|
a |
|
b/man/hsstan-package.Rd |
|
|
1 |
% Generated by roxygen2: do not edit by hand |
|
|
2 |
% Please edit documentation in R/hsstan-package.R |
|
|
3 |
\docType{package} |
|
|
4 |
\name{hsstan-package} |
|
|
5 |
\alias{hsstan-package} |
|
|
6 |
\title{Hierarchical shrinkage Stan models for biomarker selection} |
|
|
7 |
\description{ |
|
|
8 |
The \strong{hsstan} package provides linear and logistic regression models |
|
|
9 |
penalized with hierarchical shrinkage priors for selection of biomarkers. |
|
|
10 |
Models are fitted with Stan (Carpenter et al. (2017)), which allows to |
|
|
11 |
perform full Bayesian inference. |
|
|
12 |
} |
|
|
13 |
\details{ |
|
|
14 |
The package implements the horseshoe and regularized horseshoe priors |
|
|
15 |
(Piironen and Vehtari (2017)), and the projection predictive selection |
|
|
16 |
approach to recover a sparse set of predictive biomarkers (Piironen, |
|
|
17 |
Paasiniemi and Vehtari (2020)). |
|
|
18 |
|
|
|
19 |
The approach is particularly suited to selection from high-dimensional |
|
|
20 |
panels of biomarkers, such as those that can be measured by MSMS or similar |
|
|
21 |
technologies (Colombo, Valo, McGurnaghan et al. (2019), Colombo, McGurnaghan, |
|
|
22 |
Blackbourn et al. (2020)). |
|
|
23 |
} |
|
|
24 |
\references{ |
|
|
25 |
B. Carpenter et al. (2017), |
|
|
26 |
Stan: a probabilistic programming language, |
|
|
27 |
\emph{Journal of Statistical Software}, 76 (1). |
|
|
28 |
\doi{10.18637/jss.v076.i01} |
|
|
29 |
|
|
|
30 |
J. Piironen and A. Vehtari (2017), |
|
|
31 |
Sparsity information and regularization in the horseshoe and other shrinkage |
|
|
32 |
priors, \emph{Electronic Journal of Statistics}, 11 (2), 5018-5051. |
|
|
33 |
\doi{10.1214/17-EJS1337SI} |
|
|
34 |
|
|
|
35 |
J. Piironen, M. Paasiniemi and A. Vehtari (2020), |
|
|
36 |
Projective inference in high-dimensional problems: prediction and feature |
|
|
37 |
selection, \emph{Electronic Journal of Statistics}, 14 (1), 2155-2197. |
|
|
38 |
\doi{10.1214/20-EJS1711} |
|
|
39 |
|
|
|
40 |
M. Colombo, E. Valo, S.J. McGurnaghan et al. (2019), |
|
|
41 |
Biomarkers associated with progression of renal disease in type 1 diabetes, |
|
|
42 |
\emph{Diabetologia}, 62 (9), 1616-1627. |
|
|
43 |
\doi{10.1007/s00125-019-4915-0} |
|
|
44 |
|
|
|
45 |
M. Colombo, S.J. McGurnaghan, L.A.K. Blackbourn et al. (2020), |
|
|
46 |
Comparison of serum and urinary biomarker panels with albumin creatinin |
|
|
47 |
ratio in the prediction of renal function decline in type 1 diabetes, |
|
|
48 |
\emph{Diabetologia}, 63 (4), 788-798. |
|
|
49 |
\doi{10.1007/s00125-019-05081-8} |
|
|
50 |
|
|
|
51 |
M. Colombo, A. Asadi Shehni, I. Thoma et al. (2021), |
|
|
52 |
Quantitative levels of serum N-glycans in type 1 diabetes and their |
|
|
53 |
association with kidney disease, |
|
|
54 |
\emph{Glycobiology}, 31 (5), 613-623. |
|
|
55 |
\doi{10.1093/glycob/cwaa106} |
|
|
56 |
} |