Functions for estimating structural equation models using 
    instrumental variables.

An interface to build machine learning models for
    classification and regression problems. 'mikropml' implements the ML
    pipeline described by Topçuoğlu et al. (2020)
    <doi:10.1128/mBio.00434-20> with reasonable default options for data
    preprocessing, hyperparameter tuning, cross-validation, testing, model
    evaluation, and interpretation steps.  See the website
    <https://www.schlosslab.org/mikropml/> for more information,
    documentation, and examples.

Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. 
  This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions.
  It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022.
  Citation: Smug, B. J., Opalek, M., Necki, M., & Wloch-Salamon, D. (2024). Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase. Methods in Ecology and Evolution, 15, 301–307 <doi:10.1111/2041-210X.14269>.

Weakly supervised (WS), multiple instance (MI) data lives in
    numerous interesting applications such as drug discovery, object
    detection, and tumor prediction on whole slide images. The 'mildsvm'
    package provides an easy way to learn from this data by training
    Support Vector Machine (SVM)-based classifiers. It also contains
    helpful functions for building and printing multiple instance data
    frames. The core methods from 'mildsvm' come from the following
    references: Kent and Yu (2024) <doi:10.1214/24-AOAS1876>; Xiao, Liu, and Hao
    (2018) <doi:10.1109/TNNLS.2017.2766164>; Muandet et al. (2012)
    <https://proceedings.neurips.cc/paper/2012/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper.pdf>;
    Chu and Keerthi (2007) <doi:10.1162/neco.2007.19.3.792>; and Andrews
    et al. (2003)
    <https://papers.nips.cc/paper/2232-support-vector-machines-for-multiple-instance-learning.pdf>.
    Many functions use the 'Gurobi' optimization back-end to improve the
    optimization problem speed; the 'gurobi' R package and associated
    software can be downloaded from <https://www.gurobi.com> after
    obtaining a license.