Serial Limiting Dilution Assay Statistics
Michael Hudgens, Ilana Trumble, Andrew Allmon
Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and the p-value goodness of fit for infectious units per million (IUPM) from serial limiting dilution assays. This package uses the likelihood equation, PGOF, and confidence intervals described in Meyers et. al. (1994).

Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for infectious units per million (IUPM) from serial limiting dilution assays. This package uses the likelihood equation, exact PGOF, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>.

Software for Analyzing Limiting Dilution Assays
Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for infectious units per million (IUPM) from serial limiting dilution assays. This package uses the likelihood equation, exact PGOF, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>. This software is also implemented as a web application through the Shiny R package <https://iupm.shinyapps.io/sldassay/>.

Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for concentration of infectious units from serial limiting dilution assays. This package uses the likelihood equation, exact goodness of fit p-values, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>. This software is also implemented as a web application through the Shiny R package <https://iupm.shinyapps.io/sldassay/>.

Statistical Learning Based Estimation of Mutual Information
c(person("Tomasz", "Jetka", email = "t.jetka@gmail.com", role = c("aut", "cre")),
person("Karol", "Nienaltowski",role="ctb"),person("Michal", "Komorowski",role="ctb"))
The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). Technically, it allows to estimate information-theoretic measures between finite-state input and multivariate, continuous output. Method described in Jetka et al. (2019) <doi:10.1371/journal.pcbi.1007132>.
e1071, ggplot2, ggthemes, gridExtra, nnet, Hmisc, reshape2,
stringr, doParallel, caret, corrplot, foreach
knitr, rmarkdown, testthat (>= 2.1.0), data.table, covr
Tomasz Jetka [aut, cre],
Karol Nienaltowski [ctb],
Michal Komorowski [ctb]