The mouse and trackpad utility for Mac.

Probabilistic modelling and uncertainty quantification library

BioSTEAM's Premier Repository for Biorefinery Models and Results

The Compliance Utility is a powerful tool that helps troubleshoot and diagnose sensitivity labels, policies, settings and more. Whether you need to fix issues or reset configurations, this tool gives you everything you need.

Modular Python framework for topology optimization

A Review of Sensitivity Methods for Differential Equations

Fast nonlinear FEA tailored for topology optimization

VARS-TOOL sensitivity analysis package in Python

Extend scipy.integrate with various methods for solve_ivp

Tiny, zero-dependencies, package which tries to mask sensitive data in arbitrary collections (map, set), errors, objects and strings.

Report various statistics stemming from a confusion matrix in a tidy fashion. 🎯

Testing the consistency of binary classification performance scores reported in papers

Toolbox for analysis of model's quality and model's description. For further details see

Unfortunately, FMUs (fmi-standard.org) are not differentiable by design. To enable their full potential inside Julia, FMISensitivity.jl makes FMUs fully differentiable w.r.t. states and derivatives | inputs, outputs and other observable variables | parameters | event indicators | explicit time | state change sensitivity by event

katmer is a powerful library for optimizing the design of optical thin films using automatic differentiation via JAX and Equinox, enabling efficient and accurate inverse design solutions.

A tool for electromagnetic modelling of the head and sensitivity analysis.

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Tools for sensitivity analysis for weighted estimators

Filter and plot sensitivity data from CHEMKIN sensitivity spreadsheets

An R package for applying global sensitivity analysis in physiologically based kinetic modeling

Tool to analyze a confusion matrix from performance of a classification model.

Simple site with some sensitivity utilities

Comparison of Binary Diagnostic Tests in a Paired Study Design

PyTorch and Tf-Keras implementations of an epidemiology metric from Kaivanto (2008), suitable for imbalanced data

an anticheat safe (i think) sensitivity randomizer

Solve complex real-life problems with the simplicity of Keras

[AIIM] Recall & Precision results of the UTA7 statistical analysis.

A unique sensitive form for web apps, much more responsive on PC view

Discovering if there exists a relationship🔗 between specific demographic characteristics in the population that would explain the tolerance in the population to report noise nuisance to the "311" department 👮🏻‍♂️

Building a nearest-neighbor classifier to predict online shopping purchase completions based on user browsing behavior. The project uses a dataset of 12,000 sessions, analyzing features like pages visited, session duration, and bounce rates