Spike and slab regression a la McCulloch and George (1997).

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.

Boosting for Multivariate Longitudinal Response
Jointly models the multivariate longitudinal response and multiple covariates and time using gradient boosting approach.

Boosting for Multivariate Longitudinal Responses
Jointly models the multivariate longitudinal responses and multiple covariates and time using gradient boosting approach.

Bootstrapping test for chromosomal localization

Bootstrap-MRMR Technique for Informative Gene Selection
Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).

Bootstrap Prediction Intervals and Bias-Corrected Forecasting
Bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series

Bias-Corrected Forecasting and Bootstrap Prediction
Intervals for Autoregressive Time Series

Contains functions for bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series.

Adjusting for Optimism in 'glmnet' Regression using
Bootstrapping
Antonio Jose Canada Martinez <ancamar2@gmail.com>
Main objective of a predictive model is to provide accurated predictions of a new observations.
Unfortunately we don't know how well the model performs. In addition, at the current era of omic
data where p >> n, is not reasonable applying internal validation using data-splitting. Under this
background a good method to assessing model performance is applying internal bootstrap validation
(Harrell Jr, Frank E (2015) <doi:10.1007/978-1-4757-3462-1>.) This package provides bootstrap validation
for the linear and logistic 'glmnet' models.