Create r-h2o package for conda

[tomasfryda]

https://github.com/conda-forge/r-h2o-feedstock https://docs.conda.io/projects/conda-build/en/stable/user-guide/tutorials/build-r-pkgs.html

And add it to jenkins release.

meta.yaml:

{% set major_version = '3.46.0' %}
{% set minor_version = '1' %}
{% set version = major_version + '.' + minor_version %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

package:
  name: r-h2o
  version: {{ version|replace("-", "_") }}

source:
  url:
    - https://h2o-release.s3.amazonaws.com/h2o/rel-{{ major_version }}/{{ minor_version }}/Rcran/h2o_{{ major_version }}.{{ minor_version }}.tar.gz


build:
  merge_build_host: true  # [win]
  number: 0
  noarch: generic
  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - {{ posix }}zip               # [win]
    - cross-r-base {{ r_base }}    # [build_platform != target_platform]
  host:
    - r-base
    - r-rcurl
    - r-jsonlite
    - openjdk
  run:
    - r-base
    - r-rcurl
    - r-jsonlite
    - openjdk

test:
  commands:
    - $R -e "library('h2o')"           # [not win]
    - "\"%R%\" -e \"library('h2o')\""  # [win]

about:
  home: https://github.com/h2oai/h2o-3
  license: Apache-2.0
  summary: R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep
    Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), Cox Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).
  license_family: APACHE