{"count":40910,"next":"https://ports.macports.org/api/v1/autocomplete/port/?format=json&page=674","previous":"https://ports.macports.org/api/v1/autocomplete/port/?format=json&page=672","results":[{"name":"R-gplm","description":"Generalized Partial Linear Models"},{"name":"R-gplots","description":"Various R programming tools for plotting data"},{"name":"R-GPM","description":"Gaussian process modeling of multi-response and possibly noisy datasets"},{"name":"R-GPoM","description":"Generalized Polynomial Modelling"},{"name":"R-gppm","description":"Implementation of Gaussian process panel modeling in R"},{"name":"R-gps","description":"General P-splines are non-uniform B-splines penalized by a general difference penalty"},{"name":"R-gptr","description":"R interface to the OpenAI ChatGPT API"},{"name":"R-gptstudio","description":"Use large language models directly in your development environment"},{"name":"R-grafzahl","description":"Supervised machine learning for textual data using transformers and Quanteda"},{"name":"R-gRain","description":"Graphical Independence Networks"},{"name":"R-GramQuad","description":"Gram Quadrature"},{"name":"R-grangers","description":"Inference on Granger causality in the frequency domain"},{"name":"R-graph","description":"Package that implements some simple graph-handling capabilities"},{"name":"R-graphclust","description":"Hierarchical graph clustering for a collection of networks"},{"name":"R-graphicalVAR","description":"Graphical VAR for experience sampling data"},{"name":"R-graphite","description":"GRAPH interaction from pathway topological environment"},{"name":"R-graphlayouts","description":"Additional layout algorithms for network visualizations"},{"name":"R-graphsim","description":"Simulate expression data from igraph networks"},{"name":"R-gratia","description":"Graceful ggplot-based graphics and other functions for GAMs fitted with R-mgcv"},{"name":"R-gratis","description":"Generating time series with diverse and controllable characteristics"},{"name":"R-gRaven","description":"Bayes Nets"},{"name":"R-gRbase","description":"Graphical modelling in R"},{"name":"R-gRc","description":"Inference in graphical Gaussian models with edge and vertex symmetries"},{"name":"R-GREMLINS","description":"Generalized multipartite networks"},{"name":"R-gretel","description":"Generalized path analysis for social networks"},{"name":"R-greybox","description":"Toolbox for model building and forecasting"},{"name":"R-grf","description":"Generalized Random Forests"},{"name":"R-gridBase","description":"Integration of base and grid graphics"},{"name":"R-gridDebug","description":"Debugging for grid graphics"},{"name":"R-gridExtra","description":"Miscellaneous functions for grid graphics"},{"name":"R-gridGraphics","description":"Redraw base graphics using grid graphics"},{"name":"R-gridGraphviz","description":"Draw graphs with grid"},{"name":"R-gridpattern","description":"Grid pattern grobs"},{"name":"R-gridSVG","description":"Export grid graphics as SVG"},{"name":"R-gridtext","description":"Improved text rendering support for grid graphics"},{"name":"R-gRim","description":"Graphical Interaction Models"},{"name":"R-grImport","description":"Functions for converting, importing and drawing PostScript pictures in R plots"},{"name":"R-grImport2","description":"Import SVG graphics"},{"name":"R-GRNNs","description":"General Regression Neural Networks package"},{"name":"R-groc","description":"Generalized Regression on Orthogonal Components"},{"name":"R-groHMM","description":"GRO-seq analysis pipeline"},{"name":"R-groundhog","description":"Version-control for CRAN, GitHub and GitLab packages"},{"name":"R-GroupBN","description":"Infer group Bayesian networks via hierarchical feature clustering"},{"name":"R-groupdata2","description":"Create groups from data"},{"name":"R-groupr","description":"Groups with inapplicable values"},{"name":"R-grpnet","description":"Group elastic net-regularized GLM"},{"name":"R-grpreg","description":"Regularization paths for regression models with grouped covariates"},{"name":"R-GSA","description":"Gene Set Analysis"},{"name":"R-gsbDesign","description":"Group Sequential Bayes Design"},{"name":"R-gsDesign","description":"Group Sequential Design"}]}