Power Analysis and Sample Size Calculation

Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), ; Dobbin and Simon (2007), ) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), ; Thulin (2014), ), comparing two negative binomial rates (Zhu and Lakkis (2014), ), ANCOVA (Shieh (2020), ), reference ranges (Jennen-Steinmetz and Wellek (2005), ), multiple primary endpoints (Sozu et al. (2015), ISBN:978-3-319-22005-5), and AUC (Hanley and McNeil (1982), ).

Inferential Statistics

Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), ), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3), intersection-union (Sozu et al. (2015), ISBN:978-3-319-22005-5) and multiple imputation (Barnard and Rubin (1999), ) t-test; furthermore, computation of intersection-union z-test as well as multiple imputation Wilcoxon tests. Graphical visualizations: volcano plot, Bland-Altman plots (Bland and Altman (1986), ; Shieh (2018), ), mean difference plot (Boehning et al. (2008), ), plot of test statistic for permutation and bootstrap tests as well as objects of class htest.

Article Formats for R Markdown

A suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.

Tools for Statistical Disclosure Control in Research Data Centers

Tools for researchers to explicitly show that their results comply to rules for statistical disclosure control imposed by research data centers. These tools help in checking descriptive statistics and models and in calculating extreme values that are not individual data. Also included is a simple function to create log files. The methods used here are described in the "Guidelines for the checking of output based on microdata research" by Bond, Brandt, and de Wolf (2015) < https://cros.ec.europa.eu/system/files/2024-02/Output-checking-guidelines.pdf>.

Taxonomic Information from Around the Web

Interacts with a suite of web application programming interfaces (API) for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more. Some of the services supported include 'NCBI E-utilities' (< https://www.ncbi.nlm.nih.gov/books/NBK25501/>), 'Encyclopedia of Life' (< https://eol.org/docs/what-is-eol/data-services>), 'Global Biodiversity Information Facility' (< https://techdocs.gbif.org/en/openapi/>), and many more. Links to the API documentation for other supported services are available in the documentation for their respective functions in this package.

Permutational Group Sequential Test for Time-to-Event Data

Permutational group-sequential tests for time-to-event data based on the log-rank test statistic. Supports exact permutation test when the censoring distributions are equal in the treatment and the control group and approximate imputation-permutation methods when the censoring distributions are different.

A Time Series Database for Official Statistics with R and PostgreSQL

Archive and manage times series data from official statistics. The 'timeseriesdb' package was designed to manage a large catalog of time series from official statistics which are typically published on a monthly, quarterly or yearly basis. Thus timeseriesdb is optimized to handle updates caused by data revision as well as elaborate, multi-lingual meta information.

High Dimensional Categorical Data Visualization

Easy visualization for datasets with more than two categorical variables and additional continuous variables. The package is particularly useful for exploring complex categorical data in the context of pathway analysis across multiple conditions. This package is now in maintenance-only mode and kept for legacy compatibility; for new projects and active development, please use the successor package 'ggdiceplot' (see < https://github.com/maflot/ggdiceplot> and < https://dice-and-domino-plot.readthedocs.io/en/latest/>).

Dice Plot Visualization for 'ggplot2'

Provides 'ggplot2' extensions for creating dice-based visualizations where each dot position represents a specific categorical variable. The package includes geom_dice() for displaying presence/absence of categorical variables using traditional dice patterns. Each dice position (1-6) represents a different category, with dots shown only when that category is present. This allows intuitive visualization of up to 6 categorical variables simultaneously.

Parametric Time-to-Event Analysis with Variable Incubation Phases

Fit parametric models for time-to-event data that show an initial 'incubation period', i.e., a variable delay phase where the hazard is zero. The delayed Weibull distribution serves as foundational data model. The specific method of 'MPSE' (maximum product of spacings estimation) and MLE-based methods are used for parameter estimation. Bootstrap confidence intervals for parameters and significance tests in a two group setting are provided.