R-universe search: exports:ppv

Showing 11 of total 11 results (show query)

tidymodels

yardstick:Tidy Characterizations of Model Performance

Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).

Maintained by Emil Hvitfeldt. Last updated 18 days ago.

387 stars 15.47 score 2.2k scripts 60 dependents

jackstat

ModelMetrics:Rapid Calculation of Model Metrics

Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.

Maintained by Tyler Hunt. Last updated 4 years ago.

auc logloss machine-learning metrics model-evaluation model-metrics cpp

29 stars 11.83 score 1.3k scripts 306 dependents

thie1e

cutpointr:Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks

Estimate cutpoints that optimize a specified metric in binary classification tasks and validate performance using bootstrapping. Some methods for more robust cutpoint estimation are supported, e.g. a parametric method assuming normal distributions, bootstrapped cutpoints, and smoothing of the metric values per cutpoint using Generalized Additive Models. Various plotting functions are included. For an overview of the package see Thiele and Hirschfeld (2021) <doi:10.18637/jss.v098.i11>.

Maintained by Christian Thiele. Last updated 4 months ago.

bootstrapping cutpoint-optimization roc-curve cpp

88 stars 10.44 score 322 scripts 1 dependents

brian-j-smith

MachineShop:Machine Learning Models and Tools

Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.

Maintained by Brian J Smith. Last updated 7 months ago.

classification-models machine-learning predictive-modeling regression-models survival-models

62 stars 7.95 score 121 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

serkor1

SLmetrics:Machine Learning Performance Evaluation on Steroids

Performance evaluation metrics for supervised and unsupervised machine learning, statistical learning and artificial intelligence applications. Core computations are implemented in 'C++' for scalability and efficiency.

Maintained by Serkan Korkmaz. Last updated 2 days ago.

cpp data-analysis data-science eigen3 machine-learning performance-metrics rcpp rcppeigen statistics supervised-learning cpp openmp

22 stars 6.56 score

tingtingzhan

DanielBiostatistics10th:Functions for Wayne W. Daniel's Biostatistics, Tenth Edition

Functions to accompany Wayne W. Daniel's Biostatistics: A Foundation for Analysis in the Health Sciences, Tenth Edition.

Maintained by Tingting Zhan. Last updated 2 days ago.

5.81 score 27 scripts

davidbuch

gretel:Generalized Path Analysis for Social Networks

The social network literature features numerous methods for assigning value to paths as a function of their ties. 'gretel' systemizes these approaches, casting them as instances of a generalized path value function indexed by a penalty parameter. The package also calculates probabilistic path value and identifies optimal paths in either value framework. Finally, proximity matrices can be generated in these frameworks that capture high-order connections overlooked in primitive adjacency sociomatrices. Novel methods are described in Buch (2019) <https://davidbuch.github.io/analyzing-networks-with-gretel.html>. More traditional methods are also implemented, as described in Yang, Knoke (2001) <doi:10.1016/S0378-8733(01)00043-0>.

Maintained by David Buch. Last updated 5 years ago.

cpp

1 stars 3.70 score 6 scripts

r-forge

softclassval:Soft Classification Performance Measures

An extension of sensitivity, specificity, positive and negative predictive value to continuous predicted and reference memberships in [0, 1].

Maintained by C. Beleites. Last updated 9 years ago.

2.00 score 8 scripts

mikemalekahmadi

ClinSigMeasures:Clinical Significance Measures

Provides measures of effect sizes for summarized continuous variables as well as diagnostic accuracy statistics for 2x2 table data. Includes functions for Cohen's d, robust effect size, Cohen's q, partial eta-squared, coefficient of variation, odds ratio, likelihood ratios, sensitivity, specificity, positive and negative predictive values, Youden index, number needed to treat, number needed to diagnose, and predictive summary index.

Maintained by Mike Malek-Ahmadi. Last updated 9 months ago.

1.30 score 1 scripts

ram6ler

trotter:Pseudo-Vectors Containing All Permutations, Combinations and Subsets of Objects Taken from a Vector.

Class definitions and constructors for pseudo-vectors containing all permutations, combinations and subsets of objects taken from a vector. Simplifies working with structures commonly encountered in combinatorics.

Maintained by Richard Ambler. Last updated 11 years ago.

1.00 score 6 scripts