Tools to find bad commits with git bisect. See
        https://github.com/wch/bisectr for examples and test script
        templates.

Enables the user to infer potential synthetic lethal relationships
    by analysing relationships between bimodally distributed gene pairs in big
    gene expression datasets.  Enables the user to visualise these candidate
    synthetic lethal relationships.

A collection of functions to test spatial autocorrelation between variables, including Moran I, Geary C and Getis G together with scatter plots, functions for mapping and identifying clusters and outliers, functions associated with the moments of the previous statistics that will allow testing whether there is bivariate spatial autocorrelation, and a function that allows identifying (visualizing neighbours) on the map, the neighbors of any region once the scheme of the spatial weights matrix has been established.

Implementation of the 'bisque' strategy for approximate Bayesian posterior inference.  See Hewitt and Hoeting (2019) <arXiv:1904.07270> for complete details.  'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models.  The resulting approximations are computationally efficient for many hierarchical Bayesian models.  The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation.

Provides tools to accurately estimate cell type abundances 
    from heterogeneous bulk expression. A reference-based method utilizes
    single-cell information to generate a signature matrix and transformation
    of bulk expression for accurate regression based estimates. A marker-based
    method utilizes known cell-specific marker genes to measure relative
    abundances across samples.
    For more details, see Jew and Alvarez et al (2019) <doi:10.1101/669911>.

Bisulfite-treated RNA non-conversion in a set of samples is
  analysed as follows : each sample's non-conversion distribution is
  identified to a Poisson distribution. P-values adjusted for multiple
  testing are calculated in each sample. Combined non-conversion P-values
  and standard errors are calculated on the intersection of the set of
  samples. For further details, see C Legrand, F Tuorto, M Hartmann, 
  R Liebers, D Jakob, M Helm and F Lyko (2017) <doi:10.1101/gr.210666.116>.