Designed to help health economic modellers when building and reviewing models. 
    The visualisation functions allow users to more easily review the network of functions 
    in a project, and get lay summaries of them. The asserts included are intended to check for common errors,
    thereby freeing up time for modellers to focus on tests specific to the individual model in development or review.
    For more details see Smith and colleagues (2024)<doi:10.12688/wellcomeopenres.23180.1>.

Provides simple assertions with sensible defaults and customisable error messages. 
  It offers convenient assertion call wrappers and a general assert function that can handle any condition.
  Default error messages are user friendly and easily customized with inline code evaluation and styling powered by the 'cli' package.

Provides functionality to assert conditions
    that have to be met so that errors in data used in
    analysis pipelines can fail quickly. Similar to
    'stopifnot()' but more powerful, friendly, and easier
    for use in pipelines.

An extension to stopifnot() that makes it easy to declare 
    the pre and post conditions that you code should satisfy, while also 
    producing friendly error messages so that your users know what's gone
    wrong.

Provides assessment tools for regression models with discrete and semicontinuous outcomes proposed in Yang (2021) <doi:10.1080/10618600.2021.1910042>, Yang (2024) <doi:10.1080/10618600.2024.2303336>, Yang (2024) <doi:10.1093/biomtc/ujae007>, and Yang (2026) <doi:10.1002/cjs.70046>. It calculates the double probability integral transform (DPIT) residuals. It also constructs QQ plots of residuals the ordered curve for assessing mean structures, quasi-empirical distribution function for overall assessment, and a formal goodness-of-fit test.

Calculates the optimal price of assets (such as
	airline flight seats, hotel room bookings) whose value
	becomes zero after a fixed ``expiry date''.  Assumes
	potential customers arrive (possibly in groups) according
	to a known inhomogeneous Poisson process.  Also assumes a
	known time-varying elasticity of demand (price sensitivity)
	function.  Uses elementary techniques based on ordinary
	differential equations.  Uses the package deSolve to effect
	the solution of these differential equations.