Package with multivariate analysis methodologies for experiment evaluation. The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA, principal components, canonical variables, etc. (Pacote com metodologias de analise multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade, construi de dendogramas, obtem a MANOVA, componentes principais, variaveis canonicas, etc.).

This package provides a toolbox for modeling manifest and latent group differences and moderation effects in various statistical network models.

Relatively easy access is provided to 2023 version of the Maddison project data downloaded 2025-08-28. This project collates all the credible data on population and GDP for 169 countries, with some dating back to the year 1 of the current era. One function makes it easy to find the leaders for each year, allowing users to delete countries like OPEC with narrow economies to focus on technology leaders. Another function makes it easy to plot data for only selected countries or years. Another function makes it relatively easy to obtain references to the original sources, which must be cited per the copyright rules of the Maddison Project for different uses of their data.

Integrates fairness auditing and bias mitigation methods for the mlr3 ecosystem. This includes fairness metrics, reporting tools, visualizations and bias mitigation techniques such as "Reweighing" described in Kamiran, Calders (2012) <doi:10.1007/s10115-011-0463-8> and "Equalized Odds" described in Hardt et al. (2016) <https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>. Integration with mlr3 allows for auditing of ML models as well as convenient joint tuning of machine learning algorithms and debiasing methods.

Collection of the state of the art multi-label resampling algorithms. The objective of these algorithms is to achieve balance in multi-label datasets.

Calculates mean cumulative count (MCC) to estimate the expected cumulative number of recurrent events per person over time in the presence of competing risks and censoring. Implements both the Dong-Yasui equation method and sum of cumulative incidence method described in Dong, et al. (2015) <doi:10.1093/aje/kwu289>. Supports inverse probability weighting for causal inference as outlined in Gaber, et al. (2023) <doi:10.1093/aje/kwad031>. Provides S3 methods for printing, summarizing, plotting, and extracting results. Handles grouped analyses and integrates with ggplot2 <https://ggplot2.tidyverse.org/> for visualization.

This package provides a novel mediation analysis approach to address zero-inflated mediators containing true zeros and false zeros. See Jiang et al (2023) "A Novel Causal Mediation Analysis Approach for Zero-Inflated Mediators" <arXiv:2301.10064> for more details.

This package provides methods (standard and advanced) for analysis of agreement between measurement methods. These cover Bland-Altman plots, Deming regression, Lin's Total deviation index, and difference-on-average regression. See Carstensen B. (2010) "Comparing Clinical Measurement Methods: A Practical Guide (Statistics in Practice)" <doi:10.1002/9780470683019> for more information.

Matrix-Based Flexible Project Planning. This package models, plans, and schedules flexible, such as agile, extreme, and hybrid project plans. The package contains project planning, scheduling, and risk assessment functions. Kosztyan (2022) <doi:10.1016/j.softx.2022.100973>.

Model stability and variable inclusion plots [Mueller and Welsh (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al. (2008, <doi:10.1214/07-AOS517>); Jiang et al. (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.

Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) The constrained- unconstrained, residual- and double centering- approaches are estimated via lavaan (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via modsem it self. Alternatively model can be estimated via Mplus (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). <doi:10.1207/S15328007SEM0801_3>. "A note on estimating the Jöreskog-Yang model for latent variable interaction using LISREL 8.3." Klein, A., & Moosbrugger, H. (2000). <doi:10.1007/BF02296338>. "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). <doi:10.1080/00273170701710205>. "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). <doi:10.1080/10705511.2010.488999>. "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). <doi:10.1207/s15328007sem1304_1>. "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). <doi:10.1037/1082-989X.9.3.275>. "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus Userâ s Guide. Eighth Edition." <https://www.statmodel.com/>. Rosseel Y (2012). <doi:10.18637/jss.v048.i02>. "'lavaan': An R Package for Structural Equation Modeling.".

Analysis of annual average ocean water level time series from long (minimum length 80 years) individual records, providing improved estimates of trend (mean sea level) and associated real-time velocities and accelerations. Improved trend estimates are based on Singular Spectrum Analysis methods. Various gap-filling options are included to accommodate incomplete time series records. The package also contains a forecasting module to consider the implication of user defined quantum of sea level rise between the end of the available historical record and the year 2100. A wide range of screen and pdf plotting options are available in the package.

MedDRA data is used for defining adverse events in clinical studies. You can load and merge the data for use in categorizing the adverse events using this package. The package requires the data licensed from MedDRA <https://www.meddra.org/>.

Create an interactive table of descriptive statistics in HTML. This table is typically used for exploratory analysis in a clinical study (referred to as Table 1').

Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.

This package implements the Model Context Protocol (MCP). Users can start R'-based servers, serving functions as tools for large language models to call before responding to the user in MCP-compatible apps like Claude Desktop and Claude Code', with options to run those tools inside of interactive R sessions. On the other end, when R is the client via the ellmer package, users can register tools from third-party MCP servers to integrate additional context into chats.

An easy-to-use workflow that provides tools to create, update and fill literature matrices commonly used in research, specifically epidemiology and health sciences research. The project is born out of need as an easyâ toâ use tool for my research methods classes.

This package contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response. Reference for mean score method, inverse probability weighted method is Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.

Constructs trees for multivariate survival data using marginal and frailty models. Grows, prunes, and selects the best-sized tree.

Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2021) <https://www.research-collection.ethz.ch/handle/20.500.11850/477416>.

Simple helpers for matrix multiplication on data.frames. These allow for more concise code during low level mathematical operations, and help ensure code is more easily read, understood, and serviced.

Allows the user to generate a friendly user interface for emails sending. The user can choose from the most popular free email services ('Gmail', Outlook', Yahoo') and his default email application. The package is a wrapper for the Mailtoui JavaScript library. See <https://mailtoui.com/#menu> for more information.

Magic functions to obtain results from for loops.

We implement a surrogate modeling algorithm to guide simulation-based sample size planning. The method is described in detail in our paper (Zimmer & Debelak (2023) <doi:10.1037/met0000611>). It supports multiple study design parameters and optimization with respect to a cost function. It can find optimal designs that correspond to a desired statistical power or that fulfill a cost constraint. We also provide a tutorial paper (Zimmer et al. (2023) <doi:10.3758/s13428-023-02269-0>).