{"count":51973,"next":"https://ports.macports.org/api/v1/ports/?format=json&ordering=-created_at&page=230","previous":"https://ports.macports.org/api/v1/ports/?format=json&ordering=-created_at&page=228","results":[{"name":"py311-tensorflow-metadata","portdir":"python/py-tensorflow-metadata","version":"1.14.0","license":"Apache-2","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/tensorflow/metadata","description":"Library and standards for schema and statistics.","long_description":"TensorFlow Metadata provides standard representations for metadata that are useful when training machine learning models with TensorFlow.","active":false,"categories":["python"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py311-build","py311-installer","clang-17"]},{"type":"lib","ports":["python311"]},{"type":"run","ports":["py311-protobuf3","py311-google-api"]}],"depends_on":[{"type":"lib","ports":["py-tensorflow-metadata"]},{"type":"run","ports":["py311-tensorflow-datasets"]}]},{"name":"py311-soundfile","portdir":"python/py-soundfile","version":"0.13.1","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/bastibe/python-soundfile","description":"SoundFile is an audio library based on libsndfile, CFFI, and NumPy.","long_description":"SoundFile is an audio library based on libsndfile, CFFI, and NumPy. SoundFile can read and write sound files. File reading/writing is supported through libsndfile, which is a free, cross-platform, open-source (LGPL) library for reading and writing many different sampled sound file formats that runs on many platforms including Windows, OS X, and Unix. It is accessed through CFFI, which is a foreign function interface for Python calling C code. CFFI is supported for CPython 2.6+, 3.x and PyPy 2.0+. SoundFile represents audio data as NumPy arrays.","active":true,"categories":["audio","python"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py311-installer","py311-setuptools","py311-wheel","clang-18","py311-build"]},{"type":"lib","ports":["libsndfile","python311","py311-cffi"]},{"type":"run","ports":["py311-numpy"]},{"type":"test","ports":["py311-pytest"]}],"depends_on":[{"type":"run","ports":["py311-librosa"]}]},{"name":"py311-seqeval","portdir":"python/py-seqeval","version":"1.2.2","license":"MIT","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/chakki-works/seqeval","description":"Testing framework for sequence labeling","long_description":"seqeval is a Python framework for sequence labeling evaluation. seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.","active":true,"categories":["python"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-18","py311-setuptools","py311-wheel","py311-setuptools_scm","py311-build","py311-installer"]},{"type":"lib","ports":["python311"]},{"type":"run","ports":["py311-numpy","py311-scikit-learn"]}],"depends_on":[{"type":"lib","ports":["py-seqeval"]}]},{"name":"py311-sacremoses","portdir":"python/py-sacremoses","version":"0.1.1","license":"LGPL-2.1+","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/alvations/sacremoses","description":"Python port of Moses tokenizer, truecaser and normalizer","long_description":"Python port of Moses tokenizer, truecaser and normalizer","active":true,"categories":["python"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-18","py311-build","py311-installer","py311-setuptools","py311-wheel"]},{"type":"lib","ports":["python311"]},{"type":"run","ports":["py311-regex","py311-click","py311-joblib","py311-tqdm"]}],"depends_on":[]},{"name":"py311-sacrebleu","portdir":"python/py-sacrebleu","version":"2.4.3","license":"Apache-2","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/mjpost/sacrebleu","description":"Hassle-free computation of shareable, comparable, and reproducible BLEU, chrF, and TER scores","long_description":"SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores. Inspired by Rico Sennrich's multi-bleu-detok.perl, it produces the official WMT scores but works with plain text. It also knows all the standard test sets and handles downloading, processing, and tokenization for you.","active":true,"categories":["python"],"maintainers":[],"variants":["ja"],"dependencies":[{"type":"build","ports":["py311-wheel","clang-18","py311-build","py311-installer","py311-setuptools"]},{"type":"lib","ports":["python311"]},{"type":"run","ports":["py311-portalocker"]},{"type":"test","ports":["py311-pytest"]}],"depends_on":[]},{"name":"py311-promise","portdir":"python/py-promise","version":"2.3","license":"MIT","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/syrusakbary/promise","description":"Promises/A