Time series aggregation for incomplete time series data.
JS Lessels. Faculty of Agriculture, Food and Natural Resources.
The University of Sydney, Australia.

'daysAgg.R' 'hoursAgg.R' 'monthsAgg.R' 'timeSeries.R'
'yearsAgg.R' 'zoo2timeSeries.R'

Tools for Causal Inference with Possibly Invalid Instrumental
Variables
c(
person(given="David",
family = "Carl",
role = c("aut", "cre"),
email = "dcarl@ethz.ch",
comment = c(ORCID = "0000-0003-0615-0133")),
person(given = "Corinne",
family = "Emmenegger",
role = c("aut"),
email = "emmenegger@stat.math.ethz.ch",
comment = c(ORCID = "0000-0003-0353-8888")),
person(given = "Wei",
family = "Yuan",
role = c("aut"),
email = "wy204@stat.rutgers.edu"),
person(given = "Zijian",
family = "Guo",
role = c("aut"),
email = "zijguo@stat.rutgers.edu",
comment = c(ORCID = "0000-0002-2888-7016")))
Two stage curvature identification with machine learning for causal
inference in settings when instrumental variable regression is not suitable
because of potentially invalid instrumental variables. Based on Guo and
Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning:
Causal Inference with Possibly Invalid Instrumental Variables"
<arXiv:2203.12808> .
David Carl [aut, cre] (<https://orcid.org/0000-0003-0615-0133>),
Corinne Emmenegger [aut] (<https://orcid.org/0000-0003-0353-8888>),
Wei Yuan [aut],
Zijian Guo [aut] (<https://orcid.org/0000-0002-2888-7016>)

person("Tianjian", "Yang", email = "yangtj5@mail2.sysu.edu.cn",
role = c("aut", "cre"))
A set of functions to implement Time Series Cointegrated System (TSCS)
spatial interpolation and relevant data visualization.

Training Set Determination for Genomic Selection
Determining training set for genomic selection using a genetic algorithm (Holland J.H. (1975) <DOI:10.1145/1216504.1216510>) or simple exchange algorithm (change an individual every iteration). Three different criteria are used in both algorithms, which are r-score (Ou J.H., Liao C.T. (2018) <DOI:10.6342/NTU201802290>), PEV-score (Akdemir D. et al. (2015) <DOI:10.1186/s12711-015-0116-6>) and CD-score (Laloe D. (1993) <DOI:10.1186/1297-9686-25-6-557>). Phenotypic data for candidate set is not necessary for all these methods. By using it, one may readily determine a training set that can be expected to provide a better training set comparing to random sampling.