stats, grDevices, ggplot2 (>= 3.3.0), ggtext, stinepack,
forecast, magrittr, Rcpp

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>.
Steffen Moritz [aut, cre, cph]
(<https://orcid.org/0000-0002-0085-1804>),
Sebastian Gatscha [aut],
Earo Wang [ctb] (<https://orcid.org/0000-0001-6448-5260>),
Ron Hause [ctb] (<https://orcid.org/0000-0002-5229-7366>)
c(
person("Steffen", "Moritz", email="steffen.moritz10@gmail.com", role=c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-0085-1804")),
person("Sebastian", "Gatscha", email="sebastian_gatscha@gmx.at", role="aut"),
person("Earo", "Wang", email = "earo.wang@gmail.com", role = c("ctb"), comment = c(ORCID = "0000-0001-6448-5260")),
person("Ron", "Hause", email = "ronaldhause@gmail.com", role = c("ctb"), comment = c(ORCID = "0000-0002-5229-7366"))
)
stats, grDevices, ggplot2 (>= 3.3.0), ggtext, stinepack,
forecast, magrittr, methods, Rcpp
testthat, R.rsp, knitr, zoo, timeSeries, tis, xts, tibble,
tsibble, rmarkdown, covr

Test Bench for Missing Data Imputing Models/Methods Comparison
Provides a Test bench for comparison of missing
data imputation models/methods. It compares imputing methods with reference
to RMSE, MAE or MAPE parameters. It allows to add new proposed methods to test
bench and to compare with other methods. The function 'append_method()' allows
to add multiple numbers of methods to the existing methods available in test bench.
ggplot2, imputeTS, reshape2, methods, stats

Test Bench for the Comparison of Imputation Methods
Provides a test bench for the comparison of missing data imputation
methods in univariate time series. Imputation methods are compared using RMSE,
MAE or MAPE error metrics. Proposed imputation methods and alternative error
metrics can be used.
dplyr, forecast, ggplot2, imputeTS, reshape2, stats, tidyr,
zoo
c(
person(given = "Neeraj", family = "Bokde",
role = c("aut"),
email = "neerajdhanraj@gmail.com"
),
person(given = "Marcus W.", family = "Beck",
role = c("cre", "aut"),
email = "mbafs2012@gmail.com"
)
)
Neeraj Bokde [aut],
Marcus W. Beck [cre, aut]