implementation for Python","long_description":"Promises/A implementation for Python","active":true,"categories":["python"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py311-build","py311-installer","py311-setuptools","py311-wheel","clang-18"]},{"type":"lib","ports":["python311"]}],"depends_on":[{"type":"run","ports":["py311-tensorflow-datasets"]}]},{"name":"py311-fugashi","portdir":"python/py-fugashi","version":"1.5.1","license":"MIT","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://github.com/polm/fugashi","description":"A Cython MeCab wrapper for fast, pythonic Japanese tokenization.","long_description":"fugashi is a Cython wrapper for MeCab, a Japanese tokenizer and morphological analysis tool.","active":true,"categories":["textproc","python"],"maintainers":[],"variants":["universal"],"dependencies":[{"type":"build","ports":["py311-cython","clang-18","py311-build","py311-installer","py311-setuptools","py311-wheel","py311-setuptools_scm"]},{"type":"fetch","ports":["git"]},{"type":"lib","ports":["python311","mecab-base"]},{"type":"test","ports":["py311-pytest","py311-ipadic"]}],"depends_on":[]},{"name":"py311-fire","portdir":"python/py-fire","version":"0.7.0","license":"Apache-2","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/google/python-fire","description":"A library for automatically generating command line interfaces.","long_description":"A library for automatically generating command line interfaces.","active":true,"categories":["python"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py311-wheel","clang-18","py311-build","py311-installer","py311-setuptools"]},{"type":"lib","ports":["python311"]},{"type":"run","ports":["py311-termcolor"]},{"type":"test","ports":["py311-hypothesis","py311-pytest","py311-levenshtein"]}],"depends_on":[]},{"name":"dynamix-chart-width-control-gui","portdir":"games/dynamix-chart-width-control","version":"1.3.4","license":"MIT","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://github.com/AXIS5hacker/DNX-Note-Width-Changer","description":"A tool for changing the note width in a Dynamix Fanmade Chart","long_description":"A tool for changing the note width in a Dynamix Fanmade Chart. Compatible with xml charts produced with DynaMaker. This is the Qt GUI version.","active":true,"categories":["games"],"maintainers":[{"name":"i0ntempest","github":"i0ntempest","ports_count":271}],"variants":["debug","universal"],"dependencies":[{"type":"build","ports":["cmake","makeicns","qt6-qttools","clang-20"]},{"type":"lib","ports":["qt6-qtbase"]}],"depends_on":[]},{"name":"R-yahoofinancer","portdir":"R/R-yahoofinancer","version":"0.3.0","license":"MIT","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://yahoofinancer.rsquaredacademy.com","description":"Fetch data from Yahoo Finance API","long_description":"Fetch data from Yahoo Finance API","active":true,"categories":["science","www","finance","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-jsonlite","R-magrittr","R-stringr","R-purrr","R-lubridate","R-httr","R-CRAN-recommended","R-R6","R-curl"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-testthat","R-covr","R-httptest"]}],"depends_on":[]},{"name":"R-tgp","portdir":"R/R-tgp","version":"2.4-23","license":"LGPL","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://bobby.gramacy.com/r_packages/tgp","description":"Bayesian Treed Gaussian Process models","long_description":"Bayesian Treed Gaussian Process models","active":true,"categories":["science","math","R"],"maintainers":[],"variants":["clang13","clang14","clang15","clang16","clang17","clang18","clang19","clangdevel","g95","gcc10","gcc11","gcc12","gcc13","gcc14","gccdevel","gfortran"],"dependencies":[{"type":"build","ports":["clang-19","R","gcc14"]},{"type":"lib","ports":["libgcc14","libgcc","R-maptree","R-CRAN-recommended"]},{"type":"run","ports":["R"]}],"depends_on":[{"type":"lib","ports":["R-c060","R-laGP","R-penalizedSVM"]}]},{"name":"R-psp","portdir":"R/R-psp","version":"1.0.2","license":"GPL-3+","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=psp","description":"Parameter Space Partitioning MCMC for global model evaluation","long_description":"Parameter Space Partitioning MCMC for global model