Skip to main content

A Python package for Kabirian-based optinalysis (KBO) and other advanced estimations

Project description

Kabirian-Based Optinalysis Modesls

Kabirian-based optinalysis models (kbomodels) consist of a collection of mathematical and statistical techniques designed for analyzing datasets within the Kabirian-based optinalysis framework, along with other advanced methodologies. The estimation modules integrated within kbomodels include:

1a) kc_isoptinalysis : Performs Kabirian-based isomorphic optinalysis to calculate the Kabirian coefficient.

1b) doc_kc_isoptinalysis : Prints the documentation of the kc_isoptinalysis module.

2a) kc_autoptinalysis : Performs Kabirian-based automorphic optinalysis to calculate the Kabirian coefficient.

2b) doc_kc_autoptinalysis : Prints the documentation of the kc_autoptinalysis.

3a) pSimSymId : Translates the Kabirian coefficient of similarity, symmetry, or identity (kc) to probability of similarity, symmetry, or Sidentity (SimSymId).

3b) doc_pSimSymId : Prints the documentation of the pSimSymId.

4a) pDsimAsymUid : Translates probability of similarity, symmetry, or identity (pSimSymId) to probability of dissimilarity, asymmetry, or unidentity (pDsimAsymUid).

4b) doc_pDsimAsymUid : Prints the documentation of the pDsimAsymUid.

5a) kc_alt1 : Translates probability of similarity, symmetry, or identity (pSimSymId) to an ascending-alternative (A-alternative) Kabirian coefficient of similarity, symmetry, or identity (kc_alt1).

5b) doc_kc_alt1 : Prints the documentation of the kc_alt1.

6a) kc_alt2 : Translates probability of similarity, symmetry, or identity (pSimSymId) to a descending-alternative (D-alternative) Kabirian coefficient of similarity, symmetry, or identity (kc_alt2).

6b) doc_kc_alt2 : Prints the documentation of the kc_alt2.

7a) kc_alt : Translates Kabirian coefficient of similarity, symmetry, or identity (kc) to its inverse alternative Kabirian coefficient of similarity, symmetry, or identity (kc_alt).

7b) doc_kc_alt : Prints the documentation of the kc_alt.

8a) isomorphic_optinalysis : Performs Kabirian-based isomorphic optinalysis.

8b) doc_isomorphic_optinalysis : Prints the documentation of the isomorphic_optinalysis.

9a) automorphic_optinalysis : Performs Kabirian-based automorphic optinalysis.

9b) doc_automorphic_optinalysis : Prints the documentation of the automorphic_optinalysis.

10a) stat_SymAsymmetry : Estimates the statistical symmetry and asymmetry of a dataset using a customized, and optimized Kabirian-based automorphic optinalysis.

10b) doc_stat_SymAsymmetry : Prints the documentation of the stat_SymAsymmetry.

11a) bioseq_gpa : Performs pairwise sequence analysis of aligned biological sequences using a customized, and optimized Kabirian-based isomorphic optinalysis.

11b) doc_bioseq_gpa : Prints the documentation of the bioseq_gpa.

12a) stat_mirroring : Performs statistical dispersion estimations on a dataset using a parameterized, customized, and optimized Kabirian-based isomorphic optinalysis.

12b) doc_stat_mirroring : Prints the documentation of the stat_mirroring.

13a) famispacing : Estimates family spacing conformity and disconformity using a customized, and optimized Kabirian-based isomorphic optinalysis.

13b) doc_famispacing : Prints the documentation of the famispacing.

14a) smb_ordinalysis : Performs statistical mirroring-based ordinalysis (SM-based Ordinalysis), a methodology for assessing an individual's level of assessments on a defined ordinal scale by applying a customized and optimized statistical mirroring techniques.

14b) doc_smb_ordinalysis : Prints the documentation of the smb_ordinalysis.

15a) qualitative_exposuremetrics : Performs qualitative exposuremetrics analysis, a comprehensive and sensitive estimation framework for analyzing organismal resistance and susceptibility dynamics using qualitative variables.

15b) doc_qualitative_exposuremetrics : Prints the documentation of the qualitative_exposuremetrics.

Installation and Documentation

## Installation

# You can install this package via pip:

pip install kbomodels

## Print the documentation

# You can print the documentation for each module in the package by using the following command.

import kbomodels as kbo
# Examples:
kbo.doc_kc_autoptinalysis(),
kbo.doc_kc_isoptinalysis(), 
kbo.doc_bioseq_gpa(), 
kbo.doc_stat_mirroring(), 
kbo.doc_famispacing(), 
kbo.doc_smb_ordinalysis(),
kbo.doc_exposuremetrics(),
kbo.doc_qualitative_exposuremetrics(),
kbo.modules_list(), e.t.c.

# Note: The documentation includes the function definitions, input parameters, usage examples, and other relevant details for each module.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kbomodels-2025.1.0.tar.gz (25.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

kbomodels-2025.1.0-py3-none-any.whl (24.7 kB view details)

Uploaded Python 3

File details

Details for the file kbomodels-2025.1.0.tar.gz.

File metadata

  • Download URL: kbomodels-2025.1.0.tar.gz
  • Upload date:
  • Size: 25.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.6

File hashes

Hashes for kbomodels-2025.1.0.tar.gz
Algorithm Hash digest
SHA256 525684312206cc6bb7acc1c5dda635e72b9806114cc30674d2403f8cdd003303
MD5 c8890d9a4d9508ca264bba146a1b46e6
BLAKE2b-256 43e275bd278448f70b454b8ff11b9a2a406a39dc50843e0ca899362344b35183

See more details on using hashes here.

File details

Details for the file kbomodels-2025.1.0-py3-none-any.whl.

File metadata

  • Download URL: kbomodels-2025.1.0-py3-none-any.whl
  • Upload date:
  • Size: 24.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.6

File hashes

Hashes for kbomodels-2025.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 2a7847f4f17bfb89d9cc8cc4c02cd11f99e17d7c856668f1627ad8547e757839
MD5 3fd14e0480469d5b989c1f3fbed518fa
BLAKE2b-256 b0b0913362ac75f1de20e6fd2a17c987231f3e0f8e3e4cf83fe8ae27c802dee0

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page