Processing Chlorophyll Fluorescence & P700 Absorbance data. Four models are provided for the regression of Pi curves, which can be compared with each other in order to select the most suitable model for the data set. Control plots ensure the successful verification of each regression. Bundled output of alpha, ETRmax, Ik etc. enables fast and reliable further processing of the data.

Functions for easily reading and processing binary data files created by
    'Pamguard' (<https://www.pamguard.org/>). All functions for directly reading the
    binary data files are based on 'MATLAB' code written by Michael Oswald.

Data are partitioned (clustered) into k clusters "around medoids", which is
    a more robust version of K-means implemented in the function pam() in the 'cluster' package.
    The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>.
    Please refer to the pam() function documentation for more references.
    Clustered data is plotted as a split heatmap allowing visualisation of representative
    "group-clusters" (medoids) in the data as separated fractions of the graph while those
    "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.

Simulation functions to assess or explore the power of a dataset to estimates significant random effects (intercept or slope) in a mixed model. The functions are based on the "lme4" and "lmerTest" packages.

A collection of miscellaneous functions for passive acoustics. 
    Much of the content here is adapted to R from code written by other people.
    If you have any ideas of functions to add, please contact Taiki Sakai.

The Piece-wise exponential (Additive Mixed) Model
    (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a
    powerful model class for the analysis of survival (or time-to-event) data,
    based on Generalized Additive (Mixed) Models (GA(M)Ms).
    It offers intuitive specification and robust estimation of complex survival
    models with stratified baseline hazards, random effects, time-varying effects,
    time-dependent covariates and cumulative effects (Bender and others (2019)),
    as well as support for left-truncated data as well as competing risks,
    recurrent events and multi-state settings.
    pammtools provides tidy workflow for survival analysis with PAMMs,
    including data simulation, transformation and other functions for data
    preprocessing and model post-processing as well as visualization.