evaluation","active":true,"categories":["science","math","R"],"maintainers":[],"variants":["clang13","clang14","clang15","clang16","clang17","clang18","clang19","clangdevel","g95","gcc10","gcc11","gcc12","gcc13","gcc14","gccdevel","gfortran"],"dependencies":[{"type":"build","ports":["clang-19","R","gcc14"]},{"type":"lib","ports":["R-data.table","R-CRAN-recommended","libgcc14","libgcc","R-Rcpp","R-RcppArmadillo"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-testthat"]}],"depends_on":[]},{"name":"R-peperr","portdir":"R/R-peperr","version":"1.5","license":"GPL-2+","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://fbertran.github.io/peperr","description":"Parallelised Estimation of Prediction Error","long_description":"Parallelised Estimation of Prediction Error","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["R","clang-19"]},{"type":"lib","ports":["R-snowfall","R-CRAN-recommended"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-penalized","R-locfit"]}],"depends_on":[{"type":"lib","ports":["R-c060"]}]},{"name":"R-nbfar","portdir":"R/R-nbfar","version":"0.1","license":"GPL-3+","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=nbfar","description":"Negative binomial factor regression models","long_description":"Negative binomial factor regression models","active":true,"categories":["science","math","R"],"maintainers":[],"variants":["clang13","clang14","clang15","clang16","clang17","clang18","clang19","clangdevel","g95","gcc10","gcc11","gcc12","gcc13","gcc14","gccdevel","gfortran"],"dependencies":[{"type":"build","ports":["clang-19","R","gcc14"]},{"type":"lib","ports":["R-RcppParallel","R-magrittr","R-glmnet","R-rrpack","R-mpath","R-CRAN-recommended","libgcc14","libgcc","R-Rcpp","R-RcppArmadillo"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-knitr","R-markdown","R-spelling"]}],"depends_on":[]},{"name":"R-mldr","portdir":"R/R-mldr","version":"0.4.3","license":"LGPL-3+","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=mldr","description":"Exploratory data analysis and manipulation of multi-label data sets","long_description":"Exploratory data analysis and manipulation of multi-label data sets","active":true,"categories":["science","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-circlize","R-shiny","R-CRAN-recommended","R-XML"]},{"type":"run","ports":["R"]}],"depends_on":[]},{"name":"R-micss","portdir":"R/R-micss","version":"0.2.0","license":"GPL-2","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=micss","description":"Modified Iterative Cumulative Sum of Squares algorithm","long_description":"Modified Iterative Cumulative Sum of Squares algorithm","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["R","clang-19"]},{"type":"lib","ports":["R-dplyr","R-CRAN-recommended"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-testthat"]}],"depends_on":[]},{"name":"R-maptree","portdir":"R/R-maptree","version":"1.4-8","license":"Permissive","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=maptree","description":"Mapping, pruning and graphing tree models","long_description":"Mapping, pruning and graphing tree models","active":true,"categories":["science","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["R","clang-19"]},{"type":"lib","ports":["R-CRAN-recommended"]},{"type":"run","ports":["R"]}],"depends_on":[{"type":"lib","ports":["R-tgp"]}]},{"name":"R-invgamstochvol","portdir":"R/R-invgamstochvol","version":"1.0.0","license":"MIT","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=invgamstochvol","description":"Log-likelihood for an inverse Gamma stochastic volatility model","long_description":"Log-likelihood for an inverse Gamma stochastic volatility model","active":true,"categories":["science","math","R"],"maintainers":[],"variants":["clang13","clang14","clang15","clang16","clang17","clang18","clang19","clangdevel","g95","gcc10","gcc11","gcc12","gcc13","gcc14","gccdevel","gfortran"],"dependencies":[{"type":"build","ports":["clang-19","R","gcc14"]},{"type":"lib","ports":["R-CRAN-recommended","libgcc14","libgcc","R-Rcpp","R-RcppArmadillo"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-knitr","R-rmarkdown","R-spelling"]}],"depends_on":[]},{"name":"R-instantiate","portdir":"R/R-instantiate","version":"0.2.3","license":"MIT","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://wlandau.github.io/instantiate","description":"Pre-compiled CmdStan models in R packages","long_description":"Pre-compiled CmdStan models in R packages","active":true,"categories":["devel","science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-callr","R-CRAN-recommended","R-fs","R-rlang"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-withr","R-knitr","R-testthat","R-markdown","R-rmarkdown","R-cmdstanr"]}],"depends_on":[]},{"name":"R-ggpmisc","portdir":"R/R-ggpmisc","version":"0.6.0","license":"GPL-2+","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://docs.r4photobiology.info/ggpmisc","description":"Miscellaneous