SPatial and Optimally Temporal (SPOT) Sampling
c(
person("Esther", "Eustache", email = "esther.eustache@unine.ch",role = c("aut", "cre")
),
person("Raphael", "Jauslin",role = c("aut"),
comment = c(ORCID = "0000-0003-1088-3356")
),
person("Yves", "Tille",role = c("aut"),
comment = c(ORCID = "0000-0003-0904-5523"))
)
In spatial data, information of two neighboring units are generally very similar.
For spatial sampling, it is therefore more efficient to select samples that are well spread out in space. Often, the interest lies not only in estimating a measure at one point in time, but rather in estimating several points in time to also study the evolution.
Three new methods called Orfs (Optimal Rotation with Fixed sample Size), Orsp (Optimal Rotation with Spread sample), and Spot (Spatial and Optimally Temporal Sampling) are implemented in this package. Orfs allows to select temporal samples with fixed size. Orsp selects spatio-temporal samples with random size that are well spread out in space at each point in time. And Spot generates spread sample with fixed sample size at each wave. These methods provide an optimal time rotation of the selected units using the systematic sampling.
BalancedSampling, sampling, stats, utils, pracma,
WaveSampling, MASS
Esther Eustache [aut, cre],
Raphael Jauslin [aut] (<https://orcid.org/0000-0003-1088-3356>),
Yves Tille [aut] (<https://orcid.org/0000-0003-0904-5523>)

Rendering Parameterized SQL and Translation to Dialects
Martijn J. Schuemie [aut, cre],
Marc A. Suchard [aut],
A rendering tool for parameterized SQL that also translates into
different SQL dialects.  These dialects include Sql Server, Oracle,
PostgreSql, Amazon RedShift, and Microsoft PDW.

A rendering tool for parameterized SQL that also translates into
different SQL dialects.  These dialects include 'Microsoft Sql Server', 'Oracle',
'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', 'Snowflake',
'Azure Synapse Analytics Dedicated', 'Apache Spark', and 'SQLite'.
testthat, knitr, rmarkdown, shiny, shinydashboard