Authors@R: c(person("Hassan Roland", "Nasser", role = c("aut", "cre"),
person("Marie", "Schneider", role = "ctb",
person("Joanna", "Stachowicz ", role = "rev",
person("Christina", "Umstaetter ", role = "ths",
Author: Hassan Roland Nasser [aut, cre],
Maintainer: Hassan Roland Nasser <hassan.nasser@me.com>
Description: Analyze and visualize the rhythmic behavior of animals using the
degree of functional coupling (See Scheibe (1999) <doi:10.1076/brhm.30.2.216.1420>),
compute and visualize harmonic power, actograms, average activity and diurnality
Imports: tidyr, readr (>= 2.0.1), magrittr, dplyr, xts, pracma,
ggplot2, lubridate, stringr, zoo, crayon, stats
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), usethis

person("Marie", "Schneider", role = c("aut", "ctb"),
person("Joanna", "Stachowicz ", role = c("aut", "rev"),
person("Christina", "Umstaetter ", role = c("aut", "ths"),

Title: Tests for Detecting Irregular Digit Patterns
Description: Provides statistical tests and support functions for detecting irregular digit patterns in numerical data. The package includes tools for extracting digits at various locations in a number, tests for repeated values, and (Bayesian) tests of digit distributions.
Suggests: benford.analysis, BenfordTests, BeyondBenford, knitr,

Title: Deconvolution of Bulk RNA-Seq Data Based on Deep Learning
person(given = "Diego", family = "Mañanes", email = "dmananesc@cnic.es", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7247-6794")),
person(given = "Carlos", family = "Torroja", email = "ctorroja@cnic.es", role = "aut", comment = c(ORCID = "0000-0001-8914-3400")),
person(given = "Fatima", family = "Sanchez-Cabo", email = "fscabo@cnic.es", role = "aut", comment = c(ORCID = "0000-0003-1881-1664")))
Description: Deconvolution of bulk RNA-Seq data using context-specific deconvolution models based on Deep Neural Networks using scRNA-Seq data as input. These models are able to make accurate estimates of the cell composition of bulk RNA-Seq samples from the same context using the advances provided by Deep Learning and the meaningful information provided by scRNA-Seq data. See Torroja and Sanchez-Cabo (2017) <doi:10.3389/fgene.2019.00978> for more details.
Imports: rlang, Matrix, Matrix.utils, methods, tidyr,
stats, pbapply, S4Vectors, dplyr, tools, reshape2, gtools,
edgeR, reticulate, keras, tensorflow, ggplot2, ggpubr,
Suggests: knitr, rmarkdown, BiocParallel, rhdf5, DelayedArray,
SystemRequirements: Python (>= 2.7.0), TensorFlow
Packaged: 2021-10-08 07:35:44 UTC; diegommcc
Author: Diego Mañanes [aut, cre] (<https://orcid.org/0000-0001-7247-6794>),