extensions to R-ggplot2","long_description":"Miscellaneous extensions to R-ggplot2","active":true,"categories":["graphics","science","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-tibble","R-dplyr","R-generics","R-ggplot2","R-lubridate","R-scales","R-polynom","R-quantreg","R-multcomp","R-ggpp","R-multcompView","R-confintr","R-splus2R","R-lmodel2","R-CRAN-recommended","R-rlang","R-plyr"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-knitr","R-testthat","R-rmarkdown","R-broom","R-vdiffr","R-gginnards","R-ggrepel","R-broom.mixed"]}],"depends_on":[]},{"name":"R-gets","portdir":"R/R-gets","version":"0.38","license":"GPL-2+","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/gsucarrat/gets","description":"General-to-Specific (GETS) modelling and indicator saturation methods","long_description":"General-to-Specific (GETS) modelling and indicator saturation methods","active":true,"categories":["science","finance","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-zoo","R-CRAN-recommended"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-xtable","R-testthat","R-lgarch"]}],"depends_on":[{"type":"lib","ports":["R-ardl.nardl"]},{"type":"test","ports":["R-tidyfit"]}]},{"name":"R-fmesher","portdir":"R/R-fmesher","version":"0.2.0","license":"MPL-2","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://inlabru-org.github.io/fmesher","description":"Triangle meshes and related geometry tools","long_description":"Triangle meshes and related geometry tools","active":true,"categories":["science","math","R","geometry"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-sp","R-lifecycle","R-tibble","R-withr","R-dplyr","R-sf","R-CRAN-recommended","R-Rcpp","R-rlang"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-gsl","R-ggplot2","R-knitr","R-testthat","R-rmarkdown","R-rgl","R-splancs","R-terra","R-tidyterra"]}],"depends_on":[{"type":"lib","ports":["R-FRK","R-disaggregation","R-rSPDE","R-sdmTMB"]}]},{"name":"R-dynamac","portdir":"R/R-dynamac","version":"0.1.12","license":"GPL-2+","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=dynamac","description":"Dynamic simulation and testing for single-equation ARDL models","long_description":"Dynamic simulation and testing for single-equation ARDL models","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-lmtest","R-CRAN-recommended"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-knitr","R-testthat","R-rmarkdown","R-urca"]}],"depends_on":[{"type":"lib","ports":["R-bootCT"]},{"type":"test","ports":["R-ardl.nardl"]}]},{"name":"R-brms.mmrm","portdir":"R/R-brms.mmrm","version":"1.1.1","license":"MIT","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://openpharma.github.io/brms.mmrm","description":"Bayesian MMRMs using R-brms","long_description":"Bayesian MMRMs using R-brms","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-zoo","R-dplyr","R-purrr","R-tidyr","R-tidyselect","R-ggplot2","R-brms","R-ggridges","R-posterior","R-trialr","R-CRAN-recommended","R-rlang","R-tibble"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-BH","R-Rcpp","R-RcppEigen","R-RcppParallel","R-StanHeaders","R-knitr","R-rstan","R-testthat","R-markdown","R-rmarkdown","R-emmeans","R-gt","R-fst","R-gtsummary","R-mmrm"]}],"depends_on":[]},{"name":"R-ardl.nardl","portdir":"R/R-ardl.nardl","version":"1.3.0","license":"GPL-2+","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=ardl.nardl","description":"Linear and non-linear autoregressive distributed lag models","long_description":"Linear and non-linear autoregressive distributed lag models","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-stringr","R-dplyr","R-purrr","R-tidyselect","R-lmtest","R-tseries","R-texreg","R-rlist","R-car","R-nardl","R-gets","R-CRAN-recommended","R-plyr","R-sandwich"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-dynamac"]}],"depends_on":[]},{"name":"R-WPKDE","portdir":"R/R-WPKDE","version":"0.1","license":"GPL","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=WPKDE","description":"Weighted Piece-wise Kernel Density Estimation","long_description":"Weighted Piece-wise Kernel Density