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ludvigolsen

cvms:Cross-Validation for Model Selection

Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

Maintained by Ludvig Renbo Olsen. Last updated 27 days ago.

39 stars 10.31 score 492 scripts 5 dependents

dewittpe

qwraps2:Quick Wraps 2

A collection of (wrapper) functions the creator found useful for quickly placing data summaries and formatted regression results into '.Rnw' or '.Rmd' files. Functions for generating commonly used graphics, such as receiver operating curves or Bland-Altman plots, are also provided by 'qwraps2'. 'qwraps2' is a updated version of a package 'qwraps'. The original version 'qwraps' was never submitted to CRAN but can be found at <https://github.com/dewittpe/qwraps/>. The implementation and limited scope of the functions within 'qwraps2' <https://github.com/dewittpe/qwraps2/> is fundamentally different from 'qwraps'.

Maintained by Peter DeWitt. Last updated 6 months ago.

cpp

37 stars 9.80 score 448 scripts

adriancorrendo

metrica:Prediction Performance Metrics

A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.

Maintained by Adrian A. Correndo. Last updated 9 months ago.

77 stars 7.88 score 49 scripts

modeloriented

fairmodels:Flexible Tool for Bias Detection, Visualization, and Mitigation

Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) <arXiv:2104.00507>.

Maintained by Jakub Wiśniewski. Last updated 2 months ago.

explain-classifiersexplainable-mlfairnessfairness-comparisonfairness-mlmodel-evaluation

87 stars 7.73 score 51 scripts 1 dependents

signaturescience

skater:Utilities for SNP-Based Kinship Analysis

Utilities for single nucleotide polymorphism (SNP) based kinship analysis testing and evaluation. The 'skater' package contains functions for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing identity by descent (IBD) segment data. Package functions and methods are described in Turner et al. (2021) "skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation" <doi:10.1101/2021.07.21.453083>.

Maintained by Stephen Turner. Last updated 2 years ago.

9 stars 5.26 score 7 scripts

profpetrie

regclass:Tools for an Introductory Class in Regression and Modeling

Contains basic tools for visualizing, interpreting, and building regression models. It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9 <https://titles.cognella.com/introduction-to-regression-and-modeling-with-r-9781631892509>.

Maintained by Adam Petrie. Last updated 5 years ago.

3.74 score 301 scripts 1 dependents

gertjanssenswillen

processpredictR:Process Prediction

Means to predict process flow, such as process outcome, next activity, next time, remaining time, and remaining trace. Off-the-shelf predictive models based on the concept of Transformers are provided, as well as multiple ways to customize the models. This package is partly based on work described in Zaharah A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). "ProcessTransformer: Predictive Business Process Monitoring with Transformer Network" <arXiv:2104.00721>.

Maintained by Gert Janssenswillen. Last updated 2 years ago.

3.15 score 28 scripts

certe-medical-epidemiology

certestats:A Certe R Package for Statistical Modelling

A Certe R Package for early-warning, applying statistical modelling (such as creating machine learning models), QC rules and distribution analysis. This package is part of the 'certedata' universe.

Maintained by Matthijs S. Berends. Last updated 5 months ago.

statistics

3.02 score 1 scripts 1 dependents

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