An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook') <https://jupyter.org>.
    Designed to be used from a running 'IRkernel' session <https://irkernel.github.io>.

Contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.

Calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements (Kovacic and Varnai (2014) <doi:10.1097/EDE.0000000000000139>). ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects' means and residuals) is provided.

Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2013) <arXiv:1606.03658>.

Provides examples of code for analyzing data or accomplishing tasks that may be useful to institutional or educational researchers.

A comprehensive suite of tools for managing, processing, and
    analyzing data from the IFCB. I R FlowCytobot ('iRfcb') supports
    quality control, geospatial analysis, and preparation of IFCB data for
    publication in databases like <https://www.gbif.org>,
    <https://www.obis.org>, <https://emodnet.ec.europa.eu/en>,
    <https://shark.smhi.se/>, and <https://www.ecotaxa.org>. The package
    integrates with the MATLAB 'ifcb-analysis' tool, which is described in
    Sosik and Olson (2007) <doi:10.4319/lom.2007.5.204>, and provides
    features for working with raw, manually classified, and machine
    learning–classified image datasets. Key functionalities include image
    extraction, particle size distribution analysis, taxonomic data
    handling, and biomass concentration calculations, essential for
    plankton research.