Title: Bayesian Latent Threshold Modeling
person("Fernando", "Tassinari", email = "fernandotm@al.insper.edu.br", role = "aut"),
person("Daniel", "Falbel", email = "dfalbel@gmail.com", role = "ctb"))
Description: Fits latent threshold model for simulated data
and describes how to adjust model using real data. Implements algorithm
This package has a function to generate data, a function to configure
priors and a function to fit the model. Examples may be checked inside

person("Ziyue", "Yang", , "zyang2k@gmail.com", role = c("aut", "cre"),
person("Tianshu", "Zhang", , "tzhang26@smith.edu", role = "aut",
Description: Facilitates the importation of the Boston Blue Bike trip data
since 2015. Functions include the computation of trip distances of
given trip data. It can also map the location of stations within a
given radius and calculate the distance to nearby stations. Data is
Imports: dplyr, janitor, leaflet, lubridate, magrittr, readr, sf,
Packaged: 2022-05-04 05:27:12 UTC; ellayoung
Author: Ziyue Yang [aut, cre] (<https://orcid.org/0000-0002-9299-8327>),

Title: Bayesian Model Averaging for Basket Trials
person("Matt", "Psioda", email = "matt_psioda@unc.edu", role = "cre"),
person("Ethan", "Alt", email = "ethanalt@live.unc.edu", role = "aut"))
Description: An implementation of the Bayesian model averaging method
of Psioda and others (2019) <doi:10.1093/biostatistics/kxz014> for basket trials.
Contains a user-friendly wrapper for simulating basket trials under conditions
and analyzing them with a Bayesian model averaging approach.

Title: Bayesian Multivariate Analysis of Summary Statistics
Description: Multivariate tool for analyzing genome-wide association
study results in the form of univariate summary statistics. The
goal of 'bmass' is to comprehensively test all possible multivariate
models given the phenotypes and datasets provided. Multivariate
models are determined by assigning each phenotype to being either
Unassociated (U), Directly associated (D) or Indirectly associated
(I) with the genetic variant of interest. Test results for each model
are presented in the form of Bayes factors, thereby allowing direct
comparisons between models. The underlying framework implemented
here is based on the modeling developed in "A Unified Framework
for Association Analysis with Multiple Related Phenotypes",