnvWAS/EWAS approach"
},
{
"name": "R-ellipse",
"description": "Functions for drawing ellipses and ellipse-like confidence regions"
},
{
"name": "R-ellipsis",
"description": "Tool for extending functions"
},
{
"name": "R-elliptic",
"description": "Weierstrass and Jacobi elliptic functions"
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{
"name": "R-elmNNRcpp",
"description": "Extreme learning machine algorithm"
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{
"name": "R-elrm",
"description": "Exact Logistic Regression via MCMC"
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{
"name": "R-emayili",
"description": "Light, simple tool for sending e-mails with minimal dependencies"
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{
"name": "R-emBayes",
"description": "Robust Bayesian variable selection via expectation maximization"
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{
"name": "R-EMCluster",
"description": "EM algorithm for model-based clustering of finite mixture Gaussian distribution"
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{
"name": "R-emdbook",
"description": "Support functions and data for Ecological Models and Data"
},
{
"name": "R-emg",
"description": "Exponentially-Modified Gaussian (EMG) distribution"
},
{
"name": "R-emmeans",
"description": "Estimated marginal means, aka least-squares means"
},
{
"name": "R-emoa",
"description": "Evolutionary Multiobjective Optimization Algorithms"
},
{
"name": "R-emojifont",
"description": "Emoji and fontawesome in base and ggplot2 graphics both"
},
{
"name": "R-emplik",
"description": "Empirical likelihood ratio for censored/truncated data"
},
{
"name": "R-emulator",
"description": "Bayesian emulation of computer programs"
},
{
"name": "R-eNchange",
"description": "Ensemble methods for multiple change-point detection"
},
{
"name": "R-energy",
"description": "Multivariate inference via the energy of data"
},
{
"name": "R-english",
"description": "Translate integers into English"
},
{
"name": "R-enrichR",
"description": "R interface to all Enrichr databases"
},
{
"name": "R-entropy",
"description": "Estimation of entropy, mutual information and related quantities"
},
{
"name": "R-EntropyMCMC",
"description": "MCMC simulation and convergence evaluation using entropy and Kullback–Leibler divergence estimation"
},
{
"name": "R-EnvStats",
"description": "Environmental Statistics"
},
{
"name": "R-epmrob",
"description": "Robust estimation of probit models with endogeneity"
},
{
"name": "R-EQL",
"description": "Extended Quasi-Likelihood function"
},
{
"name": "R-ergm",
"description": "Fit, simulate and diagnose exponential-family models for networks"
},
{
"name": "R-ergm.count",
"description": "Fit, simulate and diagnose exponential-family models for networks with count edges"
},
{
"name": "R-ergm.multi",
"description": "Fit, simulate and diagnose exponential-family models for multiple or multilayer networks"
},
{
"name": "R-ergm.userterms",
"description": "Template package to demonstrate the use of user-specified statistics for use in ergm models"
},
{
"name": "R-ergMargins",
"description": "Process analysis for exponential random graph models"
},
{
"name": "R-ergmgp",
"description": "Tools for modelling ERGM generating processes"
}
]
}