person("Bryon", "Aragam", email = "sparsebn@gmail.com", role = c("aut", "cre")),
person("Dacheng", "Zhang", role = c("aut"))
Imports: sparsebnUtils (>= 0.0.2), Rcpp (>= 0.11.0)

Imports: sparsebnUtils (>= 0.0.4), Rcpp (>= 0.11.0), stats, utils

Imports: sparsebnUtils (>= 0.0.5), Rcpp (>= 0.11.0), stats, utils

Suggests: testthat, graph, igraph, Matrix

Description: Implementation of the CCDr (Concave penalized Coordinate Descent with reparametrization) structure learning algorithm as described in Aragam and Zhou (2015) <https://www.jmlr.org/papers/v16/aragam15a.html>. This is a fast, score-based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.

Title: Combinatorially Complex Equilibrium Model Selection
Description: Dissociation constants of quasi-equilibriums of enzymes and protein-ligand binding equilibriums in general are systematically
scanned though possibilities of being infinity and/or equal to others. The automatically generated space of models is then fitted to data.
Packaged: Thu Mar  5 17:49:48 2009; radivot

Description: Dissociation constants of quasi-equilibriums of enzymes
and protein-ligand binding equilibriums in general are
systematically scanned though possibilities of being infinity
and/or equal to others. The automatically generated space of
Packaged: Sat Mar 14 05:59:57 2009; radivot

Packaged: Fri Mar 20 20:13:13 2009; radivot

Authors@R: c(person("Weige", "Huang", role=c("aut","cre"), email="weige.huang@temple.edu"), person("Brantly", "Callaway", role="aut", email="brantly.callaway@temple.edu"))
Description: Contains methods for computing counterfactuals with a continuous treatment variable as in Callaway and Huang (2017) <https://ssrn.com/abstract=3078187>.  In particular, the package can be used to calculate the expected value, the variance, the interquantile range, the fraction of observations below or above a particular cutoff, or other user-supplied functions of an outcome of interest conditional on a continuous treatment.  The package can also be used for computing these same functionals after adjusting for differences in covariates at different values of the treatment.  Further, one can use the package to conduct uniform inference for each parameter of interest across all values of the treatment, uniformly test whether adjusting for covariates makes a difference at any value of the treatment, and test whether a parameter of interest is different from its average value at an value of the treatment.
Imports: quantreg, stats, pbapply, BMisc, tidyr, ggplot2,