Fully Bayesian Analysis of Differential Expression in RNA
Sequencing Data
This package lets you estimate the posterior distribution
of various parameters regarding RNA Sequencing data. The most
interesting parameter is the probability of differential
expression (DE) between two groups A and B. But also estimates
for the log mean and the log dispersion parameter of the
underlying poisson - log-normal model can be returned.

Bayesian Average Error approach to Sample Size Determination
Implements sample size calculations following the approach
described in "Bayesian Average Error Based Approach to
Hypothesis Testing and Sample Size Determination."

A Binary Regression Adaptive Goodness-of-Fit Test (BAGofT)
c(person("Jiawei", "Zhang", email = "zhan4362@umn.edu", role = c("aut", "cre")), person("Jie", "Ding", email = "dingj@umn.edu", role = "aut"), person("Yuhong", "Yang", email = "yangx374@umn.edu", role = "aut"))
Performs goodness-of-fit test for binary regression models with at least 1 continuous covariate. The implemented method BAGofT is from Zhang, Ding and Yang (2019) <arXiv:1911.03063>.
Jiawei Zhang [aut, cre],
Jie Ding [aut],
Yuhong Yang [aut]

The BAGofT assesses the goodness-of-fit of binary classifiers. Details can be found in Zhang, Ding and Yang (2021) <arXiv:1911.03063v2>.

Facilitates the evolutionary analysis and structure conservation study of specified amino acids in proteins.

seqinr, Rpdb, scales, stats, base, dplyr, Biostrings, readr

c(
person("Michal", "Stolarczyk", , "stolarczyk.michal93@gmail.com", role = c("aut","cre")
),person("Alicja", "Pluciennik", , "alicja.pluciennik@outlook.com", role = c("aut")
))
seqinr, Rpdb, scales, stats, base, dplyr, Biostrings, readr,
progress
Michal Stolarczyk [aut, cre],
Alicja Pluciennik [aut]

Can Wang, Christopher W. Laws, Frank A. Schmid
Bayesian analysis of loss development on insurance triangles or 'BALD' is a Bayesian model of developing aggregate loss triangles in property casualty insurance. This actuarial model makes use of a heteroskedastic and skewed t-likelihood with endogenous degrees of freedom, employs model averaging by means of Reversible Jump MCMC, and accommodates a structural break in the path of the consumption of benefits. Further, the model is capable of incorporating expert information in the calendar year effect. In an accompanying vignette, this model is applied to two widely studied General Liability and Auto Bodily Injury Liability loss triangles. For a description of the methodology, see Frank A. Schmid (2010) <doi:10.2139/ssrn.1501706>.
R (>= 3.3.0), methods, rjags (>= 3-3), logspline, utils,
lattice
'zzz.R' 'LossDevModelInput.R' 'AnnualAggLossDevModelInput.R'
'LossDevModelOutput.R' 'NodeOutput.R'
'AnnualAggLossDevModelOutput.R'
'BreakAnnualAggLossDevModelOutput.R'
'StandardAnnualAggLossDevModelOutput.R'
'AnnualAggLossDevModelOutputWithZeros.R' 'BALD.R'
'BreakAnnualAggLossDevModelInput.R' 'DocumentationOfData.R'
'StandardAnnualAggLossDevModelInput.R' 'util.R'