Packaged: 2021-03-28 09:58:21 UTC; Chainarong

Title: Multi-Resolution Scanning for Cross-Sample Differences
Description: An implementation of the MRS algorithm for comparison across distributions.
The model is based on a nonparametric process taking the form of a Markov model
that transitions between a "null" and a "alternative" state
on a multi-resolution partition tree of the sample space.
MRS effectively detects and characterizes a variety of underlying differences.
These differences can be visualized using several plotting functions.
Imports: Rcpp (>= 0.11.0), igraph, optimx

that transitions between a "null" and an "alternative" state

Description: An implementation of the MRS algorithm for comparison across distributions,
as described in Jacopo Soriano, Li Ma (2016) <doi:10.1111/rssb.12180>.

Title: Multinomial Response Models with Structured Penalties
Description: Fits regularized multinomial response models using penalized loglikelihood methods with structured penalty terms.
Depends: methods, parallel, compiler, matrixcalc, Formula, R (>=
MRSP-family-23.r MRSP-fit-39.r MRSP-formula-2.r MRSP-3.r zzz.r
Packaged: 2014-12-09 14:08:16 UTC; poessnecker

Title: A Sample Size Calculator for Micro-Randomized Trials
person("Nicholas J.","Seewald", email = "nseewald@umich.edu", role = "aut"),
person("Peng","Liao", email = "pengliao@umich.edu", role ="aut"),
person("Ji","Sun", email = "sunji@umich.edu", role ="aut"))
Description: Provide a sample size calculator for micro-randomized
trials (MRTs) based on methodology developed in Sample Size Calculations for
Micro-randomized Trials in mHealth by Liao et al. (2016) <DOI:10.1002/sim.6847>.

person("Peng","Liao", email = "pengliao@umich.edu", role =c("aut","cre")),
person("Liying","Huang", email = "lxh37@psu.edu", role = "aut"),
Copyright: The Pennsylvania State University (07.25.2020 - 09.01.2020),
Harvard University (09.01.2020 - present)
Description: Provide a sample size calculator for micro-randomized trials (MRTs) based on methodology developed in Sample Size Calculations for Micro-randomized Trials in mHealth by Liao et al. (2016) <DOI:10.1002/sim.6847>.