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Tidying Methods for Mixed Models
Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.
Procedures for Psychological, Psychometric, and Personality Research
A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the < https://personality-project.org/r/> web page.
Multiple Criteria Optimization Algorithms and Related Functions
A collection of function to solve multiple criteria optimization problems using genetic algorithms (NSGA-II). Also included is a collection of test functions.
R Interface to RNG with Multiple Streams
Provides an interface to the C implementation of the random number generator with multiple independent streams developed by L'Ecuyer et al (2002). The main purpose of this package is to enable the use of this random number generator in parallel R applications.
Generic Reproducible Parallel Backend for 'foreach' Loops
Provides functions to perform
reproducible parallel foreach loops, using independent
random streams as generated by L'Ecuyer's combined
multiple-recursive generator [L'Ecuyer (1999),
Some Additional Multiple Imputation Functions, Especially for 'mice'
Contains functions for multiple imputation which
complements existing functionality in R.
In particular, several imputation methods for the
mice package (van Buuren & Groothuis-Oudshoorn, 2011,
Multivariate Imputation by Chained Equations
Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm as described in Van Buuren and
Groothuis-Oudshoorn (2011)
Manipulate R Data Frames Using SQL
The sqldf() function is typically passed a single argument which is an SQL select statement where the table names are ordinary R data frame names. sqldf() transparently sets up a database, imports the data frames into that database, performs the SQL select or other statement and returns the result using a heuristic to determine which class to assign to each column of the returned data frame. The sqldf() or read.csv.sql() functions can also be used to read filtered files into R even if the original files are larger than R itself can handle. 'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported.
Using R to Run 'JAGS'
Providing wrapper functions to implement Bayesian analysis in JAGS. Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.
Algorithms for Routing and Solving the Traffic Assignment Problem
Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm.
Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959)