Description: Fill missing values in a data matrix using SVD imputation

Description: Fill missing values in a data matrix using SVD, SVT, or

kNN imputation.  Performance improvements to be released by

Description: Fill missing values in a data matrix using mean, kNN, SVD,
Singular Value Thresholding, tree, or linear imputation,.
Collate: 'SVD.R' 'SVT.R' 'gbmImpute.R' 'kNN.R' 'meanImpute.R' 'utils.R'

Title: impute: Imputation for microarray data
Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu
Description: Imputation for microarray data (currently KNN only)

Packaged: Tue Oct 19 18:34:48 2004; naras

Packaged: Tue May 10 08:58:44 2005; naras

Packaged: Fri Jun 17 13:40:11 2005; naras

Packaged: Mon Apr 17 11:47:58 2006; naras

Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan,
Packaged: 2009-04-21 09:12:10 UTC; biocbuild

Packaged: 2009-11-12 09:01:50 UTC; biocbuild

Packaged: 2010-04-22 22:32:37 UTC; biocbuild

Packaged: 2010-10-18 08:10:51 UTC; biocbuild

Packaged: 2011-04-14 06:18:52 UTC; biocbuild

Title: Imputation of Financial Time Series with Missing Values
Description: Missing values often occur in financial data due to a variety
of reasons (errors in the collection process or in the processing stage,
lack of asset liquidity, lack of reporting of funds, etc.). However,
most data analysis methods expect complete data and cannot be employed
with missing values. One convenient way to deal with this issue without
having to redesign the data analysis method is to impute the missing
values. This package provides an efficient way to impute the missing
values based on modeling the time series with a random walk or an
autoregressive (AR) model, convenient to model log-prices and log-volumes
in financial data. In the current version, the imputation is
univariate-based (so no asset correlation is used).
J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of
Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on
person("Junyan", "Liu", role =  "aut", email = "jliubl@connect.ust.hk"),
person(c("Daniel", "P."), "Palomar", role = c("cre", "aut"), email = "daniel.p.palomar@gmail.com")
Suggests: bookdown, knitr, ggplot2, prettydoc, rmarkdown, R.rsp,