Title: Imputation of Financial Time Series with Missing Values and/or
univariate-based (so no asset correlation is used). In addition,
person("Junyan", "Liu", role =  "aut", email = "jliubl@connect.ust.hk")
Suggests: knitr, ggplot2, prettydoc, rmarkdown, R.rsp, testthat, xts

person("Rui", "Zhou", role =  "aut", email = "rui.zhou@connect.ust.hk")
Imports: MASS, zoo, mvtnorm, magrittr, parallel

Title: Ease the Implementation of Imputation Methods
person("Tobias", "Rockel", , "Rockel.To@gmail.com", role = c("aut", "cre"))
Description: The general workflow of most imputation methods is quite
similar. The aim of this package is to provide parts of this general
workflow to make the implementation of imputation methods easier. The
heart of an imputation method is normally the used model. These models
can be defined using the 'parsnip' package or customized
specifications. The rest of an imputation method are more technical
specification e.g. which columns and rows should be used for
imputation and in which order. These technical specifications can be
Suggests: missMethods, rpart, testthat (>= 3.0.0)

Title: A collection of methods for left-censored missing data
Description: The package contains a collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR)  mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random.
Depends: R (>= 2.10), tmvtnorm, norm, pcaMethods, impute
Packaged: 2014-07-12 18:51:48 UTC; cosminlazar

Description: The package contains a collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR)  mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.
Packaged: 2015-01-18 12:23:51 UTC; cosminlazar