Provides a generic infrastructure for creating and using registries.

Provides a generic infrastructure for creating and using
registries.

Regularized logistic regression by Gibbs sampling. The
package implements subtly different MCMC schemes with varying
efficiency depending on the data type (binary v. binomial, say)
and the desired estimator (regularized maximum likelihood, or
Bayesian maximum a posteriori/posterior mean, etc.) through a
unified interface

Regularized logistic regression
by Gibbs sampling. The package implements subtly different
MCMC schemes with varying efficiency depending on the data type
(binary v. binomial, say) and the desired estimator (regularized maximum
likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a
unified interface

R (>= 2.14.0), methods, mvtnorm, boot, Matrix
Regularized (polychotomous) logistic regression
by Gibbs sampling. The package implements subtly different
MCMC schemes with varying efficiency depending on the data type
(binary v. binomial, say) and the desired estimator (regularized maximum
likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a
unified interface

Regularized (polychotomous) logistic regression
by Gibbs sampling. The package implements subtly different
MCMC schemes with varying efficiency depending on the data type
(binary v. binomial, say) and the desired estimator (regularized maximum
likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a
unified interface.

Regularized (polychotomous) logistic regression by Gibbs
sampling. The package implements subtly different MCMC schemes
with varying efficiency depending on the data type (binary v.
binomial, say) and the desired estimator (regularized maximum
likelihood, or Bayesian maximum a posteriori/posterior mean,
etc.) through a unified interface

c(
person("Jason", "Sinnwell", email = "sinnwell.jason@mayo.edu", comment=c(ORCID="0000-0003-1964-5522"), role = c("aut","cre"),),
person("Daniel", "Schaid", email = "schaid.daniel@mayo.edu", comment=c(ORCID="0000-0003-1457-6433"), role = c("aut"),),
person("Gregory", "Jenkins", email="jenkins.gregory@mayo.edu", comment=c(ORCID="0000-0001-6408-6227"), role=c("ctb"),))
Mediation analysis for multiple mediators by penalized structural equation models using sparse group lasso. The penalty considers the natural groupings of parameters that determine mediation, as well as encourages sparseness of the model parameters.