Classification Trees with Imprecise Probabilities
person("Paul", "Fink",
email = "paul.fink@stat.uni-muenchen.de",
role = c("aut", "cre"))
Creation of imprecise classification trees. They rely on
probability estimation within each node by means of either the
imprecise Dirichlet model or the nonparametric predictive
inference approach. The splitting variable is selected by the
strategy presented in Fink and Crossman (2013)
<http://www.sipta.org/isipta13/index.php?id=paper&paper=014.html>,
but also the original imprecise information gain of Abellan and
Moral (2003) <doi:10.1002/int.10143> is covered.

Fill missing values in a data matrix using SVD imputation
or kNN imputation.

Fill missing values in a data matrix using SVD, SVT, or
kNN imputation.

Fill missing values in a data matrix using SVD, SVT, or
kNN imputation.  Performance improvements to be released by
version 1.4.

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

Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu
Imputation for microarray data (currently KNN only)

Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan,
Gilbert Chu

Imputation of Financial Time Series with Missing Values
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).
The package is based on the paper:
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
Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.
c(
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")
)
bookdown, knitr, ggplot2, prettydoc, rmarkdown, R.rsp,
testthat, xts
Junyan Liu [aut],
Daniel P. Palomar [cre, aut]