This program analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. Reference to software development can be found at <doi:10.1021/acs.jproteome.0c00872>.
readxl, writexl, optimr, data.table, plotrix, tidyr, ggplot2,
UpSetR

c(person("Neil", "McCracken", email = "namccrac@iu.edu",role = c("aut")),
person("Hao", "Liu", role = c("ctb")),
person("Amber", "Mosley", email = "almosley@iu.edu",role = c("cre")))
Analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment.
McCracken (2021) <doi:10.1021/acs.jproteome.0c00872>.
readxl, data.table, plotrix, tidyr, ggplot2, xlsx, httr,
jsonlite, GGally, network, stats, RColorBrewer, svglite
Neil McCracken [aut],
Hao Liu [ctb],
Amber Mosley [cre]

Calculates the Probability of Informed Trading (PIN)
Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi: 10.1016/j.jbankfin.2011.08.003> and hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> .

Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .