Algorithms for automatically finding appropriate
thresholds for numerical data, with special functions for thresholding
images. Provides the 'ImageJ' 'Auto Threshold' plugin functionality to
R users. See <https://imagej.net/plugins/auto-threshold> and Landini et al.
(2017) <DOI:10.1111/jmi.12474>.
covr, dplyr, ggplot2, knitr, rmarkdown, spelling, styler (>=
1.3.2), testthat (>= 3.0), utils

testthat (>= 2.1.0), covr, knitr, rmarkdown, utils, ggplot2,
dplyr, spelling

Algorithms for automatically finding appropriate
thresholds for numerical data, with special functions for thresholding
images. Provides the 'ImageJ' 'Auto Threshold' plugin functionality to
R users. See <http://imagej.net/Auto_Threshold> and Landini et al.
(2017) <DOI:10.1111/jmi.12474>.
covr, dplyr, ggplot2, knitr, rmarkdown, spelling, styler (>=
1.3.2), testthat (>= 2.1.0), utils

Automated Vector Autoregression Models and Networks
c(person("Ando","Emerencia",role = c("aut","cre"),
email = "ando.emerencia@gmail.com"))
Automatically find the best vector autoregression
models and networks for a given time series data set. 'AutovarCore'
evaluates eight kinds of models: models with and without log
transforming the data, lag 1 and lag 2 models, and models with and
without day dummy variables. For each of these 8 model configurations,
'AutovarCore' evaluates all possible combinations for including
outlier dummies (at 2.5x the standard deviation of the residuals)
and retains the best model. Model evaluation includes the Eigenvalue
stability test and a configurable set of residual tests. These eight
models are further reduced to four models because 'AutovarCore'
determines whether adding day dummies improves the model fit.
Rcpp (>= 0.11.4), Amelia, jsonlite, parallel, stats, urca,
vars

Automatically find the best vector autoregression
models and networks for a given time series data set. 'AutovarCore'
evaluates eight kinds of models: models with and without log
transforming the data, lag 1 and lag 2 models, and models with and
without weekday dummy variables. For each of these 8 model configurations,
'AutovarCore' evaluates all possible combinations for including
outlier dummies (at 2.5x the standard deviation of the residuals)
and retains the best model. Model evaluation includes the Eigenvalue
stability test and a configurable set of residual tests. These eight
models are further reduced to four models because 'AutovarCore'
determines whether adding weekday dummies improves the model fit.