R-universe search: topic:machinelearning

Showing 6 of total 6 results (show query)

bioc

SPONGE:Sparse Partial Correlations On Gene Expression

This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.

Maintained by Markus List. Last updated 5 months ago.

geneexpression transcription generegulation networkinference transcriptomics systemsbiology regression randomforest machinelearning

6.66 score 38 scripts 1 dependents

winvector

RcppDynProg:'Rcpp' Dynamic Programming

Dynamic Programming implemented in 'Rcpp'. Includes example partition and out of sample fitting applications. Also supplies additional custom coders for the 'vtreat' package.

Maintained by John Mount. Last updated 2 years ago.

datascience machinelearning cpp

15 stars 5.61 score 18 scripts

noramvillanueva

clustcurv:Determining Groups in Multiples Curves

A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.

Maintained by Nora M. Villanueva. Last updated 5 months ago.

clustering data-analytics machinelearning multiple-curves nonparametric-statistics number-of-clusters regression survival-analysis

3 stars 5.53 score 38 scripts

techtonique

learningmachine:Machine Learning with Explanations and Uncertainty Quantification

Regression-based Machine Learning with explanations and uncertainty quantification.

Maintained by T. Moudiki. Last updated 4 months ago.

conformal-prediction machine-learning machine-learning-algorithms machinelearning statistical-learning uncertainty-quantification cpp

5 stars 5.53 score 21 scripts

benjilu

forestError:A Unified Framework for Random Forest Prediction Error Estimation

Estimates the conditional error distributions of random forest predictions and common parameters of those distributions, including conditional misclassification rates, conditional mean squared prediction errors, conditional biases, and conditional quantiles, by out-of-bag weighting of out-of-bag prediction errors as proposed by Lu and Hardin (2021). This package is compatible with several existing packages that implement random forests in R.

Maintained by Benjamin Lu. Last updated 4 years ago.

inference intervals machine-learning machinelearning prediction random-forest randomforest statistics

26 stars 4.62 score 16 scripts

haghish

autoEnsemble:Automated Stacked Ensemble Classifier for Severe Class Imbalance

A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability.

Maintained by E. F. Haghish. Last updated 7 days ago.

ai algorithm automated-machine-learning automl automl-algorithms ensemble ensemble-learning h2o h2oai machine-learning machinelearning metalearning stack-ensemble stacked-ensembles stacking

5 stars 4.42 score 21 scripts