Basic Setup for Projects in R for Monterey County Office of Education. It contains functions often used in the analysis of education data in the county office including seeing if an item is not in a list, rounding in the manner the general public expects, including logos for districts, switching between district names and their county-district-school codes, accessing the local SQL table and making thematically consistent graphs.

Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using Stan'.

Perform calculations for the WHO International Reference Reagents for the microbiome. Using strain, species or genera abundance tables generated through analysis of 16S ribosomal RNA sequencing or shotgun sequencing which included a reference reagent. This package will calculate measures of sensitivity, False positive relative abundance, diversity, and similarity based on mean average abundances with respect to the reference reagent.

Conducts and simulates the MABOUST design, including making interim decisions to stop a treatment for inferiority or stop the trial early for superiority or equivalency.

Analyzes subject-level data in clinical trials using the metalite data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the subject-level analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) <https://r4csr.org/>.

Data sets related to the Islas Malvinas /// Sets de datos relacionados a las Islas Malvinas - La Nación Argentina ratifica su legà tima e imprescriptible soberanà a sobre las islas Malvinas, Georgias del Sur y Sándwich del Sur y los espacios marà timos e insulares correspondientes, por ser parte integrante del territorio nacional. La recuperación de dichos territorios y el ejercicio pleno de la soberanà a, respetando el modo de vida de sus habitantes y conforme a los principios del Derecho Internacional, constituyen un objetivo permanente e irrenunciable del pueblo argentino.

Estimates the sample size needed to detect microbial contamination in a lot with a user-specified detection probability and user-specified analytical sensitivity. Various patterns of microbial contamination are accounted for: homogeneous (Poisson), heterogeneous (Poisson-Gamma) or localized(Zero-inflated Poisson). Ida Jongenburger et al. (2010) <doi:10.1016/j.foodcont.2012.02.004> "Impact of microbial distributions on food safety". Leroy Simon (1963) <doi:10.1017/S0515036100001975> "Casualty Actuarial Society - The Negative Binomial and Poisson Distributions Compared".

This package provides methods for modeling moderator variables in cross-sectional, temporal, and multi-level networks. Includes model selection techniques and a variety of plotting functions. Implements the methods described by Swanson (2020) <https://www.proquest.com/openview/d151ab6b93ad47e3f0d5e59d7b6fd3d3>.

Efficiently estimates single- and multilevel latent class models with covariates, allowing for output visualization in all specifications. For more technical details, see Lyrvall et al. (2025) <doi:10.1080/00273171.2025.2473935>.

Generates blocked designs for mixed-level factorial experiments for a given block size. Internally, it uses finite-field based, collapsed, and heuristic methods to construct block structures that minimize confounding between block effects and factorial effects. The package creates the full treatment combination table, partitions runs into blocks, and computes detailed confounding diagnostics for main effects and two-factor interactions. It also checks orthogonal factorial structure (OFS) and computes efficiencies of factorial effects using the methods of Nair and Rao (1948) <doi:10.1111/j.2517-6161.1948.tb00005.x>. When OFS is not satisfied but the design has equal treatment replications and equal block sizes, a general method based on the C-matrix and custom contrast vectors is used to compute efficiencies. The output includes the generated design, finite-field metadata, confounding summaries, OFS diagnostics, and efficiency results.

Various affine invariant multivariate normality tests are provided. It is designed to accompany the survey article Ebner, B. and Henze, N. (2020) <arXiv:2004.07332> titled "Tests for multivariate normality -- a critical review with emphasis on weighted L^2-statistics". We implement new and time honoured L^2-type tests of multivariate normality, such as the Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of Henze-Jiménes-Gamero, the test of Henze-Jiménes-Gamero-Meintanis, the test of Henze-Visage, the Dörr-Ebner-Henze test based on harmonic oscillator and the Dörr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we include the measures of multivariate skewness and kurtosis by Mardia, Koziol, Malkovich and Afifi and Móri, Rohatgi and Székely, as well as the associated tests. Thirdly, we include the tests of multivariate normality by Cox and Small, the energy test of Székely and Rizzo, the tests based on spherical harmonics by Manzotti and Quiroz and the test of Pudelko. All the functions and tests need the data to be a n x d matrix where n is the samplesize (number of rows) and d is the dimension (number of columns).

