Title: Flexible Cutoffs for Model Fit Evaluation in Covariance-Based
Description: A toolbox to derive flexible cutoffs for fit indices in 'Covariance-based Structural Equation Modeling' based on the paper by 'Niemand & Mai (2018)' <doi:10.1007/s11747-018-0602-9>. Flexible cutoffs are an alternative to fixed cutoffs - rules-of-thumb - regarding an appropriate cutoff for fit indices such as 'CFI' or 'SRMR'. It has been demonstrated that these flexible cutoffs perform better than fixed cutoffs in grey areas where misspecification is not easy to detect. The package provides an alternative to the tool at <https://flexiblecutoffs.org> as it allows to tailor flexible cutoffs to a given dataset and model, which is so far not available in the tool. The package simulates fit indices based on a given dataset and model and then estimates the flexible cutoffs. Some useful functions, e.g., to determine the 'GoF-' or 'BoF-nature' of a fit index, are provided. So far, additional options for a relative use (is a model better than another?) are provided in an exploratory manner.
Imports: lavaan, semTools, parallel, checkmate, data.table
Suggests: rmarkdown, knitr, ggplot2, MASS, testthat (>= 3.0.0)
Author: Thomas Niemand [aut, cre] (<https://orcid.org/0000-0001-7677-1168>),
Packaged: 2022-02-11 12:47:29 UTC; thomasniemand

Title: Fundamental Clustering Problems Suite
Authors@R: c(person("Michael", "Thrun", email= "m.thrun@gmx.net",role=c("aut","cre","cph")),person("Peter", "Nahrgang",role=c("ctr","ctb")),person("Alfred", "Ultsch",role=c("dtc","ctb")))
Description: Many conventional Clustering Algorithms are provided in this package with consistent input and output, which enables the user to tryout algorithms swiftly. Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data. The datasets were introduced in Ultsch, A.: "Clustering with SOM: U*C", In Workshop on Self-Organizing Maps, 2005.
Suggests: DataVisualizations, kernlab, cclust, vegan, dbscan, kohonen,
MCL, ADPclust, cluster, DatabionicSwarm, orclus, subspace,