Xiang Lyu, Will Wei Sun, Zhaoran Wang, Han Liu, Jian Yang, Guang Cheng

Melting Temperature of Nucleic Acid Sequences

Miscellaneous data and utility functions
for manipulating data and your R environment.

Miscellaneous utility functions for data manipulation,
data tidying, and working with gene expression data.

person("Stephen", "Turner", email = "vustephen@gmail.com", role = c("aut", "cre"))

person("Stephen", "Turner", email = "vustephen@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9140-9028"))
Stephen Turner [aut, cre] (<https://orcid.org/0000-0001-9140-9028>)

dplyr, tibble, utils, rstudioapi, methods

dplyr, tibble, utils, rstudioapi, methods, magrittr, stats

Identification of Conditionally Essential Genes in Transposon
Sequencing Studies
methods, limma, edgeR, Ckmeans.1d.dp, Biobase
Identification of conditionally essential genes using high-throughput sequencing data from transposon mutant libraries.

An R wrapper for using 'TooManyCells', a command line program for clustering, visualizing, and quantifying cell clade relationships. See <https://gregoryschwartz.github.io/too-many-cells/> for more details.
Matrix, imager, ggplot2, cowplot, jsonlite

Multivariate Tools for Compositional Data Analysis
R (>= 1.8.0), MASS, HardyWeinberg, calibrate
Provides functions for multivariate analysis with compositional data. Includes a function for doing compositional canonical correlation analysis. This analysis requires two data matrices of compositions, which can be adequately transformed and used as entries in a specialized program for canonical correlation analysis, that is able to deal with singular covariance matrices. The methodology is described in Graffelman et al. (2017) <doi:10.1101/144584>.

R (>= 1.8.0), MASS, calibrate, robCompositions
Provides functions for multivariate analysis with compositional data. Includes a function for doing compositional canonical correlation analysis.  This analysis requires two data matrices of compositions, which can be adequately transformed and used as entries in a specialized program for canonical correlation analysis, that is able to deal with singular covariance matrices. The methodology is described in Graffelman et al. (2017) <doi:10.1101/144584>. A function for log-ratio principal component analysis with condition number computations has been added to the package.