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0.2.0 correlated_ts_ci-0.2.0-py3-none-any.whl

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Project: correlated-ts-ci
Version: 0.2.0
Filename: correlated_ts_ci-0.2.0-py3-none-any.whl
Download: [link]
Size: 20999
MD5: ed7e5c6793b01918d233dac8ddbf1d65
SHA256: 5a3da328bd2f556ef10cec972a795be0d4a3a03ad8050cb209c3b577b77db1bb
Uploaded: 2022-08-26 16:48:42 +0000

dist-info

METADATA · WHEEL · RECORD

METADATA

Metadata-Version: 2.1
Name: correlated-ts-ci
Version: 0.2.0
Summary: Estimate confidence intervals in means of correlated time series with a small number of effective samples (like molecular dynamics simulations). If your time series is long enough that the standard error levels off completely as a function of block length, then this method is overkill and simply using a block bootstrap sampling with a sufficiently large block length is probably sufficient.
Author: Brian Novak
Author-Email: bnovak1[at]users.noreply.github.com
License: GPL-3.0-or-later
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.9,<4.0
Requires-Dist: arch (<5.0.0,>=4.16.1)
Requires-Dist: joblib (>=0.17.0)
Requires-Dist: lmfit (<2.0.0,>=1.0.2)
Requires-Dist: numpy (<2.0,>=1.2)
Description-Content-Type: text/markdown
[Description omitted; length: 4510 characters]

WHEEL

Wheel-Version: 1.0
Generator: poetry 1.0.7
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

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