Adaptive Gauss Hermite Quadrature for Bayesian Inference
Adaptive Gauss Hermite Quadrature for Bayesian inference.
The AGHQ method for normalizing posterior distributions
and making Bayesian inferences based on them. Functions are provided for doing
quadrature and marginal Laplace approximations, and summary methods are provided
for making inferences based on the results.
See Stringer (2021). "Implementing Adaptive Quadrature for Bayesian Inference:
the aghq Package" <arXiv:2101.04468>.
methods, mvQuad, matrixStats, Matrix, stringr, magrittr,
dplyr, tidyr, tidyselect, rlang, polynom, tibble, purrr
trustOptim, trust, numDeriv, testthat (>= 2.1.0), knitr,
rmarkdown, ggplot2

trustOptim, trust, numDeriv, testthat (>= 2.1.0), knitr,
rmarkdown

methods, mvQuad, Matrix, rlang, polynom, splines, numDeriv
trustOptim, trust, testthat (>= 2.1.0), parallel

Extracting and preprocessing Agilent express arrays
The current version contains a single function, AAProcess.
AAProcess extracts the raw median signals from green and red
channels of Agilent scanner array files. It replaces values for
replicate probes by the mean of all replicates, and
(optionally) removes control probes It log2 transforms data ,
and outputs txt files containing the red and green channel data
separately. It then normalises each set of data against a
baseline file (typically median or mean of a large set of
arrays, using LOESS regression. Finally outputs the normalised
data. The normalisation step can be optionally omitted.

c(
person("Kenji", "Kondo", role=c("aut", "cre", "cph"), email="kkondo.odnokk@gmail.com"),
person("Kazuhisa", "Takahashi", role=c("ctb")),
person("Hikari", "Banno", role=c("ctb"))
)
Provides functions to fit Accurate Generalized Linear Model (AGLM) models, visualize them, and predict for new data. AGLM is defined as a regularized GLM which applies a sort of feature transformations using a discretization of numerical features and specific coding methodologies of dummy variables. For more information on AGLM, see Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa (2020) <https://www.institutdesactuaires.com/global/gene/link.php?doc_id=16273&fg=1>.
glmnet (>= 4.0.2), assertthat, methods, mathjaxr
testthat, knitr, rmarkdown, MASS, faraway
Kenji Kondo [aut, cre, cph],
Kazuhisa Takahashi [ctb],
Hikari Banno [ctb]