Estimation","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["R","clang-19"]},{"type":"lib","ports":["R-CRAN-recommended"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-mvtnorm"]}],"depends_on":[]},{"name":"R-RTMB","portdir":"R/R-RTMB","version":"1.6","license":"GPL-2+","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=RTMB","description":"R bindings for TMB","long_description":"Native R interface to TMB (Template Model Builder), so models can be written entirely in R rather than C++.","active":true,"categories":["science","math","R"],"maintainers":[],"variants":["clang13","clang14","clang15","clang16","clang17","clang18","clang19","clangdevel","g95","gcc10","gcc11","gcc12","gcc13","gcc14","gccdevel","gfortran"],"dependencies":[{"type":"build","ports":["clang-19","R","gcc14"]},{"type":"lib","ports":["R-TMB","R-CRAN-recommended","libgcc14","libgcc","R-Rcpp","R-RcppEigen"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-numDeriv","R-knitr","R-igraph","R-rmarkdown","R-tinytest"]}],"depends_on":[{"type":"lib","ports":["R-LaMa"]}]},{"name":"R-RFCCA","portdir":"R/R-RFCCA","version":"2.0.0","license":"GPL-3+","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://github.com/calakus/RFCCA","description":"Random Forest with Canonical Correlation Analysis","long_description":"Random Forest with Canonical Correlation Analysis","active":true,"categories":["science","math","R"],"maintainers":[],"variants":["clang13","clang14","clang15","clang16","clang17","clang18","clang19","clangdevel","g95","gcc10","gcc11","gcc12","gcc13","gcc14","gccdevel","gfortran"],"dependencies":[{"type":"build","ports":["clang-19","R","gcc14"]},{"type":"lib","ports":["R-CRAN-recommended","libgcc14","libgcc","R-PMA","R-CCA"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-knitr","R-testthat","R-rmarkdown"]}],"depends_on":[]},{"name":"R-MVLM","portdir":"R/R-MVLM","version":"0.1.4","license":"GPL-2+","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=MVLM","description":"Multivariate linear model with analytic p-values","long_description":"Multivariate linear model with analytic p-values","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["R","clang-19"]},{"type":"lib","ports":["R-CompQuadForm","R-CRAN-recommended"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-rmarkdown","R-knitr"]}],"depends_on":[]},{"name":"R-FuzzyClass","portdir":"R/R-FuzzyClass","version":"0.1.6","license":"MIT","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/leapigufpb/FuzzyClass","description":"Fuzzy and non-fuzzy classifiers","long_description":"Fuzzy and non-fuzzy classifiers","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-purrr","R-Rdpack","R-doParallel","R-foreach","R-rootSolve","R-caTools","R-EnvStats","R-trapezoid","R-CRAN-recommended","R-e1071","R-mvtnorm"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-dplyr","R-knitr","R-testthat","R-rmarkdown","R-maxLik"]}],"depends_on":[]},{"name":"R-FatTailsR","portdir":"R/R-FatTailsR","version":"1.8-5","license":"GPL-2","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://www.inmodelia.com/fattailsr-en.html","description":"Kiener distributions and fat tails in finance","long_description":"Kiener distributions and fat tails in finance","active":true,"categories":["science","math","finance","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-CRAN-recommended","R-timeSeries","R-minpack.lm"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-xts","R-zoo"]}],"depends_on":[{"type":"test","ports":["R-fitteR"]}]},{"name":"R-ExprNet","portdir":"R/R-ExprNet","version":"1.0.0","license":"GPL-3+","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=ExprNet","description":"Characterizing differential expression of selected edges on a network","long_description":"Characterizing differential expression of selected edges on a network","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-19","R"]},{"type":"lib","ports":["R-doParallel","R-CRAN-recommended","R-here","R-foreach","R-igraph"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-testthat","R-readr"]}],"depends_on":[]},{"name":"R-Corbi","portdir":"R/R-Corbi","version":"0.6-2","license":"GPL-2+","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=Corbi","description":"Collection of rudimentary bioinformatics tools","long_description":"Collection of rudimentary bioinformatics tools","active":true,"categories":["science","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["R","clang-19"]},{"type":"lib","ports":["R-CRF","R-CRAN-recommended","R-igraph"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-mpmi","R-knitr","R-rmarkdown","R-matrixcalc","R-fitdistrplus","R-BiocParallel"]}],"depends_on":[]},{"name":"R-CRF","portdir":"R/R-CRF","version":"0.4-3","license":"GPL-2+","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://github.com/wulingyun/CRF","description":"Conditional