Description: Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. All data except for the Scottish lip cancer data relate to the set of 271 Intermediate Geographies (IG, but also known as Intermediate Zones (IZ))  that make up the 2001 definition of the  Greater Glasgow and Clyde health board.
Packaged: 2018-08-31 08:42:02 UTC; duncanlee

Packaged: 2020-08-24 15:44:10 UTC; duncanlee

Description: Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. Most of the data relate to the set of 271 Intermediate Zones (IZ)  that make up the 2001 definition of the  Greater Glasgow and Clyde health board.
Packaged: 2022-05-12 06:58:23 UTC; duncanlee

Title: Statistical package for population size estimation in
Description: The R package CARE1, the first part of the program CARE
http://chao.stat.nthu.edu.tw/softwareCE.html, can be used to
analyze epidemiological data via sample coverage approach (Chao
et al. 2001a). Based on the input of records from several
incomplete lists (or samples) of individuals, the R package
CARE1 provides output of population size estimate and related
statistics. The maximum number of lists is 5 for version 1.0.

Title: Ecological Quality Ratios Calculation and Plot
Author: Danilo Pecorino, Gina de la Fuente Mancebo, Xavier Torras
Description: Functions to calculate and plot ecological quality ratios (EQR) as specified by Ballesteros et al. 2007.

Title: Predicting Categorical and Continuous Outcomes Using Rule of Ten
Authors@R: c(person("Alina", "Bazarova", email = "a.bazarova@bham.ac.uk", role = c("aut", "cre")),person("Marko", "Raseta", email = "m.raseta@keele.ac.uk", role = c("aut")))
Description: Predicts categorical or continuous outcomes while concentrating on four key points. These are Cross-validation, Accuracy, Regression and Rule of Ten (CARRoT). It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying a rule of ten events per variable are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Recever Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> and Rhemtulla et al. (2012) <doi:10.1037/a0029315>.