Packaged: 2022-11-08 15:50:31 UTC; jakehughey

Packaged: 2022-11-24 03:30:47 UTC; jakehughey

Packaged: 2022-02-16 19:49:09 UTC; joshuaschoenbachler

Authors@R: person("Michal", "Oleszak", email = "oleszak.michal@gmail.com", role = c("aut", "cre"))
Description: Dynamic panel modelling framework based on an empirical-Bayes approach.
Contains tools for computing point forecasts and bootstrapping prediction intervals.
Imports: plm, pracma, MASS, Matrix, minqa, dplyr, data.table, moments,
magrittr, ggplot2, stats, utils, graphics

Title: Probability Models for Ranking Data
Maintainer: Paul H. Lee <honglee@graduate.hku.hk>
Description: Fitting probability models for ranking data. Current, only
complete rankings are supported by this package.

Description: Fitting probability models for ranking data. Current, only complete rankings are supported by this package.

Description: Fitting probability models for ranking data. Currently,
only complete rankings are supported by this package.

Description: Descriptive statistics (mean rank, pairwise frequencies,
and marginal matrix), Analytic Hierarchy Process models (with
Saaty's and Koczkodaj's inconsistencies), probability models
(Luce models, distance-based models, and rank-ordered logit
models) and visualization with multidimensional preference
analysis for ranking data are provided. Current, only complete

Description: Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.

Title: Calculates the Minimum Sample Size Required for Developing a
Authors@R: c(person("Joie", "Ensor", role = c("aut", "cre"), email = "j.ensor@keele.ac.uk"),
person("Emma C.", "Martin", role = "aut", email = "emma.martin@leicester.ac.uk"),
person("Richard D.", "Riley", role = "aut", email = "r.riley@keele.ac.uk"))
Description: Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.