Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.This package implements the methods described by Hu et al. (2022) <doi:10.1002/sim.9548>.

Allows interaction with 'Interactive Brokers' 'Trader Workstation'
    <https://interactivebrokers.github.io/tws-api/>.
    Handles the connection over the network and the exchange of messages.
    Data is encoded and decoded between user and wire formats.
    Data structures and functionality closely mirror the official implementations.

Recursive algorithms for computing various relatedness
    coefficients, including pairwise kinship, kappa and identity
    coefficients. Both autosomal and X-linked coefficients are computed.
    Founders are allowed to be inbred, which enables construction of any
    given kappa coefficients, as described in Vigeland (2020)
    <doi:10.1007/s00285-020-01505-x>. In addition to the standard
    coefficients, 'ribd' also computes a range of lesser-known
    coefficients, including generalised kinship coefficients, multi-person
    coefficients and two-locus coefficients (Vigeland, 2023,
    <doi:10.1093/g3journal/jkac326>). Many features of 'ribd' are
    available through the online app 'QuickPed' at
    <https://magnusdv.shinyapps.io/quickped>; see Vigeland (2022)
    <doi:10.1186/s12859-022-04759-y>.

The provided benchmark suite enables the automated evaluation and comparison of any existing and novel indirect method for reference interval ('RI') estimation in a systematic way.
	Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated
	statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be 
	used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty.
	To include any indirect method, a custom wrapper function needs to be provided. 
	The package offers functions for generating the test sets, executing the indirect method and evaluating the results.
	See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features. 
	A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) <doi:10.1093/clinchem/hvac142>.