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>.
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data-visualizationimputationimputation-algorithmimputetsmissing-datatime-seriescpp
12.42 score 173 stars 25 dependents 2.7k scripts 17k 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>.
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regressionridge-regressiongsl
7.11 score 19 stars 1 dependents 146 scripts 1.6k 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.
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missing-data
4.95 score 16 stars 56 scripts 545 downloadskssa - Known Sub-Sequence Algorithm
Implements the Known Sub-Sequence Algorithm <doi:10.1016/j.aaf.2021.12.013>, which helps to automatically identify and validate the best method for missing data imputation in a time series. Supports the comparison of multiple state-of-the-art algorithms.
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3.78 score 3 stars 3 scripts 139 downloads