Depends: astroFns, lubridate, plotrix, pracma, R (>= 3.4.1)
Packaged: 2017-08-18 21:05:18 UTC; Kiki Moreau

Packaged: 2017-09-12 11:21:03 UTC; Kiki Moreau

Depends: astroFns, lubridate, plotrix, pracma, R (>= 3.4.2)
Packaged: 2017-11-05 11:47:22 UTC; Kiki Moreau

Depends: astroFns, lubridate, plotrix, pracma, R (>= 3.4.0)
Packaged: 2018-01-06 19:43:42 UTC; Kiki Moreau

Depends: astroFns, lubridate, plotrix, pracma, R (>= 3.5.0)

Description: An adaptation of the multi-variable stochastic weather generator proposed in 'Rglimclim' to perform gap-filling and temporal extension at sub-daily resolution. Simulation is performed based on large scale variables and climatic observation data that could be generated from different gauged stations having geographical proximity. SWG relies on reanalyses. Multi-variable dependence is taking into account by using the decomposition of the product rule (in statistics) into conditional probabilities. See <https://hal.archives-ouvertes.fr/hal-02554676>.
Depends: R (>= 3.5.0), chron, glmnet, MASS

Title: Statistical Methods for Normalizing Metabolomics Data
Maintainer: Alysha M De Livera <alyshad@unimelb.edu.au>
Description: Metabolomics data are inevitably subject to a component of unwanted variation, due to factors such as batch effects, matrix effects, and confounding biological variation. This package contains a collection of R functions which can be used to remove unwanted variation and obtain normalized metabolomics data.

Title: Separate Metabolites into Likely Measurement Artifacts and True
Description: Split an untargeted metabolomics data set into a set of likely true
metabolites and a set of likely measurement artifacts. This process involves
comparing missing rates of pooled plasma samples and biological samples. The
functions assume a fixed injection order of samples where biological samples are
randomized and processed between intermittent pooled plasma samples. By comparing
patterns of missing data across injection order, metabolites that appear in blocks
and are likely artifacts can be separated from metabolites that seem to have
random dispersion of missing data. The two main metrics used are: 1. the number of
consecutive blocks of samples with present data and 2. the correlation of missing rates
between biological samples and flanking pooled plasma samples.