Joshua Schwab, Maya Petersen, and Mark van der Laan, with
contributions from Susan Gruber
c(person("Joshua", "Schwab", role=c("aut", "cre"),
email="joshuaschwab@yahoo.com"), person("Maya", "Petersen",
role="aut"), person("Mark", "van der Laan", role="aut"),
person("Susan", "Gruber", role="ctb"))
Targeted Maximum Likelihood Estimation (TMLE) of
treatment/censoring specific mean outcome or marginal
structural model for point-treatment and longitudinal data

c(person("Joshua", "Schwab", role=c("aut", "cre"),
email="jschwab77@berkeley.edu"), person("Samuel", "Lendle", role="aut"),
person("Maya", "Petersen", role="aut"), person("Mark", "van der Laan",
role="aut"), person("Susan", "Gruber", role="ctb"))

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

Methods of developing linear time series modelling.  Methods are given for loglikelihood computation, forecasting and simulation.

Methods of developing linear time series modelling.
Methods are given for loglikelihood computation, forecasting
and simulation.

Ridge and Liu Estimates based on LTS (Least Trimmed Squares)
Method
Betul Kan Kilinc [aut, cre], Ozlem Alpu [aut, cre]
This is a new tool to estimate Ridge and Liu estimators based on LTS method in multiple linear regression analysis.

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

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

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)>.

Implement local time space variogram estimation and
kriging
R (>= 2.10), mvtnorm, fields, gstat, snow, spam, sp,
spacetime, zoo, xts

Sparse Principal Component Based on Least Trimmed Squares
c(
person(given="Yixin",family="Wang", role = c("aut","cre"),
email="wangyixin07@outlook.com"),
person(given="Stefan",family="Van Aelst", role = c("aut"),
email="stefan.vanaelst@kuleuven.be"),
person(given="Holger",family="Cevallos Valdiviezo",role=c("ctb"),
comment = "Original R code for the LTS-PCA algorithm"),
person(given="Tom",family="Reynkens",role=c("ctb"),
comment = "Original R code for angle in the rospca package")
)
Implementation of robust and sparse PCA algorithm of Wang and Van Aelst (2019) <DOI:10.1080/00401706.2019.1671234>.
robustbase, rrcov, stats, mvtnorm, graphics, knitr,
rmarkdown, testthat
Yixin Wang [aut, cre],
Stefan Van Aelst [aut],
Holger Cevallos Valdiviezo [ctb] (Original R code for the LTS-PCA
algorithm),
Tom Reynkens [ctb] (Original R code for angle in the rospca package)