This package contains the function mice.impute.midastouch(). Technically this function is to be run from within the mice package (van Buuren et al. 2011), type ??mice. It substitutes the method pmm within mice by midastouch'. The authors have shown that midastouch is superior to default pmm'. Many ideas are based on Siddique / Belin 2008's MIDAS.

This R package provides an implementation of multivariate extensions of a well-known fractal analysis technique, Detrended Fluctuations Analysis (DFA; Peng et al., 1995<doi:10.1063/1.166141>), for multivariate time series: multivariate DFA (mvDFA). Several coefficients are implemented that take into account the correlation structure of the multivariate time series to varying degrees. These coefficients may be used to analyze long memory and changes in the dynamic structure that would by univariate DFA. Therefore, this R package aims to extend and complement the original univariate DFA (Peng et al., 1995) for estimating the scaling properties of nonstationary time series.

This package provides a suite of mixed-integer linear programming (MILP) model builders and solversâ including Gurobi', HiGHS', Symphony', GNU Linear Programming Kit (GLPK)', and lpSolve'â for automated test assembly (ATA) in multistage testing (MST). Offers filtering of decision variables through itemâ module eligibility and the application of explicit bounds to simplify the MILP model and accelerate the optimization process. Supports bottom up, top down, and hybrid assembly strategies; enemy-item and enemy-stimulus exclusions; stimulus all in/all out or partial selection; anchor item/stimulus specification; and item exposure control. Accommodates both single-objective and multi-objective optimization ('weighted sum', maximin', capped maximin', minimax', and goal programming'). Enables simultaneous assembly of multiple panels with item and stimulus content balancing and exposure control. Provides analytical evaluation of assembled MST performance within seconds. Includes tools for diagnosing infeasible optimization models by systematically identifying sources of infeasibility and reformulating models with slack variables to restore feasibility.Methods implemented in this package build on established work in optimal test assembly (van der Linden, 2005 <doi:10.1007/0-387-29054-0>), item-set constrained test assembly (van der Linden, 2000 <doi:10.1177/01466210022031697>), hybrid assembly (Xiong, 2018 <doi:10.1177/0146621618762739>), recursion-based analytic methods (Lim et al., 2021 <doi:10.1111/jedm.12276>), and classification evaluation (Rudner, 2000 <doi:10.7275/an9m-2035>; Rudner, 2005 <doi:10.7275/56a5-6b14>).

This package provides functions for actuarial risk modeling, including survival models, life annuities, multiple-decrement models, and mortality improvement projections. The package is designed to align with standard actuarial notation and supports teaching, exam preparation, and reproducible actuarial analysis. The methods are based on standard actuarial references including Camilli, Duncan and London (2014, ISBN:9781625423474) "Models for Quantifying Risk" and Dickson, Hardy and Waters (2020, ISBN:9781108478083) "Actuarial Mathematics for Life Contingent Risks".

This package implements methods to normalize multiplexed imaging data, including statistical metrics and visualizations to quantify technical variation in this data type. Reference for methods listed here: Harris, C., Wrobel, J., & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging Data. Journal of Open Source Software, 7(71), 4180, <doi:10.21105/joss.04180>.

Diagnostic tools as residual analysis, global, local and total-local influence for the multivariate model from the random intercept Poisson generalized log gamma model are available in this package. Including also, the estimation process by maximum likelihood method, for details see Fabio, L. C; Villegas, C. L.; Carrasco, J.M.F and de Castro, M. (2023) <doi:10.1080/03610926.2021.1939380> and Fábio, L. C.; Villegas, C.; Mamun, A. S. M. A. and Carrasco, J. M. F. (2025) <doi:10.28951/bjb.v43i1.728>.

Uses a kernel smoothing approach to calculate Mutual Information for comparisons between all types of variables including continuous vs continuous, continuous vs discrete and discrete vs discrete. Uses a nonparametric bias correction giving Bias Corrected Mutual Information (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to large genomic datasets.

