QTL mapping toolkit for inbred crosses and recombinant
        inbred lines. Includes maximum likelihood and Bayesian tools.

Adjusting the bias due to residual confounding (often called
    treatment selection bias) in estimating the treatment effect in a
    proportional hazard model, as described in Williamson et al.
    (2022) <doi:10.1158/1078-0432.ccr-21-2468>.

Performs brace expansions on strings.  Made popular by Unix shells, brace expansion allows users to concisely generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string.  The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.

The goal of this method is to identify associations between bacteria and an environmental variable in 16S or other compositional data. The environmental variable is any variable which is measure for each microbiome sample, for example, a butyrate measurement paired with every sample in the data. Microbiome data is compositional, meaning that the total abundance of each sample sums to 1, and this introduces severe statistical distortions. This method takes a Bayesian approach to correcting for these statistical distortions, in which the total abundance is treated as an unknown variable. This package runs the python implementation using reticulate.

Provides access to a range of functions for analyzing, applying and visualizing Bayesian response-adaptive trial designs for a binary endpoint. Includes the predictive probability approach and the predictive evidence value designs for binary endpoints.

Specify and fit the Bradley-Terry model, including structured
    versions in which the parameters are related to explanatory variables
    through a linear predictor and versions with contest-specific effects,
    such as a home advantage.

Forecasters predicting the chances of a future event may disagree due to
    differing evidence or noise. To harness the collective evidence of the crowd, 
    Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions"
    <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945> proposes a Bayesian
    aggregator that is regularized by analyzing the forecasters' disagreement and ascribing 
    over-dispersion to noise. This aggregator requires no user intervention and can be computed
    efficiently even for a large numbers of predictions. The author evaluates
    the aggregator on subjective probability predictions collected during
    a four-year forecasting tournament sponsored by the US intelligence community.
    The aggregator improves the accuracy of simple averaging by around 20% and
    other state-of-the-art aggregators by 10-25%. The advantage stems almost
    exclusively from improved calibration. This aggregator -- know as "the revealed
    aggregator" -- inputs a) forecasters' probability predictions (p) of a future binary event
    and b) the forecasters' common prior (p0) of the future event. In this R-package,
    the function sample_aggregator(p,p0,...) allows the user to calculate the revealed
    aggregator. Its use is illustrated with a simple example.