The german Wikibook "GNU R" introduces R to new users. This package is a collection of functions and datas used in the german WikiBook "GNU R".

Use various regression models for the analysis of win loss endpoints adjusting for non-binary and multivariate covariates.

Evaluation of prediction performance of smaller regions of spectra for Chemometrics. Segmentation of spectra, evolving dimensions regions and sliding windows as selection methods. Election of the best model among those computed based on error metrics. Chen et al.(2017) <doi:10.1007/s00216-017-0218-9>.

This package implements inferential and graphic procedures for the semiparametric proportional means regression of weighted composite endpoint of recurrent event and death (Mao and Lin, 2016, <doi:10.1093/biostatistics/kxv050>).

All functions and data sets required for the examples in the book Hyndman (2026) "That's Weird: Anomaly Detection Using R" <https://OTexts.com/weird/>. All packages needed to run the examples are also loaded.

Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data. Fasta-formatted proteomes (eg from UniProt Consortium <doi:10.1093/nar/gky1049>) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted. Initial results from multiple software for protein (and peptide) quantitation can be imported (to a common format): MaxQuant (Tyanova et al 2016 <doi:10.1038/nprot.2016.136>), Dia-NN (Demichev et al 2020 <doi:10.1038/s41592-019-0638-x>), Fragpipe (da Veiga et al 2020 <doi:10.1038/s41592-020-0912-y>), ionbot (Degroeve et al 2021 <doi:10.1101/2021.07.02.450686>), MassChroq (Valot et al 2011 <doi:10.1002/pmic.201100120>), OpenMS (Strauss et al 2021 <doi:10.1038/nmeth.3959>), ProteomeDiscoverer (Orsburn 2021 <doi:10.3390/proteomes9010015>), Proline (Bouyssie et al 2020 <doi:10.1093/bioinformatics/btaa118>), AlphaPept (preprint Strauss et al <doi:10.1101/2021.07.23.453379>) and Wombat-P (Bouyssie et al 2023 <doi:10.1021/acs.jproteome.3c00636>. Meta-data provided by initial analysis software and/or in sdrf format can be integrated to the analysis. Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons. Help is provided to inspect the data graphically to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement algorithms. Meta-data in sdrf-format (Perez-Riverol et al 2020 <doi:10.1021/acs.jproteome.0c00376>) or similar tabular formats can be imported and included. Missing values can be inspected and imputed based on the concept of NA-neighbours or other methods. Dedicated filtering and statistical testing using the framework of package limma (<doi:10.18129/B9.bioc.limma>) can be run, enhanced by multiple rounds of NA-replacements to provide robustness towards rare stochastic events. Multi-species samples, as frequently used in benchmark-tests (eg Navarro et al 2016 <doi:10.1038/nbt.3685>, Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>), can be run with special options considering such sub-groups during normalization and testing. Subsequently, ROC curves (Hand and Till 2001 <doi:10.1023/A:1010920819831>) can be constructed to compare multiple analysis approaches. As detailed example the data-set from Ramus et al 2016 (<doi:10.1016/j.jprot.2015.11.011>) quantified by MaxQuant, ProteomeDiscoverer, and Proline is provided with a detailed analysis of heterologous spike-in proteins.

Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test that is needed for a given power and two sided type I error rate. The method works for metric data with and without ties, count data, ordered categorical data, and even dichotomous data. But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect. See Happ et al. (2019, <doi:10.1002/sim.7983>) for details.

Imports WhatsApp chat logs and parses them into a usable dataframe object. The parser works on chats exported from Android or iOS phones and on Linux, macOS and Windows. The parser has multiple options for extracting smileys and emojis from the messages, extracting URLs and domains from the messages, extracting names and types of sent media files from the messages, extracting timestamps from messages, extracting and anonymizing author names from messages. Can be used to create anonymized versions of data.

Retrieve geographical information for airports using their IATA or ICAO codes.

This package provides a multi-visit clinical trial may collect participant responses on an ordinal scale and may utilize a stratified design, such as randomization within centers, to assess treatment efficacy across multiple visits. Baseline characteristics may be strongly associated with the outcome, and adjustment for them can improve power. The win ratio (ignores ties) and the win odds (accounts for ties) can be useful when analyzing these types of data from randomized controlled trials. This package provides straightforward functions for adjustment of the win ratio and win odds for stratification and baseline covariates, facilitating the comparison of test and control treatments in multi-visit clinical trials. For additional information concerning the methodologies and applied examples within this package, please refer to the following publications: 1. Weideman, A.M.K., Kowalewski, E.K., & Koch, G.G. (2024). â Randomization-based covariance adjustment of win ratios and win odds for randomized multi-visit studies with ordinal outcomes.â Journal of Statistical Research, 58(1), 33â 48. <doi:10.3329/jsr.v58i1.75411>. 2. Kowalewski, E.K., Weideman, A.M.K., & Koch, G.G. (2023). â SAS macro for randomization-based methods for covariance and stratified adjustment of win ratios and win odds for ordinal outcomes.â SESUG 2023 Proceedings, Paper 139-2023.

