c(
person("Radosław", "Piliszek", email = "r.piliszek@uwb.edu.pl", role = c("aut", "cre")),
person("Krzysztof", "Mnich", email = "k.mnich@uwb.edu.pl", role = "aut"),
person("Paweł", "Tabaszewski", email = "tabaszewski.pawel@gmail.com", role = "aut"),
person("Szymon", "Migacz", email = "szmigacz@gmail.com", role = "aut"),
person("Andrzej", "Sułecki", email = "asulecki@gmail.com", role = "aut"),
person(c("Witold", "Remigiusz"), "Rudnicki", email = "w.rudnicki@icm.edu.pl", role = "aut"))
Functions for MultiDimensional Feature Selection (MDFS):
calculating multidimensional information gains, scoring variables,
finding important variables, plotting selection results.
This package includes an optional CUDA implementation that speeds up
information gain calculation using NVIDIA GPGPUs.
Radosław Piliszek [aut, cre],
Krzysztof Mnich [aut],
Paweł Tabaszewski [aut],
Szymon Migacz [aut],
Andrzej Sułecki [aut],
Witold Remigiusz Rudnicki [aut]

Functions for MultiDimensional Feature Selection (MDFS):
calculating multidimensional information gains, scoring variables,
finding important variables, plotting selection results.
This package includes an optional CUDA implementation that speeds up
information gain calculation using NVIDIA GPGPUs.
R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.

The multinomial diversity model is a toolbox for relating diversity to complex predictors. It is based on (1) Shannon diversity; (2) the multinomial logit model, and (3) the link between Shannon diversity and the log-likelihood of the MLM.

The multinomial diversity model is a toolbox for relating
diversity to complex predictors. It is based on (1) Shannon
diversity; (2) the multinomial logit model, and (3) the link
between Shannon diversity and the log-likelihood of the MLM.

Glenn De'ath <g.death@aims.gov.au>; Code for mdm was adapted from multinom in the nnet package (Brian Ripley <ripley@stats.ox.ac.uk> and Bill Venables <Bill.Venables@csiro.au>)

Molecular Detection Mapping and Analysis Platform
Runs a Shiny web application that merges raw 'qPCR' fluorescence data with related
metadata to visualize species presence/absence detection patterns and assess data quality.
The application calculates threshold values from raw fluorescence data using a method based
on the second derivative method, Luu-The et al (2005) <doi:10.2144/05382RR05>,  and utilizes
the ‘chipPCR’ package by Rödiger, Burdukiewicz, & Schierack (2015) <doi:10.1093/bioinformatics/btv205>
to calculate Cq values. The application has the ability to connect to a custom developed MySQL
database to populate the applications interface. The application allows users to interact with
visualizations such as a dynamic map, amplification curves and standard curves, that allow for
zooming and/or filtering. It also enables the generation of customized exportable reports based
on filtered mapping data.
RMySQL, shinydashboard, DBI, DT, leaflet, leaflet.extras,
shinyjs, ggplot2, dplyr, readxl, plotly, reactable, writexl,
xfun, berryFunctions, shinyWidgets, shiny, htmltools, methods,
utils, stats