Title: Predict the Race and Gender of a Given Name Using Census and
Description: Predicts the most common race of a surname and based on U.S. Census
data, and the most common first named based on U.S. Social Security Administration data.

Title: Assessing Performance of Prediction Models for Predicting
Description: Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

Authors@R: person("Max", "Menssen", email = "menssen@cell.uni-hannover.de",
Description: An implementation of prediction intervals for overdispersed count data,
for overdispersed binomial data and for linear random effects models.

Packaged: 2022-04-29 09:16:09 UTC; maxmenssen

Packaged: 2022-06-24 12:37:28 UTC; maxmenssen

Description: Implements the predictive k-means method for clustering observations, using a mixture of experts model to allow covariates to influence cluster centers. Motivated by air pollution epidemiology settings, where cluster membership needs to be predicted across space. Includes functions for predicting cluster membership using spatial splines and principal component analysis (PCA) scores using either multinomial logistic regression or support vector machines (SVMs). For method details see Keller et al. (2017) <doi:10.1214/16-AOAS992>.
Imports: Rcpp (>= 0.11.5), maxLik, e1071, mgcv

Title: Classification rule based on Bayesian mixture models with
Description: "train_predict_mix" predicts the binary response with

Description: "train_predict_mix" predicts the binary response with binary features
Packaged: Fri Nov  9 17:07:11 2007; longhai

person("Amin", "Adibi", email = "adibi@alumni.ubc.ca", role = c("cre"),
person("Abdollah", "Safari", email = "asafari@mail.ubc.ca", role = c("aut"))
Description: Provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.
Imports: Rcpp, pROC, stats, graphics, RConics, ggplot2, dplyr, magrittr
Author: Mohsen Sadatsafavi [aut, cph] (<https://orcid.org/0000-0002-0419-7862>),