Title: Median Bias Reduction in Binomial-Response GLMs
Author: Euloge Clovis Kenne Pagui, Nicola Sartori  and Alessandra Salvan
Maintainer: Euloge C. Kenne Pagui <kenne@stat.unipd.it>
Description: Fit generalized linear models with binomial responses using  a median modified score approach (Kenne Pagui et al., 2016, <https://arxiv.org/abs/1604.04768>) to median bias reduction. This method respects equivariance under reparameterizations for each parameter component and also solves the infinite estimates problem (data separation).
Depends: R (>= 2.6.0), nleqslv, enrichwith
Packaged: 2017-02-05 07:42:35 UTC; nicolasartori

Title: Multivariate Bayesian Structural Time Series
Description: Multivariate time series regression using dynamic linear models fit by MCMC. See Qiu, Jammalamadaka and Ning (2018) <http://www.jmlr.org/papers/volume19/18-009/18-009.pdf>.
Depends: pscl, MASS, KFAS, MCMCpack, Matrix, R(>= 3.5)

Author: Jinwen Qiu <qjwsnow_ctw@hotmail.com>, Ning Ning <patricianing@gmail.com>
Description: Tools for data analysis with multivariate Bayesian structural time series (MBSTS) models.  Specifically, the package provides facilities for implementing general structural time series models, flexibly adding on different time series components (trend, season, cycle, and regression), simulating them, fitting them to multivariate correlated time series data, conducting feature selection on the regression component.
Imports: KFAS, Matrix, pscl, MCMCpack, MASS, ggplot2, reshape2, BBmisc,

Title: A Monte Carlo Based Heterogeneity Test for Meta-Analysis
Author: Han Du <hdu@psych.ucla.edu>, Zijun Ke <keziyun@mail.sysu.edu.cn>, Ge Jiang <gejiang2@illinois.edu>
Description: Implements a Monte Carlo Based Heterogeneity Test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (OR) in Meta-Analysis Studies. Depending on the presence of moderators, this Monte Carlo Based Test can be implemented in the random or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihood, so installation of R package 'metafor' is required.
Suggests: base, HSAUR2, roxygen2, knitr, rmarkdown