Random Fields","long_description":"Conditional Random Fields","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["R","clang-19"]},{"type":"lib","ports":["R-CRAN-recommended"]},{"type":"run","ports":["R"]},{"type":"test","ports":["R-knitr","R-rmarkdown","R-Rglpk"]}],"depends_on":[{"type":"lib","ports":["R-Corbi"]}]},{"name":"R-CCA","portdir":"R/R-CCA","version":"1.2.2","license":"GPL-2+","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://cran.r-project.org/package=CCA","description":"Canonical Correlation Analysis","long_description":"Canonical Correlation Analysis","active":true,"categories":["science","math","R"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["R","clang-19"]},{"type":"lib","ports":["R-fields","R-fda","R-CRAN-recommended"]},{"type":"run","ports":["R"]}],"depends_on":[{"type":"lib","ports":["R-RFCCA"]}]},{"name":"uvw2","portdir":"devel/uvw","version":"2.12.1","license":"MIT","platforms":"darwin","epoch":0,"replaced_by":null,"homepage":"https://github.com/skypjack/uvw","description":"Header-only, event based, tiny and easy to use libuv wrapper in modern C++","long_description":"Header-only, event based, tiny and easy to use libuv wrapper in modern C++","active":true,"categories":["devel"],"maintainers":[],"variants":["debug","universal"],"dependencies":[{"type":"build","ports":["cmake","pkgconfig","clang-20"]},{"type":"lib","ports":["libuv"]}],"depends_on":[]},{"name":"py311-tobler","portdir":"python/py-tobler","version":"0.14.0","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://pysal.org/tobler/","description":"Spatial interpolation, Dasymetric Mapping, & Change of Support (tobler)","long_description":"The PySAL tobler is a library for areal interpolation and dasymetric mapping.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py311-build","clang-18","py311-setuptools_scm","py311-wheel","py311-setuptools","py311-installer"]},{"type":"lib","ports":["python311"]},{"type":"run","ports":["py311-rasterio","py311-geopandas","py311-rasterstats","py311-libpysal","py311-statsmodels","py311-tqdm","py311-scipy","py311-joblib","py311-pandas","py311-numpy"]}],"depends_on":[{"type":"lib","ports":["py311-pysal"]}]},{"name":"py310-tobler","portdir":"python/py-tobler","version":"0.14.0","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://pysal.org/tobler/","description":"Spatial interpolation, Dasymetric Mapping, & Change of Support (tobler)","long_description":"The PySAL tobler is a library for areal interpolation and dasymetric mapping.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py310-setuptools","clang-18","py310-installer","py310-build","py310-wheel","py310-setuptools_scm"]},{"type":"lib","ports":["python310"]},{"type":"run","ports":["py310-rasterio","py310-geopandas","py310-rasterstats","py310-libpysal","py310-joblib","py310-tqdm","py310-statsmodels","py310-pandas","py310-scipy","py310-numpy"]}],"depends_on":[{"type":"lib","ports":["py310-pysal"]}]},{"name":"py39-tobler","portdir":"python/py-tobler","version":"0.12.1","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://pysal.org/tobler/","description":"Spatial interpolation, Dasymetric Mapping, & Change of Support (tobler)","long_description":"The PySAL tobler is a library for areal interpolation and dasymetric mapping.","active":false,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py39-setuptools","clang-17","py39-installer","py39-build","py39-wheel","py39-setuptools_scm"]},{"type":"lib","ports":["python39"]},{"type":"run","ports":["py39-scipy","py39-statsmodels","py39-tqdm","py39-rasterstats","py39-rasterio","py39-pandas","py39-numpy","py39-libpysal","py39-joblib","py39-geopandas"]}],"depends_on":[{"type":"lib","ports":["py39-pysal"]}]},{"name":"py-tobler","portdir":"python/py-tobler","version":"0.14.0","license":"BSD","platforms":"any","epoch":0,"replaced_by":null,"homepage":"https://pysal.org/tobler/","description":"Spatial interpolation, Dasymetric Mapping, & Change of Support (tobler)","long_description":"The PySAL tobler is a library for areal interpolation and dasymetric mapping.