Three generalizations of the synthetic control method (which has already an implementation in package Synth') are implemented: first, MSCMT allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klöà ner (2018) <doi:10.1016/j.ecosta.2017.08.002>.

Deploy file changes across multiple GitHub repositories using the GitHub Web API <https://docs.github.com/en/rest>. Allows synchronizing common files, Continuous Integration ('CI') workflows, or configurations across many repositories with a single command.

Some basic math calculators for finding angles for triangles and for finding the greatest common divisor of two numbers and so on.

This package provides tools and demonstrates methods for working with individual undergraduate student-level records (registrar's data) in R'. Tools include filters for program codes, data sufficiency, and timely completion. Methods include gathering blocs of records, computing quantitative metrics such as graduation rate, and creating charts to visualize comparisons. midfieldr interacts with practice data provided in midfielddata', an R data package available at <https://midfieldr.github.io/midfielddata/>. midfieldr also interacts with the full MIDFIELD database for users who have access. This work is supported by the US National Science Foundation through grant numbers 1545667 and 2142087.

This package provides a method for the multiresolution analysis of spatial fields and images to capture scale-dependent features. mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales. To infer which of the captured features are credible, Bayesian analysis is used. The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction. (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found. (3) Posterior credibility analysis of the differences of smooths created. The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011> and extended in Flury, Gerber, Schmid and Furrer (2021) <DOI:10.1016/j.spasta.2020.100483>.

Implementation of adaptive assessment procedures based on Knowledge Space Theory (KST, Doignon & Falmagne, 1999 <ISBN:9783540645016>) and Formal Psychological Assessment (FPA, Spoto, Stefanutti & Vidotto, 2010 <doi:10.3758/BRM.42.1.342>) frameworks. An adaptive assessment is a type of evaluation that adjusts the difficulty and nature of subsequent questions based on the test taker's responses to previous ones. The package contains functions to perform and simulate an adaptive assessment. Moreover, it is integrated with two Shiny interfaces, making it both accessible and user-friendly. The package has been partially funded by the European Union - NextGenerationEU and by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.5, project â RAISE - Robotics and AI for Socio-economic Empowermentâ (ECS00000035).

Model for simulating language evolution in terms of cultural evolution (Smith & Kirby (2008) <DOI:10.1098/rstb.2008.0145>; Deacon 1997). The focus is on the emergence of argument-marking systems (Dowty (1991) <DOI:10.1353/lan.1991.0021>, Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e. noun marking (Aristar (1997) <DOI:10.1075/sl.21.2.04ari>, Lestrade (2010) <DOI:10.7282/T3ZG6R4S>), person indexing (Ariel 1999, Dahl (2000) <DOI:10.1075/fol.7.1.03dah>, Bhat 2004), and word order (Dryer 2013), but extensions are foreseen. Agents start out with a protolanguage (a language without grammar; Bickerton (1981) <DOI:10.17169/langsci.b91.109>, Jackendoff 2002, Arbib (2015) <DOI:10.1002/9781118346136.ch27>) and interact through language games (Steels 1997). Over time, grammatical constructions emerge that may or may not become obligatory (for which the tolerance principle is assumed; Yang 2016). Throughout the simulation, uniformitarianism of principles is assumed (Hopper (1987) <DOI:10.3765/bls.v13i0.1834>, Givon (1995) <DOI:10.1075/z.74>, Croft (2000), Saffran (2001) <DOI:10.1111/1467-8721.01243>, Heine & Kuteva 2007), in which maximal psychological validity is aimed at (Grice (1975) <DOI:10.1057/9780230005853_5>, Levelt 1989, Gaerdenfors 2000) and language representation is usage based (Tomasello 2003, Bybee 2010). In Lestrade (2015b) <DOI:10.15496/publikation-8640>, Lestrade (2015c) <DOI:10.1075/avt.32.08les>, and Lestrade (2016) <DOI:10.17617/2.2248195>), which reported on the results of preliminary versions, this package was announced as WDWTW (for who does what to whom), but for reasons of pronunciation and generalization the title was changed.

This package provides probability mass, distribution, quantile, random variate generation, and method-of-moments parameter fitting for the MBBEFD family of distributions used in insurance modeling as described in Bernegger (1997) <doi:10.2143/AST.27.1.563208> without any external dependencies.

Functionalities for facilitating systematic reviews, data extractions, and meta-analyses. It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter- reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; figure and image extractions from PDFs; web scraping of citations; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software (i.e., metawin). Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323-330 <doi:10.1111/2041-210X.12472>.

Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" <doi:10.1101/2024.03.27.587024>.

Algorithms to build set partitions and commutator matrices and their use in the construction of multivariate d-Hermite polynomials; estimation and derivation of theoretical vector moments and vector cumulants of multivariate distributions; conversion formulae for multivariate moments and cumulants. Applications to estimation and derivation of multivariate measures of skewness and kurtosis; estimation and derivation of asymptotic covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis. The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods".

Companion package of Carrion-i-Silvestre & Sansó (2023): "Generalized Extreme Value Approximation to the CUMSUMQ Test for Constant Unconditional Variance in Heavy-Tailed Time Series". It implements the Modified Iterative Cumulative Sum of Squares Algorithm, which is an extension of the Iterative Cumulative Sum of Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in the unconditional variance of a time series controlling for the tail index of the underlying distribution. The fourth order moment is estimated non-parametrically to avoid the size problems when the innovations are non-Gaussian (see, Sansó et al., 2004). Critical values and p-values are generated using a Generalized Extreme Value distribution approach. References Carrion-i-Silvestre J.J & Sansó A (2023) <https://www.ub.edu/irea/working_papers/2023/202309.pdf>. Inclan C & Tiao G.C (1994) <doi:10.1080/01621459.1994.10476824>, Sansó A & Aragó V & Carrion-i-Silvestre J.L (2004) <https://dspace.uib.es/xmlui/bitstream/handle/11201/152078/524035.pdf>.

Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.

Visualize the relationship between linear regression variables and causes of multi-collinearity. Implements the method in Lin et. al. (2020) <doi:10.1080/10618600.2020.1779729>.

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.

The multiple instance data set consists of many independent subjects (called bags) and each subject is composed of several components (called instances). The outcomes of such data set are binary or categorical responses, and, we can only observe the subject-level outcomes. For example, in manufacturing processes, a subject is labeled as "defective" if at least one of its own components is defective, and otherwise, is labeled as "non-defective". The milr package focuses on the predictive model for the multiple instance data set with binary outcomes and performs the maximum likelihood estimation with the Expectation-Maximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection.

Computation of the multivariate marine recovery index, including functions for data visualization and ecological diagnostics of marine ecosystems. The computational details are described in the original publication. Reference: Chauvel, N., Grall, J., Thiébaut, E., Houbin, C., Pezy, J.P. (in press). "A general-purpose Multivariate Marine Recovery Index for quantifying the influence of human activities on benthic habitat ecological status". Ecological Indicators.

Mica is a server application used to create data web portals for large-scale epidemiological studies or multiple-study consortia. Mica helps studies to provide scientifically robust data visibility and web presence without significant information technology effort. Mica provides a structured description of consortia, studies, annotated and searchable data dictionaries, and data access request management. This Mica client allows to perform data extraction for reporting purposes.

This package provides tools for univariate and multivariate generalized linear models with model averaging and null model technique.

Simulation from an mrgsolve <https://cran.r-project.org/package=mrgsolve> model using a parallel backend. Input data sets are split (chunked) and simulated in parallel using mclapply() or future_lapply() <https://cran.r-project.org/package=future.apply>.

This package provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided. For details, see the paper by Jasjeet Sekhon (2007, <doi:10.18637/jss.v042.i07>).

Random Forest Spatial Interpolation (RFSI, SekuliÄ et al. (2020) <doi:10.3390/rs12101687>) and spatio-temporal geostatistical (spatio-temporal regression Kriging (STRK)) interpolation for meteorological (Kilibarda et al. (2014) <doi:10.1002/2013JD020803>, SekuliÄ et al. (2020) <doi:10.1007/s00704-019-03077-3>) and other environmental variables. Contains global spatio-temporal models calculated using publicly available data.

Computes indirect effects, conditional effects, and conditional indirect effects in a structural equation model or path model after model fitting, with no need to define any user parameters or label any paths in the model syntax, using the approach presented in Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z>. Can also form bootstrap confidence intervals by doing bootstrapping only once and reusing the bootstrap estimates in all subsequent computations. Supports bootstrap confidence intervals for standardized (partially or completely) indirect effects, conditional effects, and conditional indirect effects as described in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Model fitting can be done by structural equation modeling using lavaan() or regression using lm().

This package provides two variants of multiple correspondence analysis (ca): multiple ca and ordered multiple ca via orthogonal polynomials of Emerson.