Actuarial models in Python
Project description
lifelib is a collection of actuarial projection models. lifelib models are built as modelx models, ready to be used out of the box with sample formulas and input files, and they are fully customizable by users.
Feature highlights
Formulas and their calculated values paired as Cells, just like spreadsheet cells
Relevant cells grouped together as a Space, just like a spreadsheet
Spaces in other spaces (subspaces), forming trees of spaces
Models composed of spaces
Space inheritance
Parametrized dynamic subspaces created automatically
Saving to / loading from files
Conversion to Pandas objects
Reading data from Excel files
Cells graph to track cells interdependency
Why lifelib?
Better model integrity and extensibility
For readable formula expressions
For eliminating spreadsheet errors
For better version control/model governance
What for?
Pricing / Profit testing
Model validation / testing
Prototyping for production models
As corporate models
For simulations
As replacement for any spreadsheet models
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file lifelib-0.6.0.tar.gz.
File metadata
- Download URL: lifelib-0.6.0.tar.gz
- Upload date:
- Size: 7.0 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.11.3 pkginfo/1.8.2 requests/2.28.1 requests-toolbelt/0.9.1 tqdm/4.64.0 CPython/3.9.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 81f37afb00b606a5b614fceace5027c1d7fffae0b95328df42236485de472d6e |
|
| MD5 | 3f4314369ed508042f5506b1f6fb1e67 |
|
| BLAKE2b-256 | 100832c1bf355bb63f6a393712aa63afc64cd55db2a9f6ddb1fda91b7bf88c41 |
File details
Details for the file lifelib-0.6.0-py3-none-any.whl.
File metadata
- Download URL: lifelib-0.6.0-py3-none-any.whl
- Upload date:
- Size: 7.2 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.11.3 pkginfo/1.8.2 requests/2.28.1 requests-toolbelt/0.9.1 tqdm/4.64.0 CPython/3.9.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d16f00a778b7a491993d9863e60e3f0a001e81da57c7718ba5371e743bf23bf2 |
|
| MD5 | 589974ff5fc30cf289ee9c9441d105eb |
|
| BLAKE2b-256 | 82c930bcfcfdc5e9e42e4ee37d04b617e270f5819f5b0be4f78c9c936266e0f6 |