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-18"]},{"type":"lib","ports":["py314-tobler"]}],"depends_on":[]},{"name":"py311-spvcm","portdir":"python/py-spvcm","version":"0.3.0","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/pysal/spvcm","description":"Multilevel spatially-correlated variance components models (spvcm)","long_description":"The PySAL spvcm is a package to estimate spatially-correlated variance components models/varying intercept models. In addition to a general toolkit to conduct Gibbs sampling in Python, the package also provides an interface to PyMC3 and CODA. For a complete overview, consult the walkthrough.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py311-build","clang-18","py311-wheel","py311-setuptools","py311-installer"]},{"type":"lib","ports":["python311"]},{"type":"run","ports":["py311-spreg","py311-libpysal","py311-seaborn","py311-scipy","py311-pandas","py311-numpy"]}],"depends_on":[{"type":"lib","ports":["py311-pysal"]}]},{"name":"py310-spvcm","portdir":"python/py-spvcm","version":"0.3.0","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/pysal/spvcm","description":"Multilevel spatially-correlated variance components models (spvcm)","long_description":"The PySAL spvcm is a package to estimate spatially-correlated variance components models/varying intercept models. In addition to a general toolkit to conduct Gibbs sampling in Python, the package also provides an interface to PyMC3 and CODA. For a complete overview, consult the walkthrough.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py310-setuptools","clang-18","py310-installer","py310-build","py310-wheel"]},{"type":"lib","ports":["python310"]},{"type":"run","ports":["py310-spreg","py310-libpysal","py310-seaborn","py310-pandas","py310-scipy","py310-numpy"]}],"depends_on":[{"type":"lib","ports":["py310-pysal"]}]},{"name":"py39-spvcm","portdir":"python/py-spvcm","version":"0.3.0","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/pysal/spvcm","description":"Multilevel spatially-correlated variance components models (spvcm)","long_description":"The PySAL spvcm is a package to estimate spatially-correlated variance components models/varying intercept models. In addition to a general toolkit to conduct Gibbs sampling in Python, the package also provides an interface to PyMC3 and CODA. For a complete overview, consult the walkthrough.","active":false,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py39-setuptools","clang-17","py39-installer","py39-build","py39-wheel"]},{"type":"lib","ports":["python39"]},{"type":"run","ports":["py39-spreg","py39-seaborn","py39-scipy","py39-pandas","py39-numpy","py39-libpysal"]}],"depends_on":[{"type":"lib","ports":["py39-pysal"]}]},{"name":"py-spvcm","portdir":"python/py-spvcm","version":"0.3.0","license":"BSD","platforms":"any","epoch":0,"replaced_by":null,"homepage":"https://github.com/pysal/spvcm","description":"Multilevel spatially-correlated variance components models (spvcm)","long_description":"The PySAL spvcm is a package to estimate spatially-correlated variance components models/varying intercept models. In addition to a general toolkit to conduct Gibbs sampling in Python, the package also provides an interface to PyMC3 and CODA. For a complete overview, consult the walkthrough.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-18"]},{"type":"lib","ports":["py313-spvcm"]}],"depends_on":[]},{"name":"py311-spreg","portdir":"python/py-spreg","version":"1.9.0","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://pysal.org/spreg/","description":"PySAL Spatial Econometrics Package (spreg)","long_description":"PySAL Spatial Econometrics Package (spreg), short for “spatial regression”, is a Python package to estimate simultaneous autoregressive spatial regression models. These models are useful when modeling processes where observations interact with one another.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-18","py311-build","py311-installer","py311-setuptools","py311-wheel","py311-setuptools_scm"]},{"type":"lib","ports":["python311"]},{"type":"run","ports":["py311-numpy","py311-pandas","py311-scikit-learn","py311-scipy","py311-libpysal"]}],"depends_on":[{"type":"lib","ports":["py311-pysal"]},{"type":"run","ports":["py311-mgwr","py311-spglm","py311-spint","py311-splot","py311-spvcm"]}]},{"name":"py310-spreg","portdir":"python/py-spreg","version":"1.9.0","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://pysal.org/spreg/","description":"PySAL Spatial Econometrics Package (spreg)","long_description":"PySAL Spatial Econometrics Package (spreg), short for “spatial regression”, is a Python package to estimate simultaneous autoregressive spatial regression models. These models are useful when modeling processes where observations interact with one another.