Presubmission inquiry

[Nischalcs50]

Submitting Author: Name (@Nischalcs50)
Package Name: nsEVDx: A python Library for modelling non-stationary extreme value distributions
One-Line Description of Package: This package implements both bayesian sampling techniques and frequentist methods to estimate the parameters of non-stationary extreme value distributions
Repository Link (if existing): https://github.com/Nischalcs50/nsEVDx,
https://pypi.org/project/nsEVDx/
EiC: TBD

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• I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

Description

• Include a brief paragraph describing what your package does:

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Scope

• Please indicate which category or categories this package falls under:

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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• Explain how and why the package falls under these categories (briefly, 1-2 sentences). For community partnerships, check also their specific guidelines as documented in the links above. Please note any areas you are unsure of:
  The package processes and models hydroclimatic extremes using advanced statistical methods, falling under "data processing/munging." It also serves as a scientific wrapper by integrating Bayesian sampling and frequentist estimation methods for EVDs. Its focus on rainfall/flood extremes places it within geospatial and hydrological domains. It could also be used on other extreme values such as in finance.

• Who is the target audience and what are the scientific applications of this package?
  The target audience includes hydrologists, climate scientists, and civil/environmental engineers working on flood frequency analysis, design storm estimation, and climate impact studies. It is also relevant to applied statisticians studying rare event distributions in non-stationary contexts.

• Are there other Python packages that accomplish similar things? If so, how does yours differ?
  Some functionality overlaps with libraries like scipy.stats, lmoments3, or extRemes (in R). However, these packages generally assume stationarity or lack Bayesian implementations. nsEVDx differs by providing an integrated framework for non-stationary extreme value modelling, combining both Bayesian and frequentist approaches, and offering utilities tailored to hydroclimatic applications.

• Any other questions or issues we should be aware of:
  At this stage, I welcome guidance on best practices for testing, packaging, and documenting scientific software to meet pyOpenSci standards.

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