Suggests: SuperLearner, memoise, DEoptim, testthat, tmle

Author: Joshua Schwab, Maya Petersen, and Mark van der Laan, with
role="aut"), person("Mark", "van der Laan", role="aut"),
treatment/censoring specific mean outcome or marginal
structural model for point-treatment and longitudinal data

Suggests: SuperLearner, testthat, tmle, knitr, rmarkdown, nnls, arm

Author: A.I. McLeod, Hao Yu, Zinovi Krougly
Description: Methods of developing linear time series modelling.  Methods are given for loglikelihood computation, forecasting and simulation.

Packaged: Tue Jan  8 10:06:55 2008; hornik

Description: Methods of developing linear time series modelling.
Methods are given for loglikelihood computation, forecasting

Packaged: 2015-08-22 19:37:22 UTC; IanMcLeod

Packaged: 2015-12-21 02:25:34 UTC; IanMcLeod

Title: Ridge and Liu Estimates based on LTS (Least Trimmed Squares)
Author: Betul Kan Kilinc [aut, cre], Ozlem Alpu [aut, cre]
Maintainer: Betul Kan Kilinc <bkan@anadolu.edu.tr>
Description: This is a new tool to estimate Ridge and Liu estimators based on LTS method in multiple linear regression analysis.

Author: Jin Chen, Jun Chen, Naresh Kumar, Dong Liang
Description: Implement local time space variogram estimation and kriging

Author: Naresh Kumar, Dong Liang, Jin Chen, Jun Chen
Description: Implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively.

Description: Implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively. The method is documented in Kumar et al (2013) <https://www.nature.com/articles/jes201352)>.

Description: Implement local time space variogram estimation and
Depends: R (>= 2.10), mvtnorm, fields, gstat, snow, spam, sp,

Title: Sparse Principal Component Based on Least Trimmed Squares
person(given="Stefan",family="Van Aelst", role = c("aut"),
comment = "Original R code for the LTS-PCA algorithm"),
comment = "Original R code for angle in the rospca package")
Description: Implementation of robust and sparse PCA algorithm of Wang and Van Aelst (2019) <DOI:10.1080/00401706.2019.1671234>.
Suggests: robustbase, rrcov, stats, mvtnorm, graphics, knitr,
Holger Cevallos Valdiviezo [ctb] (Original R code for the LTS-PCA
Tom Reynkens [ctb] (Original R code for angle in the rospca package)