Suite of functions to study animal incubation.
    At the core of incR 
    lies an algorithm that allows for the scoring of 
    incubation behaviour. Additionally, several functions 
    extract biologically relevant metrics of incubation such as off-bout number 
    and off-bout duration - for a review of avian incubation studies, 
    see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) 
    edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.

Tools for estimating incidence from biomarker data in cross-
    sectional surveys, and for calibrating tests for recent infection. 
    Implements and extends the method of Kassanjee et al. (2012)
    <doi:10.1097/EDE.0b013e3182576c07>.

Fit parametric models for time-to-event data that show an initial
    'incubation period', i.e., a variable delay phase where the hazard is zero. The
    delayed Weibull distribution serves as foundational data model. The
    specific method of 'MPSE' (maximum product of spacings estimation) and MLE-based methods are used for parameter
    estimation. Bootstrap confidence intervals for parameters and significance
    tests in a two group setting are provided.

A 'shiny' app that supports both dual and bulk RNA-seq, with the dual
    RNA-seq functionality offering the flexibility to perform either a
    sequential approach (where reads are mapped separately to each genome)
    or a combined approach (where reads are aligned to a single merged
    genome). The user-friendly interface automates the analysis process,
    providing step-by-step guidance, making it easy for users to navigate
    between different analysis steps, and download intermediate results
    and publication-ready plots.

Genome-wide gene insertion and deletion rates can be modelled in a maximum 
    likelihood framework with the additional flexibility of modelling potential missing 
    data using the models included within. These models simultaneously estimate insertion 
    and deletion (indel) rates of gene families and proportions of "missing" data for 
    (multiple) taxa of interest. The likelihood framework is utilized for parameter 
    estimation. A phylogenetic tree of the taxa and gene presence/absence patterns 
    (with data ordered by the tips of the tree) are required. See Dang et al.
    (2016) <doi:10.1534/genetics.116.191973> for more details.