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).

A novel feature selection algorithm for linear regression called 
    BOSO (Bilevel Optimization Selector Operator). The main contribution is the 
    use a bilevel optimization problem to select the variables in the training 
    problem that minimize the error in the validation set. Preprint available: 
    [Valcarcel, L. V., San Jose-Eneriz, E., Cendoya, X., Rubio, A., Agirre, X., 
    Prosper, F., & Planes, F. J. (2020). "BOSO: a novel feature selection 
    algorithm for linear regression with high-dimensional data." bioRxiv. 
    <doi:10.1101/2020.11.18.388579>]. 
    In order to run the vignette, it is recommended to install the 'bestsubset' package,
    using the following command: devtools::install_github(repo="ryantibs/best-subset", subdir="bestsubset").
    If you do not have gurobi, run devtools::install_github(repo="lvalcarcel/best-subset", subdir="bestsubset").
    Moreover, to install cplexAPI you can check <https://github.com/lvalcarcel/cplexAPI>.