PySaem

Nonlinear mixed-effects models written as ordinary differential equations in plain Python, estimated by the stochastic approximation EM algorithm (SAEM).

The package is in development. This version holds the name only and contains no code; the first release will accompany its preprint.

Metadata

Release files for pysaem 0.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pysaem 0.0.1
File	Size	Uploaded	Provenance
pysaem-0.0.1.tar.gz	1.4 kB	Oct 25, 2026	Without provenance	Details

Built distribution (wheel)

Table of built distributions (wheels) for pysaem 0.0.1
File	Interpreter	ABI	Platform	Reset
pysaem-0.0.1-py3-none-any.whl 1.5 kB Oct 25, 2026 Without provenance	Python 3	none	any	Details

Total release size: 2.9 kB

Release files / pysaem-0.0.1.tar.gz

Download URL	pysaem-0.0.1.tar.gz
Size	1.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`49265fd7bd2ba0e73c39ab1e26571c781d33ab3a3f47374f081f003df44dadb2`
BLAKE2b-256 checksum How to use checksums	`2168a72b7193ef8c805d291d77fd6b2d9216363eb8be5c7572ccc6686cc16c52`
Upload date	Oct 25, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.11`

Release files / pysaem-0.0.1-py3-none-any.whl

Download URL	pysaem-0.0.1-py3-none-any.whl
Size	1.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`35715220ee795348ecc79a10eeb4f13a9ff183ea1b00dee2c1ee96556b65b8ef`
BLAKE2b-256 checksum How to use checksums	`dbb8eb17bc456afd686a02d911f8447003e64315dce4203250861f64a9391f07`
Upload date	Oct 25, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.11`

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This release

0.0.1 This release

Oct 25, 2026 2 release files No provenance

pysaem 0.0.1