Transforms focal observations' data, where different types of social interactions
  can be recorded by multiple observers, into asymmetric data matrices.
  Each cell in these matrices provides counts on the number of times
  a specific type of social interaction was initiated by the row subject and
  directed to the column subject.

Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.

Estimate population average treatment effects from a primary data source 
  with borrowing from supplemental sources. Causal estimation is done with either a 
  Bayesian linear model or with Bayesian additive regression trees (BART) to adjust 
  for confounding. Borrowing is done with multisource exchangeability models (MEMs). For 
  information on BART, see Chipman, George, & McCulloch (2010) <doi:10.1214/09-AOAS285>. 
  For information on MEMs, see Kaizer, Koopmeiners, & 
  Hobbs (2018) <doi:10.1093/biostatistics/kxx031>.

An all relevant feature selection wrapper algorithm.
 It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).

Classical Boson Sampling using the algorithm of
 Clifford and Clifford (2017) <arXiv:1706.01260>. Also provides functions for
 generating random unitary matrices, evaluation of matrix permanents (both
 real and complex) and evaluation of complex permanent minors.

Reads and plots phylogenetic placements.

The Biomarker Optimal Segmentation System R package, 'bossR', is designed for precision medicine, helping to identify individual traits using biomarkers. It focuses on determining the most effective cutoff value for a continuous biomarker, which is crucial for categorizing patients into two groups with distinctly different clinical outcomes. The package simultaneously finds the optimal cutoff from given candidate values and tests its significance. Simulation studies demonstrate that 'bossR' offers statistical power and false positive control non-inferior to the permutation approach (considered the gold standard in this field), while being hundreds of times faster.