This package provides efficient implementations of weighted dependence measures and related asymptotic tests for independence. Implemented measures are the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, Hoeffding's D, and Chatterjee's xi; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0>, Hollander et al. (2015, ISBN:9780470387375), and Chatterjee (2021) <doi:10.1080/01621459.2020.1758115>.

Conducts single coefficient tests and multiple-contrast hypothesis tests of meta-regression models using cluster wild bootstrapping, based on methods examined in Joshi, Pustejovsky, and Beretvas (2022) <DOI:10.1002/jrsm.1554>.

The wavelet-based variance transformation method is used for system modelling and prediction. It refines predictor spectral representation using Wavelet Theory, which leads to improved model specifications and prediction accuracy. Details of methodologies used in the package can be found in Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962>, Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>, and Jiang, Z., Sharma, A., & Johnson, F. (2021) <doi:10.1016/J.JHYDROL.2021.126816>.

Shows the relationship between an independent and dependent variable through Weight of Evidence and Information Value.

Extract features and classify documents with noisy labels given by document-meta data or keyword matching Watanabe & Zhou (2020) <doi:10.1177/0894439320907027>.

Select data analysis plots, under a standardized calling interface implemented on top of ggplot2 and plotly'. Plots of interest include: ROC', gain curve, scatter plot with marginal distributions, conditioned scatter plot with marginal densities, box and stem with matching theoretical distribution, and density with matching theoretical distribution.

Application to estimate statistical values using properties provided by a group of individuals to describe concepts using shiny'. It estimates the underlying distribution to generate new descriptive words Canessa et al. (2023) <doi:10.3758/s13428-022-01811-w>, applies a new clustering model, and uses simulations to estimate the probability that two persons describe the same words based on their descriptions Canessa et al. (2022) <doi:10.3758/s13428-022-02030-z>.

R interface to the weightederm package for Python', which provides scikit-learn'-style estimators for offline change point regression (data segmentation) via weighted empirical risk minimization. Supports least-squares, Huber, and logistic losses with fixed or cross-validated numbers of change points. Wraps Python via reticulate'. Arpino and Venkataramanan (2026) <doi:10.48550/arXiv.2604.11746>.

Graphical data analysis of accelerated life tests. Methods derived from Wayne Nelson (1990, ISBN: 9780471522775), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6).

Numerous time series admit autoregressive moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent. These models are called weak ARMA by opposition to the standard ARMA models, also called strong ARMA models, in which the error terms are supposed to be independent and identically distributed (iid). This package allows the study of nonlinear time series models through weak ARMA representations. It determines identification, estimation and validation for ARMA models and for AR and MA models in particular. Functions can also be used in the strong case. This package also works on white noises by omitting arguments p', q', ar and ma'. See Francq, C. and Zakoïan, J. (1998) <doi:10.1016/S0378-3758(97)00139-0> and Boubacar Maïnassara, Y. and Saussereau, B. (2018) <doi:10.1080/01621459.2017.1380030> for more details.

Collect multichannel marketing data from sources such as Google analytics, Facebook Ads, and many others using the Windsor.ai API <https://www.windsor.ai/api-fields/>.

This package provides a new inverse probability of selection weighted Cox model to deal with outcome-dependent sampling in survival analysis.

Estimate and plot wavelet quantile correlations(Kumar and Padakandla,2022) between two time series. Wavelet quantile correlation is used to capture the dependency between two time series across quantiles and different frequencies. This method is useful in identifying potential hedges and safe-haven instruments for investment purposes. See Kumar and Padakandla(2022) <doi:10.1016/j.frl.2022.102707> for further details.

The Model Disability Survey (MDS) <https://www.who.int/activities/collection-of-data-on-disability> is a World Health Organization (WHO) general population survey instrument to assess the distribution of disability within a country or region, grounded in the International Classification of Functioning, Disability and Health <https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health>. This package provides fit-for-purpose functions for calculating and presenting the results from this survey, as used by the WHO. The package primarily provides functions for implementing Rasch Analysis (see Andrich (2011) <doi:10.1586/erp.11.59>) to calculate a metric scale for disability.