try someone elses recipe
-{% set name = "pygam" %}
-{% set version = "0.5.2" %}
-{% set sha256 = "a220ec76e8cabf69cdfad4d4404db4afb9768f6f86e92c69815d1ed97fb29124" %}
+{% set name = "kombine" %}
+{% set version = "0.8.3" %}
+{% set sha256 = "da7a9542600c81df4b2ede2772b730dacef61ea3bc19b495f4fa445ff2b92f75" %}
package:
name: {{ name }}
@@ -19,30 +19,33 @@ build:
requirements:
host:
- python
+ - setuptools
- pip
run:
- python
- numpy
- - scipy >= 0.17
- - progressbar2
- - future
+ - scipy
test:
+ requires:
+ - nose
imports:
- - pygam
+ - kombine
+ commands:
+ - nosetests kombine
about:
- home: https://github.com/dswah/pyGAM
+ home: https://github.com/bfarr/kombine
license: GPLv3
license_family: GPL
license_file: LICENSE
- summary: Generalized Additive Models in Python
+ summary: An embarrassingly parallel, kernel-density-based ensemble sampler
description: |
- pyGAM is a library for training generalized additive models in Python.
- GAMs are powerful and interpretable models, which makes it easy to fit
- complex data while still being able to understand the model.
- dev_url: https://github.com/dswah/pyGAM
+ kombine is an ensemble sampler that uses a clustered
+ kernel-density-estimate proposal density, allowing for
+ massive parallelization and efficient sampling.
extra:
recipe-maintainers:
- - dswah
+ - duncanmmacleod
+ - bfarr
