{"count":42391,"next":"https://ports.macports.org/api/v1/autocomplete/port/?format=json&page=728","previous":"https://ports.macports.org/api/v1/autocomplete/port/?format=json&page=726","results":[{"name":"R-pcnetmeta","description":"Bayesian arm-based network meta-analysis for datasets with binary, continuous and count outcomes"},{"name":"R-PCovR","description":"Principal covariates regression"},{"name":"R-pcse","description":"Panel-corrected standard error estimation in R"},{"name":"R-pcts","description":"Periodically correlated and periodically integrated time series"},{"name":"R-pdc","description":"Permutation Distribution Clustering"},{"name":"R-pder","description":"Panel Data Econometrics with R"},{"name":"R-pdfCluster","description":"Cluster analysis via non-parametric density estimation"},{"name":"R-pdfetch","description":"Fetch economic and financial time series data"},{"name":"R-pdftools","description":"Text extraction, rendering and converting of PDF documents"},{"name":"R-pdist","description":"Partitioned distance function"},{"name":"R-pdp","description":"Partial Dependence Plots"},{"name":"R-pdqr","description":"Create, transform and summarize custom random variables with distribution functions"},{"name":"R-PDQutils","description":"PDQ functions via Gram Charlier, Edgeworth and Cornish Fisher approximations"},{"name":"R-pdR","description":"Threshold model and unit root tests in cross-section and time series data"},{"name":"R-PDSCE","description":"Positive Definite Sparse Covariance Estimators"},{"name":"R-PDShiny","description":"Probability Distribution Shiny"},{"name":"R-PeakError","description":"Compute the label error of peak calls"},{"name":"R-PeakSegDisk","description":"Disk-based constrained change-point detection"},{"name":"R-PeakSegDP","description":"Dynamic programming algorithm for peak detection in ChIP-Seq data"},{"name":"R-PeakSegJoint","description":"Joint peak detection in several ChIP-Seq samples"},{"name":"R-PeakSegOptimal","description":"Optimal segmentation subject to up-down constraints"},{"name":"R-PearsonDS","description":"Pearson Distribution System"},{"name":"R-pec","description":"Prediction error curves for risk prediction models in survival analysis"},{"name":"R-pema","description":"Penalized Meta-Analysis"},{"name":"R-penalized","description":"Fitting possibly high-dimensional penalized regression models"},{"name":"R-penalizedSVM","description":"Feature selection SVM using penalty functions"},{"name":"R-penaltyLearning","description":"Implementations of algorithms from Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression"},{"name":"R-penppml","description":"Penalized Poisson Pseudo Maximum Likelihood Regression"},{"name":"R-pense","description":"Penalized elastic net s/mm-estimator of regression"},{"name":"R-peperr","description":"Parallelised Estimation of Prediction Error"},{"name":"R-peppm","description":"Piece-wise exponential distribution with random time grids"},{"name":"R-peramo","description":"Permutation tests for randomization model"},{"name":"R-performance","description":"Assessment of regression models performance"},{"name":"R-PerformanceAnalytics","description":"Econometric tools for performance and risk analysis"},{"name":"R-perm","description":"Exact or asymptotic permutation tests"},{"name":"R-perms","description":"Fast permutation computation"},{"name":"R-permutations","description":"The symmetric group: permutations of a finite set"},{"name":"R-permute","description":"Restricted permutations"},{"name":"R-permutes","description":"Permutation tests for time series data"},{"name":"R-perry","description":"Resampling-based prediction error estimation for regression models"},{"name":"R-perryExamples","description":"Examples for integrating prediction error estimation into regression models"},{"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"},{"name":"R-pgmm","description":"Parsimonious Gaussian Mixture Models"},{"name":"R-pgnorm","description":"p-Generalized normal distribution"}]}