imputeTS - Time Series Missing Value Imputation
Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: 'Mean', 'LOCF', 'Interpolation', 'Moving Average', 'Seasonal Decomposition', 'Kalman Smoothing on Structural Time Series models', 'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) <doi:10.32614/RJ-2017-009>.
Last updated 2 years ago
data-visualizationimputationimputation-algorithmimputetsmissing-datatime-seriescpp
12.13 score 161 stars 27 dependents 1.9k scripts 13k downloadsridge - Ridge Regression with Automatic Selection of the Penalty Parameter
Linear and logistic ridge regression functions. Additionally includes special functions for genome-wide single-nucleotide polymorphism (SNP) data. More details can be found in <doi: 10.1002/gepi.21750> and <doi: 10.1186/1471-2105-12-372>.
Last updated 3 years ago
regressionridge-regressiongsl
7.13 score 18 stars 2 dependents 124 scripts 920 downloadsimputeR - A General Multivariate Imputation Framework
Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.
Last updated 4 years ago
missing-data
4.94 score 16 stars 54 scripts 352 downloads