This package provides functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>. Some functions for continuous times Markov chains depend on the suggested ctmcd package.

Evaluate bias and precision in method comparison studies. One provides measurements for each method and it takes care of the estimates. Multiple plots to evaluate bias, precision and compare methods.

Estimates key quantities in causal mediation analysis - including average causal mediation effects (indirect effects), average direct effects, total effects, and proportions mediated - in the presence of multiple uncausally related mediators. Methods are described by Jerolon et al., (2021) <doi:10.1515/ijb-2019-0088> and extended to accommodate survival outcomes as described by Domingo-Relloso et al., (2024) <doi:10.1101/2024.02.16.24302923>.

This package provides tools to simulate, analyse, visualise, and benchmark grid-based number merge puzzles. The package implements generic grid mechanics, tile-spawning rules, merge rules, scoring functions, reproducible simulation utilities, and local Shiny and WebGL interfaces for interactive use. It is intended for teaching, algorithmic experimentation, and game-theoretic examples. The autoplay helpers use standard heuristic search and Monte Carlo simulation ideas described in Russell and Norvig (2021, ISBN:9780134610993) and Robert and Casella (2004, ISBN:9780387212395).

This package provides a suite of utility functions providing functionality commonly needed for production level projects such as logging, error handling, cache management and date-time parsing. Functions for date-time parsing and formatting require that time zones be specified explicitly, avoiding a common source of error when working with environmental time series.

Missing data imputation based on the missForest algorithm (Stekhoven, Daniel J (2012) <doi:10.1093/bioinformatics/btr597>) with adaptations for prediction settings. The function missForest() is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict() is used to impute one or multiple new observations (test set) using the models learned on the training data. For more details see Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict--Missing data imputation for prediction settings <doi:10.48550/arXiv.2407.03379>.

The provided package implements multiple contrast tests for functional data (Munko et al., 2023, <arXiv:2306.15259>). These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data.

Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023), <doi:10.1007/s12532-023-00238-4>).

This package provides functions to compute and plot multivariate (partial) Mantel correlograms.

Download meteorological data from the Meteo-France public API Données climatologiques <https://portail-api.meteofrance.fr> and SYNOP open data archives <https://meteo.data.gouv.fr/datasets/686f8595b351c06a3a790867>. It provides functions to authenticate, list stations, retrieve station metadata, request data, download files, and import data directly into R'.

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.

The sample mean and standard deviation are two commonly used statistics in meta-analyses, but some trials use other summary statistics such as the median and quartiles to report the results. Therefore, researchers need to transform those information back to the sample mean and standard deviation. This package implemented sample mean estimators by Luo et al. (2016) <arXiv:1505.05687>, sample standard deviation estimators by Wan et al. (2014) <arXiv:1407.8038>, and the best linear unbiased estimators (BLUEs) of location and scale parameters by Yang et al. (2018, submitted) based on sample quantiles derived summaries in a meta-analysis.

This package creates and manages a PostgreSQL database suitable for storing fisheries data and aggregating ready for use within a Gadget <https://gadget-framework.github.io/gadget2/> model. See <https://mareframe.github.io/mfdb/> for more information.

This package provides a web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the mlr3 framework.

Load, validate, and manipulate Clinical Data Interchange Standards Consortium ('CDISC') Analysis Data Model ('ADaM') dataset metadata stored as YAML files. Metadata files are validated against a JSON schema. Provides functions to inspect and modify columns, parameters, and row-level operations within and across ADaM domains. Designed for use with the mighty framework.

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.

Easily import the MI-SUVI data sets. The user can import data sets with full metrics, percentiles, Z-scores, or rankings. Data is available at both the County and Zip Code Tabulation Area (ZCTA) levels. This package also includes a function to import shape files for easy mapping and a function to access the full technical documentation. All data is sourced from the Michigan Department of Health and Human Services.