Multi-Resolution Scanning for Cross-Sample Differences
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.

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 an "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.

An implementation of the MRS algorithm for comparison across distributions,
as described in Jacopo Soriano, Li Ma (2016) <doi:10.1111/rssb.12180>.
The model is based on a nonparametric process taking the form of a Markov model
that transitions between a "null" and an "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.

Multinomial Response Models with Structured Penalties
Fits regularized multinomial response models using penalized loglikelihood methods with structured penalty terms.
methods, parallel, compiler, matrixcalc, Formula, R (>=
2.14.1)
MRSP-prespecifier.r MRSP-classes-7.r FISTA-classes-3.r
FISTA-generics-2.r FISTA-27.r MRSP-methods-15.r
MRSP-cfunctions-2.r MRSP-helpers-21.r MRSP-fista-7.r
MRSP-family-23.r MRSP-fit-39.r MRSP-formula-2.r MRSP-3.r zzz.r

A Sample Size Calculator for Micro-Randomized Trials
c(
person("Liying","Huang", email = "lxh37@psu.edu", role = c("aut","cre")),
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"))
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>.
Liying Huang [aut, cre],
Nicholas J. Seewald [aut],
Peng Liao [aut],
Ji Sun [aut]