{% set name = "cmdstan" %} {% set version = "2.37.0" %} {% set posix = 'm2-' if win else '' %} package: name: {{ name|lower }} version: {{ version }} source: url: https://github.com/stan-dev/{{ name }}/releases/download/v{{ version }}/{{ name }}-{{ version }}.tar.gz # [x86 or arm64] sha256: 635e2e1cf9c4774c9019001325354d3610bae2adc4ed5af2fed87872dfc671fc # [x86 or arm64] url: https://github.com/stan-dev/{{ name }}/releases/download/v{{ version }}/{{ name }}-{{ version }}-linux-arm64.tar.gz # [aarch64] sha256: 7e979a4f088aab64b19af271bd0b76a4ad81b6cccd61f805154570c5a2624275 # [aarch64] url: https://github.com/stan-dev/{{ name }}/releases/download/v{{ version }}/{{ name }}-{{ version }}-linux-ppc64el.tar.gz # [ppc64le] sha256: 29f3f62673957bd5e001c9df9afd209eb1c4f7a2322abba89f45397547f34555 # [ppc64le] build: number: 1 binary_relocation: false requirements: build: - {{ compiler('cxx') }} # [not win] - {{ stdlib("c") }} # [not win] - {{ compiler('m2w64_cxx') }} # [win] - {{ stdlib("m2w64_c") }} # [win] - {{ posix }}make - {{ posix }}filesystem # [win] - {{ posix }}sed # [win] - {{ posix }}coreutils # [win] host: - tbb-devel # [not win] run: - {{ compiler('cxx') }} # [not win] - {{ stdlib("c") }} # [not win] - {{ compiler('m2w64_cxx') }} # [win] - {{ stdlib("m2w64_c") }} # [win] - {{ posix }}make - tbb-devel # [not win] - {{ posix }}filesystem # [win] - {{ posix }}sed # [win] - {{ posix }}coreutils # [win] - gxx_{{ target_platform }} >={{ cxx_compiler_version }} # [linux] run_constrained: - tbb >=2021 # [win] test: downstreams: - cmdstanpy - r-cmdstanr # [not win] about: home: https://mc-stan.org/users/interfaces/cmdstan.html license: BSD-3-Clause license_family: BSD license_file: LICENSE summary: CmdStan, the command line interface to Stan description: | CmdStan is the command line interface to Stan, a package providing * full Bayesian inference using the No-U-Turn sampler (NUTS), a variant of Hamiltonian Monte Carlo (HMC), * penalized maximum likelihood estimation (MLE) using optimization, either Newton or quasi-Newton algorithms BFGS and L-BFGS, * approximate Bayesian inference using automatic differentiation variational inference (ADVI), * a full first- and higher-order automatic differentiation library based on C++ template overloads, and * a supporting fully-templated matrix, linear algebra, and probability special function library. doc_url: https://mc-stan.org/docs/cmdstan-guide/index.html dev_url: https://github.com/stan-dev/cmdstan extra: recipe-maintainers: - maresb - WardBrian