Joint dimension reduction and spatial clustering is conducted for
    Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to 
    Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems.  Gadagkar, S. R. and Call, G. B. (2015) <doi:10.1016/j.vascn.2014.08.006> Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) <doi:10.1371/journal.pone.0146021>.

Decodes meshes and point cloud data encoded by the Draco mesh
    compression library from Google. Note that this is only designed for basic
    decoding and not intended as a full scale wrapping of the Draco library.

Creates interactive draggable plots from grouped projected
    'sf' geometry. The primary deliverable is a browser-based 'D3' helper
    where regions and labels can be moved freely; users drag, then copy or
    download the resulting offset tables. Labels can be derived
    automatically with make_region_labels(), supplied directly with
    as_drag_labels(), and their moved positions saved and restored with
    read_label_state() and apply_label_state(). Hierarchical spatial
    datasets are supported via hierarchy detection, upload profiling,
    make_hierarchy_key(), and inherit_layout(), which recommend parent-child
    groupings and propagate parent-level drag offsets to finer child groupings.
    Automatic starting layouts are provided by suggest_offsets() using
    radial, grid, or directional algorithms. Spatial file diagnostics are
    available through dragmapr_diagnostics(). When a reproducible static
    image is also needed, render_dragged_map() reconstructs the layout as
    a 'ggplot2' plot from the source geometry plus the exported offset
    tables. Project bundles can be written with write_dragmapr_project()
    and rendered with render_dragmapr_project(). The interactive layer is
    built on the 'D3' library: Bostock, Ogievetsky and Heer (2011)
    <doi:10.1109/TVCG.2011.185>. Spatial data handling uses the 'sf'
    package: Pebesma (2018) <doi:10.32614/RJ-2018-009>.