Provides tools for Bayesian learning of spatiotemporal dynamical mechanistic models.
    Includes methods for parameter estimation, simulation, and inference using hierarchical and
    state-space modeling approaches, following Banerjee, Chen, Frankenburg and Zhou (2025)
    <https://jmlr.org/papers/v26/22-0896.html>.

Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163). The package features in Mariel et al. (2025) Environmental Valuation with Discrete Choice Experiments in R. (<doi:10.1007/978-3-031-89338-4>).

Provides functionality for simulating data generation processes across various spatial regression models, conceptually aligned with the 'dgp' module of the 'Python' library 'spreg' <https://pysal.org/spreg/api.html#dgp>.

Logging functions in 'RcppSpdlog' provide access to the logging
 functionality from the 'spdlog' 'C++' library. This package offers shorter convenience
 wrappers for the 'R' functions which match the 'C++' functions, namely via, say,
 'spdl::debug()' at the debug level. The actual formatting is done by the
 'fmt::format()' function from the 'fmtlib' library (that is also 'std::format()'
 in 'C++20' or later).

An implementation of split-population duration regression models. 
    Unlike regular duration models, split-population duration models are
    mixture models that accommodate the presence of a sub-population that is 
    not at risk for failure, e.g. cancer patients who have been cured by 
    treatment. This package implements Weibull and Loglogistic forms for the 
    duration component, and focuses on data with time-varying covariates. 
    These models were originally formulated in Boag (1949) and Berkson and Gage 
    (1952), and extended in Schmidt and Witte (1989).