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Externally Studentized Midrange Distribution
Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers.
Computer Simulations of 'SNP' Data
Allows to simulate SNP data using genlight objects. For example, it is straight forward to simulate a simple drift scenario with exchange of individuals between two populations or create a new genlight object based on allele frequencies of an existing genlight object.
Estimate Entry Models
Tools for measuring empirically the effects of entry in concentrated markets, based in Bresnahan and Reiss (1991) < https://www.jstor.org/stable/2937655>.
'Shiny' Application for Whole Genome Duplication Analysis
Provides a comprehensive 'Shiny' application for analyzing Whole Genome Duplication ('WGD') events. This package provides a user-friendly 'Shiny' web application for non-experienced researchers to prepare input data and execute command lines for several well-known 'WGD' analysis tools, including 'wgd', 'ksrates', 'i-ADHoRe', 'OrthoFinder', and 'Whale'. This package also provides the source code for experienced researchers to adjust and install the package to their own server. Key Features 1) Input Data Preparation This package allows users to conveniently upload and format their data, making it compatible with various 'WGD' analysis tools. 2) Command Line Generation This package automatically generates the necessary command lines for selected 'WGD' analysis tools, reducing manual errors and saving time. 3) Visualization This package offers interactive visualizations to explore and interpret 'WGD' results, facilitating in-depth 'WGD' analysis. 4) Comparative Genomics Users can study and compare 'WGD' events across different species, aiding in evolutionary and comparative genomics studies. 5) User-Friendly Interface This 'Shiny' web application provides an intuitive and accessible interface, making 'WGD' analysis accessible to researchers and 'bioinformaticians' of all levels.
Estimating Speakers of Texts
Estimates the authors or speakers of texts. Methods developed in Huang, Perry, and Spirling (2020)
Analyze Data from Stepped Wedge Cluster Randomized Trials
Provide various functions and tools to help fit models for
estimating treatment effects in stepped wedge cluster randomized trials.
Implements methods described in Kenny, Voldal, Xia, and Heagerty (2022)
"Analysis of stepped wedge cluster randomized trials in the presence of a
time-varying treatment effect",
Estimating Speaker Style Distinctiveness
Estimates distinctiveness in speakers' (authors') style. Fits models that can be used for predicting speakers of new texts. Methods developed in Huang et al (2020)
Multiples Comparisons Procedures
Performs the execution of the main procedures of multiple comparisons in the literature, Scott-Knott (1974) < http://www.jstor.org/stable/2529204>, Batista (2016) < http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations and export to different extensions of its results. An additional part of the package is the presence of the performance evaluation of the tests (Type I error per experiment and the power). This will assist the user in making the decision for the chosen test.
Incorporate Expert Opinion with Parametric Survival Models
Enables users to incorporate expert opinion with parametric survival analysis using a Bayesian or frequentist approach. Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023)
Estimating Aboveground Biomass and Its Uncertainty in Tropical Forests
Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests.
These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty,
(3) construct height-diameter models, (4) manage tree and plot coordinates,
(5) estimate the aboveground biomass/carbon at the stand level with associated uncertainty.
To cite 'BIOMASS', please use citation("BIOMASS").
See more in the article of Réjou-Méchain et al. (2017)