Showing 16 of total 16 results (show query)
topepo
caret:Classification and Regression Training
Misc functions for training and plotting classification and regression models.
Maintained by Max Kuhn. Last updated 4 months ago.
1.6k stars 19.24 score 61k scripts 303 dependents
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.
aucloglossmachine-learningmetricsmodel-evaluationmodel-metricscpp
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.
bootstrappingcutpoint-optimizationroc-curvecpp
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-modelsmachine-learningpredictive-modelingregression-modelssurvival-models
62 stars 7.95 score 121 scripts
bioc
MLInterfaces:Uniform interfaces to R machine learning procedures for data in Bioconductor containers
This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers.
Maintained by Vincent Carey. Last updated 5 months ago.
7.63 score 79 scripts 6 dependents
egenn
rtemis:Machine Learning and Visualization
Advanced Machine Learning and Visualization. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and interactive graphics.
Maintained by E.D. Gennatas. Last updated 2 months ago.
data-sciencedata-visualizationmachine-learningmachine-learning-libraryvisualization
145 stars 7.09 score 50 scripts 2 dependents
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.
cppdata-analysisdata-scienceeigen3machine-learningperformance-metricsrcpprcppeigenstatisticssupervised-learningcppopenmp
22 stars 6.56 score
dppalomar
spectralGraphTopology:Learning Graphs from Data via Spectral Constraints
In the era of big data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become a prominent task in machine learning and has found applications in many fields such as finance, health care, and networks. 'spectralGraphTopology' is an open source, documented, and well-tested R package for learning graphs from data. It provides implementations of state of the art algorithms such as Combinatorial Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation based on Majorization-Minimization (GLE-MM), and Graph Estimation based on Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph learning has been widely employed for clustering, where specific algorithms are available in the literature. To this end, we provide an implementation of the Constrained Laplacian Rank (CLR) algorithm.
Maintained by Ze Vinicius. Last updated 3 years ago.
2 stars 5.91 score 135 scripts 1 dependents
ballings
AUC:Threshold Independent Performance Measures for Probabilistic Classifiers
Various functions to compute the area under the curve of selected measures: The area under the sensitivity curve (AUSEC), the area under the specificity curve (AUSPC), the area under the accuracy curve (AUACC), and the area under the receiver operating characteristic curve (AUROC). Support for visualization and partial areas is included.
Maintained by Michel Ballings. Last updated 3 years ago.
5.45 score 424 scripts 7 dependents
netfacs
NetFACS:Network Applications to Facial Communication Data
Functions to analyze and visualize communication data, based on network theory and resampling methods. Farine, D. R. (2017) <doi:10.1111/2041-210X.12772>; Carsey, T., & Harden, J. (2014) <doi:10.4135/9781483319605>. Primarily targeted at datasets of facial expressions coded with the Facial Action Coding System. Ekman, P., Friesen, W. V., & Hager, J. C. (2002). "Facial action coding system - investigator's guide" <https://www.paulekman.com/facial-action-coding-system/>.
Maintained by Alan V. Rincon. Last updated 11 months ago.
8 stars 5.08 score 5 scripts
annennenne
causalDisco:Tools for Causal Discovery on Observational Data
Various tools for inferring causal models from observational data. The package includes an implementation of the temporal Peter-Clark (TPC) algorithm. Petersen, Osler and Ekstrøm (2021) <doi:10.1093/aje/kwab087>. It also includes general tools for evaluating differences in adjacency matrices, which can be used for evaluating performance of causal discovery procedures.
Maintained by Anne Helby Petersen. Last updated 27 days ago.
19 stars 4.76 score 10 scripts
cran
PresenceAbsence:Presence-Absence Model Evaluation
Provides a set of functions useful when evaluating the results of presence-absence models. Package includes functions for calculating threshold dependent measures such as confusion matrices, pcc, sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It will calculate optimal threshold choice according to a choice of optimization criteria. It also includes functions to plot the threshold independent ROC curves along with the associated AUC (area under the curve).
Maintained by Elizabeth Freeman. Last updated 2 years ago.
1 stars 4.01 score 9 dependents
imares-group
glossa:User-Friendly 'shiny' App for Bayesian Species Distribution Models
A user-friendly 'shiny' application for Bayesian machine learning analysis of marine species distributions. GLOSSA (Global Species Spatiotemporal Analysis) uses Bayesian Additive Regression Trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) to model species distributions with intuitive workflows for data upload, processing, model fitting, and result visualization. It supports presence-absence and presence-only data (with pseudo-absence generation), spatial thinning, cross-validation, and scenario-based projections. GLOSSA is designed to facilitate ecological research by providing easy-to-use tools for analyzing and visualizing marine species distributions across different spatial and temporal scales.
Maintained by Jorge Mestre-Tomás. Last updated 4 months ago.
1 stars 3.00 score 5 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