Title: Stepwise Regression with Assumptions Checking
person("Klairung", "Samart", role = c("aut")))
Description: The stepwise regression with assumptions checking and the possible Box-Cox transformation.
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), spelling

Title: Efficient Estimation of Bayesian SBMs & MLSBMs
Description: Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs).
Packaged: 2021-02-01 21:22:53 UTC; carterallen

Title: Use the MLS Junk Generator Algorithm to Generate a Stream of
Authors@R: person("Steve", "Myles", email = "steve@mylesandmyles.info", role = c("aut", "cre"))
Description: Generate a stream of pseudo-random numbers generated using the MLS
Junk Generator algorithm. Functions exist to generate single pseudo-random
numbers as well as a vector, data frame, or matrix of pseudo-random numbers.
Packaged: 2015-08-16 17:52:09 UTC; John Henry

Packaged: 2015-09-07 18:26:04 UTC; John Henry

Description: Likelihood-based estimation of conditional transformation
models via the most likely transformation approach.
Depends: basefun (>= 0.0-30), variables (>= 0.0-30)
Imports: BB, stats, quadprog, graphics, methods, grDevices, sandwich,
Suggests: MASS, nnet, TH.data, survival, multcomp

Suggests: MASS, nnet, TH.data, survival, multcomp, alabama

models via the most likely transformation approach described in
Depends: basefun (>= 0.0-35), variables (>= 0.0-30)

Depends: basefun (>= 0.0-37), variables (>= 0.0-30)

Imports: BB, alabama, stats, quadprog, graphics, methods, grDevices,

Depends: basefun (>= 0.0-39), variables (>= 0.0-30)
Imports: BB, alabama, stats, coneproj, graphics, methods, grDevices,

Depends: basefun (>= 1.0-5), variables (>= 1.0-2)

Depends: basefun (>= 1.1-0), variables (>= 1.1-0)
sandwich, numDeriv, survival, nloptr, Matrix