Title: Generic Functions for Cross Validation
Description: This package contains generic functions for performing
cross validation and for computing diagnostic errors.

Description: Contains generic functions for performing

Title: Cross-Validation for Change-Point Regression
Authors@R: c(person("Pein", "Florian", role = c("aut", "cre"), email = "f.pein@lancaster.ac.uk"))
Imports: changepoint (>= 2.0), wbs (>= 1.4), stepR (>= 2.0), FDRSeg (>=
Description: Can be customised by providing different cross-validation criteria, estimators for the change-point locations and local parameters, and freely chosen folds. Pre-implemented estimators and criteria are available. It also includes our own implementation of the COPPS procedure <doi:10.1214/19-AOS1814>.

Description: Implements the cross-validation methodology from Pein and Shah (2021) <arXiv:2112.03220>. Can be customised by providing different cross-validation criteria, estimators for the change-point locations and local parameters, and freely chosen folds. Pre-implemented estimators and criteria are available. It also includes our own implementation of the COPPS procedure <doi:10.1214/19-AOS1814>.

Title: Rename and Encode Data Frames Using External Crosswalk Files
Authors@R: person("Benjamin", "Skinner", email = "btskinner@virginia.edu",
Description: A pair of functions for renaming and encoding data frames
using external crosswalk files. It is especially useful when
constructing master data sets from multiple smaller data
sets that do not name or encode variables consistently
across files. Based on similar commands in 'Stata'.
Imports: dplyr, haven, labelled, methods, readr, readxl, tibble

Author: Benjamin Skinner [aut, cre] (<https://orcid.org/0000-0002-0337-7415>)

Imports: haven, labelled, methods, readr, readxl, tibble

Title: Generating Crosswords from Word Lists
Description: Generate crosswords from a list of words.

Title: Constrained Regression Model for an Ordinal Response and Ordinal
Description: Fits a constrained regression model for an ordinal response with ordinal predictors and possibly others, Espinosa and Hennig (2018) <arXiv:1804.08715>. The parameter estimates associated with an ordinal predictor are constrained to be monotonic. If a monotonicity direction (isotonic or antitonic) is not specified for an ordinal predictor by the user, then the monotonicity direction classification procedure establishes it. A monotonicity test is also available to test the null hypothesis of monotonicity over a set of parameters associated with an ordinal predictor.