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"description": "Principal covariates regression"
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"name": "R-pcse",
"description": "Panel-corrected standard error estimation in R"
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"description": "Periodically correlated and periodically integrated time series"
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"name": "R-pdc",
"description": "Permutation Distribution Clustering"
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"name": "R-pder",
"description": "Panel Data Econometrics with R"
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"name": "R-pdfCluster",
"description": "Cluster analysis via non-parametric density estimation"
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"name": "R-pdfetch",
"description": "Fetch economic and financial time series data"
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{
"name": "R-pdftools",
"description": "Text extraction, rendering and converting of PDF documents"
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{
"name": "R-pdist",
"description": "Partitioned distance function"
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"name": "R-pdp",
"description": "Partial Dependence Plots"
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{
"name": "R-pdqr",
"description": "Create, transform and summarize custom random variables with distribution functions"
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"name": "R-PDQutils",
"description": "PDQ functions via Gram Charlier, Edgeworth and Cornish Fisher approximations"
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"name": "R-pdR",
"description": "Threshold model and unit root tests in cross-section and time series data"
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"name": "R-PDSCE",
"description": "Positive Definite Sparse Covariance Estimators"
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"name": "R-PDShiny",
"description": "Probability Distribution Shiny"
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"name": "R-PeakError",
"description": "Compute the label error of peak calls"
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"description": "Disk-based constrained change-point detection"
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"name": "R-PeakSegDP",
"description": "Dynamic programming algorithm for peak detection in ChIP-Seq data"
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{
"name": "R-PeakSegJoint",
"description": "Joint peak detection in several ChIP-Seq samples"
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{
"name": "R-PeakSegOptimal",
"description": "Optimal segmentation subject to up-down constraints"
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{
"name": "R-PearsonDS",
"description": "Pearson Distribution System"
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"name": "R-pec",
"description": "Prediction error curves for risk prediction models in survival analysis"
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"name": "R-pema",
"description": "Penalized Meta-Analysis"
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"name": "R-penalized",
"description": "Fitting possibly high-dimensional penalized regression models"
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"name": "R-penalizedSVM",
"description": "Feature selection SVM using penalty functions"
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"description": "Implementations of algorithms from Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression"
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{
"name": "R-penppml",
"description": "Penalized Poisson Pseudo Maximum Likelihood Regression"
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{
"name": "R-pense",
"description": "Penalized elastic net s/mm-estimator of regression"
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{
"name": "R-peperr",
"description": "Parallelised Estimation of Prediction Error"
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{
"name": "R-peppm",
"description": "Piece-wise exponential distribution with random time grids"
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{
"name": "R-peramo",
"description": "Permutation tests for randomization model"
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{
"name": "R-performance",
"description": "Assessment of regression models performance"
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{
"name": "R-PerformanceAnalytics",
"description": "Econometric tools for performance and risk analysis"
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{
"name": "R-perm",
"description": "Exact or asymptotic permutation tests"
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{
"name": "R-perms",
"description": "Fast permutation computation"
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{
"name": "R-permutations",
"description": "The symmetric group: permutations of a finite set"
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{
"name": "R-permute",
"description": "Restricted permutations"
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{
"name": "R-permutes",
"description": "Permutation tests for time series data"
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{
"name": "R-perry",
"description": "Resampling-based prediction error estimation for regression models"
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{
"name": "R-perryExamples",
"description": "Examples for integrating prediction error estimation into regression models"
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{
"name": "R-perturbR",
"description": "Random perturbation of count matrices"
},
{
"name": "R-petrinetR",
"description": "Building, visualizing, exporting and replaying Petri nets"
},
{
"name": "R-pexm",
"description": "Loading a JAGS module for the piecewise exponential distribution"
},
{
"name": "R-pfr",
"description": "Interface to the C++ Pf library"
},
{
"name": "R-pg",
"description": "Polya Gamma distribution sampler"
},
{
"name": "R-pgdraw",
"description": "Generate random samples from the polya-gamma distribution"
},
{
"name": "R-pglm",
"description": "Panel Generalized Linear Models"
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{
"name": "R-pgmm",
"description": "Parsimonious Gaussian Mixture Models"
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{
"name": "R-pgnorm",
"description": "p-Generalized normal distribution"
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