{"count":40975,"next":"https://ports.macports.org/api/v1/autocomplete/port/?format=json&page=701","previous":"https://ports.macports.org/api/v1/autocomplete/port/?format=json&page=699","results":[{"name":"R-parabar","description":"Progress bar for parallel tasks"},{"name":"R-paradox","description":"Define and work with parameter spaces for complex algorithms"},{"name":"R-parallelDist","description":"Parallel distance matrix computation using multiple threads"},{"name":"R-ParallelLogger","description":"Support for parallel computation, logging and function automation"},{"name":"R-parallelly","description":"Enhancing the parallel package"},{"name":"R-parallelMap","description":"Unified interface to parallelization back-ends"},{"name":"R-parallelpam","description":"Parallel partitioning-around-medoids (PAM) for big sets of data"},{"name":"R-param2moment","description":"Raw, central and standardized moments of parametric distributions"},{"name":"R-parameters","description":"Processing of model parameters"},{"name":"R-ParamHelpers","description":"Helpers for parameters in black-box optimization, tuning and machine learning"},{"name":"R-params","description":"Interface to simplify organizing parameters used in a package via external configuration files."},{"name":"R-paran","description":"Horn’s test of principal components/factors"},{"name":"R-Pareto","description":"Pareto, Piecewise Pareto and Generalized Pareto distributions"},{"name":"R-ParetoPosStable","description":"Computing, fitting and validating the PPS distribution"},{"name":"R-parglm","description":"Provides a parallel estimation method for generalized linear models without compiling with a multi-threaded LAPACK or BLAS"},{"name":"R-paropt","description":"Parameter optimizing of ODE systems"},{"name":"R-parsec","description":"Partial orders in socio-economics"},{"name":"R-parsedate","description":"Recognize and parse dates in various formats"},{"name":"R-parsermd","description":"Formal parser and related tools for R markdown documents"},{"name":"R-parsnip","description":"Common API to modeling and analysis functions"},{"name":"R-partitions","description":"Additive partitions of integers"},{"name":"R-partsm","description":"Periodic autoregressive time series models"},{"name":"R-party","description":"Computational toolbox for recursive partitioning"},{"name":"R-partykit","description":"Toolkit for recursive partytioning"},{"name":"R-pastecs","description":"Package for analysis of space-time ecological series"},{"name":"R-PASWR","description":"Probability and Statistics with R"},{"name":"R-patchwork","description":"Composer of ggplots"},{"name":"R-patrick","description":"Parameterized unit testing"},{"name":"R-pbANOVA","description":"Parametric Bootstrap for ANOVA models"},{"name":"R-pbapply","description":"Adding progress bar to *apply functions"},{"name":"R-pbdMPI","description":"Interface to MPI"},{"name":"R-pbdSLAP","description":"ScaLAPACK/PBLAS/BLACS/LAPACK library for use with pbdR"},{"name":"R-pbdZMQ","description":"Interface to ZeroMQ"},{"name":"R-pbivnorm","description":"Vectorized bivariate normal CDF"},{"name":"R-pbkrtest","description":"Parametric Bootstrap, Kenward–Roger and Satterthwaite based methods for test in mixed models"},{"name":"R-pbmcapply","description":"Tracking the progress of mc*pply with progress bar"},{"name":"R-pBrackets","description":"Plot brackets"},{"name":"R-pbs","description":"Periodic b-splines"},{"name":"R-pbv","description":"Probabilities for Bivariate Normal distribution"},{"name":"R-pcadapt","description":"Fast principal component analysis for outlier detection"},{"name":"R-pcalg","description":"Methods for graphical models and causal inference"},{"name":"R-pcaMethods","description":"Collection of PCA methods"},{"name":"R-PCAmixdata","description":"Multivariate analysis of mixed data"},{"name":"R-pcaPP","description":"Robust PCA by Projection Pursuit"},{"name":"R-pcFactorStan","description":"Stan models for the paired comparison factor model"},{"name":"R-pcgen","description":"Reconstruction of causal networks for data with random genetic effects"},{"name":"R-pchc","description":"Bayesian network learning with the PCHC"},{"name":"R-PCICt","description":"Implementation of POSIXct work-alike for 365- and 360-day calendars"},{"name":"R-pcLasso","description":"Principal Components Lasso"},{"name":"R-pcnetmeta","description":"Bayesian arm-based network meta-analysis for datasets with binary, continuous and count outcomes"}]}