A C++ library for Bayesian modeling, with an emphasis on Markov
   chain Monte Carlo.  Although boom contains a few R utilities (mainly plotting
   functions), its primary purpose is to install the BOOM C++ library on your
   system so that other packages can link against it.

Spike and slab regression with a variety of residual error
  distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a
  few others.  Spike and slab regression is Bayesian regression with prior
  distributions containing a point mass at zero.  The posterior updates the
  amount of mass on this point, leading to a posterior distribution that is
  actually sparse, in the sense that if you sample from it many coefficients are
  actually zeros.  Sampling from this posterior distribution is an elegant way
  to handle Bayesian variable selection and model averaging.  See
  <DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.

Includes functions to estimate production frontiers 
    and make ideal output predictions in the Data Envelopment Analysis (DEA) 
    context using both standard models from DEA and Free Disposal Hull (FDH)
    and boosting techniques. In particular, EATBoosting (Guillen et al., 2023 
    <doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package 
    includes code for estimating several technical efficiency measures using 
    different models such as the input and output-oriented radial measures, the
    input and output-oriented Russell measures, the Directional Distance 
    Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based 
    Measure (SBM).

'R' bindings for the various functions and statistical distributions
    provided by the 'Boost' Math library <https://www.boost.org/doc/libs/latest/libs/math/doc/html/index.html>.

Jointly models the multivariate longitudinal responses and multiple covariates and time using gradient boosting approach.

Implements Friedman's gradient descent boosting algorithm for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal.  A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter. Although the package is design for longitudinal data, it can handle cross-sectional data as well. Implementation details are provided in Pande et al. (2017), Mach Learn <DOI:10.1007/s10994-016-5597-1>.