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Uncertainty treatment library

A Review of Sensitivity Methods for Differential Equations

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

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

The mouse and trackpad utility for Mac.

Morris sensitivity method module

DiaMetrics_DE is the German version of Diametrics: a web-based educational resource for exploring important concepts regarding binary classification (and its evaluation), which is important in many different fields such as psychodiagnostics (e.g. in determining cut-off values for tests), machine learning or medical testing.

Nonparametric comparison of convolutional neural networks and transformers to classify COVID-19

Extend scipy.integrate with various methods for solve_ivp

Comparison of Binary Diagnostic Tests in a Paired Study Design

Testing the consistency of binary classification performance scores reported in papers

DiaMetrics is a web-based educational resource for exploring important concepts regarding binary classification (and its evaluation), which is important in many different fields such as psychodiagnostics (e.g. in determining cut-off values for tests), machine learning or medical testing.

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

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

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

Estimate the integral and/or differential flux-sensitivity of your instrument

Functions for Medical Decision Making for ClinicoPath jamovi Module

Tools for sensitivity analysis for weighted estimators

Evaluation of the performance of classification models can be facilitated through a combination of calculating certain types of performance metrics and generating model performance evaluation graphics. The purpose of this exercise is to calculate a suite of classification model performance metrics via Python code functions.