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Statistical Methods for Psychologists
Implements confidence interval and sample size methods that are especially useful in psychological research. The methods can be applied in 1-group, 2-group, paired-samples, and multiple-group designs and to a variety of parameters including means, medians, proportions, slopes, standardized mean differences, standardized linear contrasts of means, plus several measures of correlation and association. The confidence intervals and sample size functions are applicable to single parameters as well as differences, ratios, and linear contrasts of parameters. The sample size functions can be used to approximate the sample size needed to estimate a parameter or function of parameters with desired confidence interval precision or to perform a variety of hypothesis tests (directional two-sided, equivalence, superiority, noninferiority) with desired power. For details see: Statistical Methods for Psychologists, Volumes 1 – 4, < https://dgbonett.sites.ucsc.edu/>.
Varying Coefficient Meta-Analysis
Implements functions for varying coefficient meta-analysis methods. These methods do not assume effect size homogeneity. Subgroup effect size comparisons, general linear effect size contrasts, and linear models of effect sizes based on varying coefficient methods can be used to describe effect size heterogeneity. Varying coefficient meta-analysis methods do not require the unrealistic assumptions of the traditional fixed-effect and random-effects meta-analysis methods. For details see: Statistical Methods for Psychologists, Volume 5, < https://dgbonett.sites.ucsc.edu/>.
Regression with Interval-Censored Covariates
Provides functions to simulate and analyze data for a regression model with an interval censored covariate, as described in Morrison et al. (2021)
Examples from Multilevel Modelling Software Review
Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.
Consistent Anonymisation Across Datasets
A simple function that anonymises a list of variables in a consistent way: anonymised factors are not recycled and the same original levels receive the same anonymised factor even if located in different datasets.
Supplementary Item Response Theory Models
Supplementary functions for item response models aiming
to complement existing R packages. The functionality includes among others
multidimensional compensatory and noncompensatory IRT models
(Reckase, 2009,
Nonparametric Methods for Cognitive Diagnosis
An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.
Read and Process 'Pamguard' Binary Data
Functions for easily reading and processing binary data files created by 'Pamguard' (< https://www.pamguard.org/>). All functions for directly reading the binary data files are based on 'MATLAB' code written by Michael Oswald.
Multi-Resolution Kriging Based on Markov Random Fields
Methods for the interpolation of large spatial datasets. This package uses a basis function approach that provides a surface fitting method that can approximate standard spatial data models. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) Moreover, the covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations and this package makes of the R spam package for sparse linear algebra. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific R functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary covariances and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation. See the 'LatticeKrigRPackage' GitHub repository for a vignette of this package. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research.
Nonlinear least squares examples from NIST
Datasets for testing nonlinear regression routines.