Differential Analysis of Rhythmic Transcriptome Data
c(
person('Jake', 'Hughey', , 'jakejhughey@gmail.com', c('aut', 'cre')),
person('Jordan', 'Singer', , 'jordan.m.singer@vumc.org', 'ctb'))
A flexible approach, inspired by cosinor regression, for
differential analysis of rhythmic transcriptome data. See Singer and Hughey
(2018) <doi:10.1177/0748730418813785>.
Jake Hughey [aut, cre],
Jordan Singer [ctb]

Quantify Rhythmicity and Differential Rhythmicity in Genomic
Data
c(
person('Jake', 'Hughey', , 'jakejhughey@gmail.com', c('aut', 'cre')),
person('Dora', 'Obodo', , 'dora.obodo@vanderbilt.edu', 'aut'),
person('Elliot', 'Outland', , 'ehoutland@gmail.com', 'aut'))
Fit linear models based on periodic splines, moderate model
coefficients using multivariate adaptive shrinkage, then compute properties of
the moderated curves.
Jake Hughey [aut, cre],
Dora Obodo [aut],
Elliot Outland [aut]

Perform Polynomial Evaluation of Linearity
Evaluates whether the relationship between two vectors is linear or nonlinear. Performs a test to determine how well a linear model fits the data compared to higher order polynomial models. Jhang et al. (2004) <doi:10.1043/1543-2165(2004)128%3C44:EOLITC%3E2.0.CO;2>.

David Moriña (ISGlobal, Centre for Research in Environmental Epidemiology)
Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures.

This package implements the methodology of
Likelihood-based Imprecise Regression (LIR) for the case of
linear regression with interval data.

c(
person("Ethan Z.", "Welty", email = "ethan.welty+linbin@gmail.com", role = c("aut", "cre")),
person("Christian E.", "Torgersen", role = "ctb", comment = "author support and guidance"),
person("Samuel J.", "Brenkman", role = "ctb", comment = "elwha and quinault datasets"),
person("Jeffrey J.", "Duda", role = "ctb", comment = "elwha dataset"),
person("Jonathan B.", "Armstrong", role = "ctb", comment = "fishmotion dataset"))
Binning and Plotting of Linearly Referenced Data
Short for 'linear binning', the linbin package provides functions for manipulating,
binning, and plotting linearly referenced data. Although developed for data collected on river
networks, it can be used with any interval or point data referenced to a 1-dimensional coordinate
system. Flexible bin generation and batch processing makes it easy to compute and visualize
variables at multiple scales, useful for identifying patterns within and between variables and
investigating the influence of scale of observation on data interpretation.
Ethan Z. Welty [aut, cre],
Christian E. Torgersen [ctb] (author support and guidance),
Samuel J. Brenkman [ctb] (elwha and quinault datasets),
Jeffrey J. Duda [ctb] (elwha dataset),
Jonathan B. Armstrong [ctb] (fishmotion dataset)