Provides a user-friendly interface for managing PostgreSQL
    database connection settings.  The package supplies helper functions to
    create, edit and load connection and option configuration files stored in a
    user-specific directory using the 'odbc' and 'RPostgres' back ends.  These
    helpers make it easy to construct a reproducible connection string from a
    configuration file, either by reading user-defined YAML files or by parsing
    an environment variable.

An enhanced version of the semi-empirical, spatially distributed
    emission and transport model PhosFate implemented in 'R' and 'C++'. It is 
    based on the D-infinity, but also supports the D8 flow method. The currently
    available substances are suspended solids (SS) and particulate phosphorus 
    (PP). A major feature is the allocation of substance loads entering surface 
    waters to their sources of origin, which is a basic requirement for the 
    identification of critical source areas and in consequence a cost-effective
    implementation of mitigation measures. References: Hepp et al. (2022)
    <doi:10.1016/j.jenvman.2022.114514>; Hepp and Zessner (2019)
    <doi:10.3390/w11102161>; Kovacs (2013)
    <http://hdl.handle.net/20.500.12708/9468>.

Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data (Goolsby et al. 2017). Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.

Work with the PhyloPic Web Service (<http://api-docs.phylopic.org/v2/>) 
    to fetch silhouette images of organisms. Includes functions for adding 
    silhouettes to both base R plots and ggplot2 plots.

Get information (boards, pins and
    users) from the Pinterest <http://www.pinterest.com>
    API.

A test for the well-specification of the linear instrumental
    variable model. The test is based on trying to predict the residuals of a
    two-stage least-squares regression using a random forest. Details can be
    found in Scheidegger, Londschien and Bühlmann (2025) "A residual prediction
    test for the well-specification of linear instrumental variable models" 
    <doi:10.48550/arXiv.2506.12771>.