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Processing of Model Parameters
Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).
Access the Speechs and Speaker's Informations of House of Representatives of Brazil
Scrap speech text and speaker informations of speeches of House of Representatives of Brazil, and transform in a cleaned tibble.
Import Professional Baseball Data from 'Retrosheet'
A collection of tools to import and structure the (currently) single-season event, game-log, roster, and schedule data available from < https://www.retrosheet.org>. In particular, the event (a.k.a. play-by-play) files can be especially difficult to parse. This package does the parsing on those files, returning the requested data in the most practical R structure to use for sabermetric or other analyses.
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/>.
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. Confidence interval and sample size functions are given for 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/>.
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.
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,
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.
Bayesian Linear Mixed-Effects Models
Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013)