Downloading, Reading and Analyzing PNADC Microdata

Provides tools for downloading, reading and analyzing the Continuous National Household Sample Survey - PNADC, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website < https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.

Transformation Models

Formula-based user-interfaces to specific transformation models implemented in package 'mlt' (, ). Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, ). The underlying theory is described in Hothorn et al. (2018) . An extension to transformation models for clustered data is provided (Barbanti and Hothorn, 2022, ) and a tutorial explains applications in survival analysis (Siegfried et al., 2025, ). Multivariate conditional transformation models (Klein et al, 2022, ) and shift-scale transformation models (Siegfried et al, 2023, ) can be fitted as well. The package contains an implementation of a doubly robust score test, described in Kook et al. (2024, ).

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 but are also useful in educational, social science, business, and biological research. This package includes more than 100 confidence interval functions and more than 80 sample size functions for 1-group, 2-group, paired-samples, and multiple-group designs and for a variety of parameters including means, medians, proportions, slopes, standardized mean differences, standardized linear contrasts of means, and several measures of correlation and association. 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 about these methods see: Statistical Methods for Psychologists, Volumes 1 – 4, < https://dgbonett.sites.ucsc.edu/>.

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.

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) . Extends package 'lme4' (Bates, Maechler, Bolker, and Walker (2015) ).

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) .

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.

Computation and Plots of Influence Functions for Risk and Performance Measures

Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Chen and Martin (2018) < https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019) < https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.