mde - Missing Data Explorer
Correct identification and handling of missing data is one of the most important steps in any analysis. To aid this process, 'mde' provides a very easy to use yet robust framework to quickly get an idea of where the missing data lies and therefore find the most appropriate action to take. Graham WJ (2009) <doi:10.1146/annurev.psych.58.110405.085530>.
Last updated 3 years ago
data-analysisdata-cleaningdata-explorationdata-sciencedatacleanerdatacleaningexploratory-data-analysismissingmissing-datamissing-value-treatmentmissing-valuesmissingnessomitrecodereplacestatistics
5.61 score 4 stars 34 scripts 379 downloadsmanymodelr - Build and Tune Several Models
Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) <arXiv:1405.6974>.
Last updated 3 years ago
analysis-of-varianceanovacorrelationcorrelation-coefficientgeneralized-linear-modelsgradient-boosting-decision-treesknn-classificationlinear-modelslinear-regressionmachine-learningmissing-valuesmodelsr-programmingrandom-forest-algorithmregression-models
5.30 score 2 stars 50 scripts 354 downloads