Imports: RSAGA, magrittr, Rcpp, checkmate, ijtiff

Imports: RSAGA, magrittr, Rcpp, checkmate, ijtiff (>= 0.3.0)

Imports: magrittr, Rcpp, checkmate, ijtiff (>= 1.0.0), filesstrings (>=

Imports: magrittr, Rcpp, checkmate (>= 1.8.5), ijtiff (>= 1.1.0),
Author: Rory Nolan [aut, cre, trl] (<https://orcid.org/0000-0002-5239-4043>),
Gabriel Landini [ctb, cph] (<https://orcid.org/0000-0002-9689-0989>)

Description: 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)
Imports: magrittr, Rcpp, checkmate (>= 1.8.5), ijtiff (>= 1.5.0),
filesstrings (>= 3.0.0), glue, purrr, stats, stringr, rlang
Suggests: testthat, covr, knitr, rmarkdown, utils, ggplot2, dplyr,

Imports: stats, magrittr (>= 1.5), Rcpp (>= 1.0.1), checkmate (>=

Description: 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/Auto_Threshold> and Landini et al.
Imports: checkmate (>= 1.9.3), ijtiff (>= 2.2), magrittr (>= 1.5),
Suggests: covr, dplyr, ggplot2, knitr, rmarkdown, spelling, styler (>=

R users. See <https://imagej.net/plugins/auto-threshold> and Landini et al.
Packaged: 2021-11-18 17:54:05 UTC; rorynolan

Suggests: testthat (>= 2.1.0), covr, knitr, rmarkdown, utils, ggplot2,

R users. See <http://imagej.net/Auto_Threshold> and Landini et al.
Imports: checkmate (>= 1.9.3), filesstrings (>= 3.1.5), ijtiff (>=

Title: Automated Vector Autoregression Models and Networks
Description: 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.
Imports: Rcpp (>= 0.11.4), Amelia, jsonlite, parallel, stats, urca,