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-18","py310-setuptools","py310-setuptools_scm","py310-wheel","py310-build","py310-installer"]},{"type":"lib","ports":["python310"]},{"type":"run","ports":["py310-numpy","py310-scipy","py310-pandas","py310-scikit-learn","py310-libpysal"]}],"depends_on":[{"type":"lib","ports":["py310-pysal"]},{"type":"run","ports":["py310-mgwr","py310-spglm","py310-spint","py310-splot","py310-spvcm"]}]},{"name":"py39-spreg","portdir":"python/py-spreg","version":"1.8.4","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://pysal.org/spreg/","description":"PySAL Spatial Econometrics Package (spreg)","long_description":"PySAL Spatial Econometrics Package (spreg), short for “spatial regression”, is a Python package to estimate simultaneous autoregressive spatial regression models. These models are useful when modeling processes where observations interact with one another.","active":false,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-18","py39-setuptools","py39-setuptools_scm","py39-wheel","py39-build","py39-installer"]},{"type":"lib","ports":["python39"]},{"type":"run","ports":["py39-libpysal","py39-numpy","py39-pandas","py39-scikit-learn","py39-scipy"]}],"depends_on":[{"type":"lib","ports":["py39-pysal"]},{"type":"run","ports":["py39-mgwr","py39-spglm","py39-spint","py39-spvcm","py39-splot"]}]},{"name":"py-spreg","portdir":"python/py-spreg","version":"1.9.0","license":"BSD","platforms":"any","epoch":0,"replaced_by":null,"homepage":"https://pysal.org/spreg/","description":"PySAL Spatial Econometrics Package (spreg)","long_description":"PySAL Spatial Econometrics Package (spreg), short for “spatial regression”, is a Python package to estimate simultaneous autoregressive spatial regression models. These models are useful when modeling processes where observations interact with one another.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["clang-18"]},{"type":"lib","ports":["py314-spreg"]}],"depends_on":[]},{"name":"py311-spopt","portdir":"python/py-spopt","version":"0.7.0","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/pysal/spopt","description":"Spatial Optimization (spopt)","long_description":"Spatial Optimization (spopt) is an open-source Python library for solving optimization problems with spatial data. Originating from the region module in PySAL (Python Spatial Analysis Library), it is under active development for the inclusion of newly proposed models and methods for regionalization, facility location, and transportation-oriented solutions.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py311-build","clang-18","py311-setuptools_scm","py311-wheel","py311-setuptools","py311-installer"]},{"type":"lib","ports":["python311"]},{"type":"run","ports":["py311-mapclassify","py311-shapely","py311-geopandas","py311-pulp","py311-libpysal","py311-pointpats","py311-spaghetti","py311-tqdm","py311-scipy","py311-scikit-learn","py311-pandas","py311-numpy","py311-networkx"]},{"type":"test","ports":["py311-pytest","py311-coverage","py311-pytest-cov","py311-pytest-xdist","py311-matplotlib","py311-mapclassify","py311-codecov","py311-folium"]}],"depends_on":[{"type":"lib","ports":["py311-pysal"]}]},{"name":"py310-spopt","portdir":"python/py-spopt","version":"0.6.1","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/pysal/spopt","description":"Spatial Optimization (spopt)","long_description":"Spatial Optimization (spopt) is an open-source Python library for solving optimization problems with spatial data. Originating from the region module in PySAL (Python Spatial Analysis Library), it is under active development for the inclusion of newly proposed models and methods for regionalization, facility location, and transportation-oriented solutions.","active":true,"categories":["python","gis"],"maintainers":[],"variants":[],"dependencies":[{"type":"build","ports":["py310-setuptools","clang-18","py310-installer","py310-build","py310-wheel","py310-setuptools_scm"]},{"type":"lib","ports":["python310"]},{"type":"run","ports":["py310-networkx","py310-geopandas","py310-mapclassify","py310-pulp","py310-libpysal","py310-pointpats","py310-spaghetti","py310-scikit-learn","py310-tqdm","py310-shapely","py310-pandas","py310-scipy","py310-numpy"]},{"type":"test","ports":["py310-pytest","py310-pytest-cov","py310-coverage","py310-matplotlib","py310-pytest-xdist","py310-codecov","py310-mapclassify","py310-folium"]}],"depends_on":[{"type":"lib","ports":["py310-pysal"]}]}]}