Title: Time Series Forecasting using ARIMA-ANN Hybrid Model
Authors@R: c(person("Ramasubramanian ", "V.", role = c("aut", "ctb")),
person("Mrinmoy", "Ray", role = c("aut", "cre"),
Depends: R (>= 2.3.1), stats,forecast, tseries
Description: Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) <doi:10.1016/S0925-2312(01)00702-0>.

Author: Livio Finos, Jelle Goeman, Wouter Weeda, Jonathan Rosenblatt, Aldo Solari
Description: It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature,
it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis.
The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) <doi:10.1016/j.neuroimage.2018.07.060>.

Title: Covariate-Adjusted Receiver Operating Characteristic Curve
person(given = "Maria Xose", family = "Rodriguez-Alvarez",
email = "mxrodriguez@bcamath.org", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1329-9238")),
person(given = "Vanda", family = "Inacio de Carvalho", email = "Vanda.Inacio@ed.ac.uk", role = c("aut"), comment = c(ORCID = "0000-0001-8084-1616")))
Imports: stats, grDevices, graphics, splines, np, Matrix, Hmisc, MASS,
Description: Estimates the covariate-adjusted Receiver Operating Characteristic (AROC) curve and pooled (unadjusted) ROC curve by different methods. Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018) <arXiv:1806.00473>.
Packaged: 2019-11-15 09:54:36 UTC; mrodriguez
Author: Maria Xose Rodriguez-Alvarez [aut, cre]
Maintainer: Maria Xose Rodriguez-Alvarez <mxrodriguez@bcamath.org>

Packaged: 2019-12-05 17:12:27 UTC; mrodriguez

person(given = "Vanda", family = "Inacio", email = "Vanda.Inacio@ed.ac.uk", role = c("aut"), comment = c(ORCID = "0000-0001-8084-1616")))
moments, nor1mix, spatstat.geom, spatstat (>= 2.0-0)
Description: Estimates the covariate-adjusted Receiver Operating Characteristic (AROC) curve and pooled (unadjusted) ROC curve by different methods. Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018) <arXiv:1806.00473>. NOTE: We have created a new package, ROCnReg, with more functionalities. It also implements all the methods included in AROC. We, therefore, recommend using ROCnReg (AROC will no longer be maintained).
Packaged: 2021-03-17 16:20:03 UTC; mrodriguez