Synthetic Control Methods with Micro- And Meso-Level Data
A generalization of the 'Synth' package that is designed
for data at a more granular level (e.g., micro-level). Provides
functions to construct weights (including propensity score-type
weights) and run analyses for synthetic control methods with
micro- and meso-level data; see Robbins, Saunders, and Kilmer
(2017) <doi:10.1080/01621459.2016.1213634>.
boot, survey, MASS, stats, utils, kernlab, LowRankQP, nleqslv,
pracma
c(
person("Michael", "Robbins", , "mrobbins@rand.org", c("aut", "cre")),
person("Steven", "Davenport", , "sdavenpo@rand.org", "aut"))
Michael Robbins [aut, cre],
Steven Davenport [aut]

boot, survey, MASS, stats, utils, kernlab, LowRankQP, nleqslv,
pracma, parallel

c(person(given = "Michael",
family = "Robbins",
role = c("aut", "cre"),
email = "mrobbins@rand.org"),
person(given = "Steven",
family = "Davenport",
role = "aut",
email = "ssdport@gmail.com"))
A generalization of the 'Synth' package that is
designed for data at a more granular level (e.g., micro-level).
Provides functions to construct weights (including propensity
score-type weights) and run analyses for synthetic control methods
with micro- and meso-level data; see Robbins, Saunders, and Kilmer
(2017) <doi:10.1080/01621459.2016.1213634> and Robbins and Davenport
(2021) <doi:10.18637/jss.v097.i02>.
boot, survey, kernlab, LowRankQP, nleqslv, stats, parallel,
utils, pracma

Contains functions for converting existing HTML/JavaScript
source into equivalent 'shiny' functions. Bootstraps the process of making
new 'shiny' functions by allowing us to turn HTML snippets directly into
R functions.

Implementation of Bayesian drug-combination platform design. More and more immuno-oncology drug combinations make the traditional two-arm phase II trials inefficient, which stimulate the emerge of platform trials. In the case of multiple trial objectives such as candidates screening and subgroup analysis, we propose an information borrowing drug-combination Bayesian design for platform trials with subgroup exploration. MIDAS-2 consists of one control arm and several experimental agents. We use Bayesian spike and slab prior to identify factors that should be included in regression model and borrow information between combinations in the existence of subgroup interaction. Promising drug combinations are allowed to graduated early to move to next stage and new combination strategies can be added accordingly. Catch-up rule, curtail rule and early stopping rules are also applied to accelerate the trial process.