The inference in multi-state models is traditionally performed under a Markov assumption that claims that past and future of the process are independent given the present state. In this package, we consider tests of the Markov assumption that are applicable to general multi-state models. Three approaches using existing methodology are considered: a simple method based on including covariates depending on the history in Cox models for the transition intensities; methods based on measuring the discrepancy of the non-Markov estimators of the transition probabilities to the Markov Aalen-Johansen estimators; and, finally, methods that were developed by considering summaries from families of log-rank statistics where patients are grouped by the state occupied of the process at a particular time point (see Soutinho G, Meira-Machado L (2021) <doi:10.1007/s00180-021-01139-7> and Titman AC, Putter H (2020) <doi:10.1093/biostatistics/kxaa030>).

Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet (Process) mixture of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used to reduce the dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically infer an appropriate number of latent classes or find k classes, as defined by the user. The model is based on a paper by Dunson and Xing (2009) <doi:10.1198/jasa.2009.tm08439>, but implements a scalable variational inference algorithm so that it is applicable to large datasets. It is described and tested in the accompanying paper by Ahlmann-Eltze and Yau (2018) <doi:10.1109/DSAA.2018.00068>.

Multi-Fidelity emulator for data from computer simulations of the same underlying system but at different input locations and fidelity level, where both the input locations and fidelity level can be continuous. Active Learning can be performed with an implementation of the Integrated Mean Square Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025, <doi:10.48550/arXiv.2503.23158>).

This package provides a leadership-inference framework for multivariate time series. The framework for multiple-faction-leadership inference from coordinated activities or mFLICA uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. mFLICA reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) <doi:10.1137/1.9781611975321.62> for methodology and Chainarong Amornbunchornvej (2021) <doi:10.1016/j.softx.2021.100781> for software when referring to this package in publications.

This package provides methods of selecting one from many numeric predictors for a regression model, to ensure that the additional predictor has the maximum effect size.

Multivariate joint models of longitudinal and time-to-event data based on functional principal components implemented with bamlss'. Implementation for Volkmann, Umlauf, Greven (2023) <arXiv:2311.06409>.

Mouse-tracking, the analysis of mouse movements in computerized experiments, is a method that is becoming increasingly popular in the cognitive sciences. The mousetrap package offers functions for importing, preprocessing, analyzing, aggregating, and visualizing mouse-tracking data. An introduction into mouse-tracking analyses using mousetrap can be found in Wulff, Kieslich, Henninger, Haslbeck, & Schulte-Mecklenbeck (2023) <doi:10.31234/osf.io/v685r> (preprint: <https://osf.io/preprints/psyarxiv/v685r>).

This package implements analytical methods for multidimensional plant traits, including Competitors-Stress tolerators-Ruderals strategy analysis using leaf traits, Leaf-Height-Seed strategy analysis, Niche Periodicity Table analysis, and Trait Network analysis. Provides functions for data analysis, visualization, and network metrics calculation. Methods are based on Grime (1974) <doi:10.1038/250026a0>, Pierce et al. (2017) <doi:10.1111/1365-2435.12882>, Westoby (1998) <doi:10.1023/A:1004327224729>, Winemiller et al. (2015) <doi:10.1111/ele.12462>, He et al. (2020) <doi:10.1016/j.tree.2020.06.003>.

Create native charts for Microsoft PowerPoint and Microsoft Word documents. These can then be edited and annotated. Functions are provided to let users create charts, modify and format their content. The chart's underlying data is automatically saved within the Word document or PowerPoint presentation. It extends package officer that does not contain any feature for Microsoft native charts production.

This package provides a guidance system for analysis with missing data. It incorporates expert, up-to-date methodology to help researchers choose the most appropriate analysis approach when some data are missing. You provide the available data and the assumed causal structure, including the likely causes of missing data. midoc will advise which analysis approaches can be used, and how best to perform them. midoc follows the framework for the treatment and reporting of missing data in observational studies (TARMOS). Lee et al (2021). <doi:10.1016/j.jclinepi.2021.01.008>.

Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.

Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by debug package.

This package provides a complement to all editions of *Modern Data Science with R* (ISBN: 978-0367191498, publisher URL: <https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498>). This package contains data and code to complete exercises and reproduce examples from the text. It also facilitates connections to the SQL database server used in the book. All editions of the book are supported by this package.