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r-msmtools
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A fast and general method for restructuring classical longitudinal data into augmented ones. The reason for this is to facilitate the modeling of longitudinal data under a multi-state framework using the 'msm' package.
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2025-04-22 |
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r-msigdbr
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Provides the 'Molecular Signatures Database' (MSigDB) gene sets typically used with the 'Gene Set Enrichment Analysis' (GSEA) software (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>, Liberzon et al. 2015 <doi:10.1016/j.cels.2015.12.004>) in a standard R data frame with key-value pairs. The package includes the human genes as listed in MSigDB as well as the corresponding symbols and IDs for frequently studied model organisms such as mouse, rat, pig, fly, and yeast.
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2025-04-22 |
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r-mschart
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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.
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2025-04-22 |
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r-mrds
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Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.
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2025-04-22 |
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r-mr.raps
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Mendelian randomization is a method of identifying and estimating a confounded causal effect using genetic instrumental variables. This packages implements methods for two-sample Mendelian randomization with summary statistics by using Robust Adjusted Profile Score (RAPS). References: Qingyuan Zhao, Jingshu Wang, Jack Bowden, Dylan S. Small. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. <arXiv:1801.09652>.
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2025-04-22 |
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r-mrbin
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A collection of functions for processing and analyzing metabolite data. The namesake function mrbin() converts 1D or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time. The function fia() ranks features according to their impact on classifier models, especially artificial neural network models.
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2025-04-22 |
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r-mpoly
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Symbolic computing with multivariate polynomials in R.
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2025-04-22 |
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r-mplusautomation
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Leverages the R language to automate latent variable model estimation and interpretation using 'Mplus', a powerful latent variable modeling program developed by Muthen and Muthen (<https://www.statmodel.com>). Specifically, this package provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics.
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2025-04-22 |
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r-mosaiccalc
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Software to support the introductory *MOSAIC Calculus* textbook <https://www.mosaic-web.org/MOSAIC-Calculus/>), one of many data- and modeling-oriented educational resources developed by Project MOSAIC (<https://www.mosaic-web.org/>). Provides symbolic and numerical differentiation and integration, as well as support for applied linear algebra (for data science), and differential equations/dynamics. Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc. The software is suitable for general use, but intended mainly for teaching calculus.
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2025-04-22 |
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r-mosaiccore
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Common utilities used in other MOSAIC-family packages are collected here.
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2025-04-22 |
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r-mosaic
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Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.
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2025-04-22 |
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r-moonbook
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Several analysis-related functions for the book entitled "R statistics and graph for medical articles" (written in Korean), version 1, by Keon-Woong Moon with Korean demographic data with several plot functions.
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2025-04-22 |
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r-mortalitylaws
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Fit the most popular human mortality 'laws', and construct full and abridge life tables given various input indices. A mortality law is a parametric function that describes the dying-out process of individuals in a population during a significant portion of their life spans. For a comprehensive review of the most important mortality laws see Tabeau (2001) <doi:10.1007/0-306-47562-6_1>. Practical functions for downloading data from various human mortality databases are provided as well.
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2025-04-22 |
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r-monitor
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Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.
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2025-04-22 |
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r-modistsp
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Allows automating the creation of time series of rasters derived from MODIS satellite land products data. It performs several typical preprocessing steps such as download, mosaicking, reprojecting and resizing data acquired on a specified time period. All processing parameters can be set using a user-friendly GUI. Users can select which layers of the original MODIS HDF files they want to process, which additional quality indicators should be extracted from aggregated MODIS quality assurance layers and, in the case of surface reflectance products, which spectral indexes should be computed from the original reflectance bands. For each output layer, outputs are saved as single-band raster files corresponding to each available acquisition date. Virtual files allowing access to the entire time series as a single file are also created. Command-line execution exploiting a previously saved processing options file is also possible, allowing users to automatically update time series related to a MODIS product whenever a new image is available. For additional documentation refer to the following article: Busetto and Ranghetti (2016) <doi:10.1016/j.cageo.2016.08.020>.
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2025-04-22 |
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r-modistools
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Programmatic interface to the Oak Ridge National Laboratories 'MODIS Land Products Subsets' web services (<https://modis.ornl.gov/data/modis_webservice.html>). Allows for easy downloads of 'MODIS' time series directly to your R workspace or your computer.
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2025-04-22 |
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r-moderndive
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Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.
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2025-04-22 |
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r-modest
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User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression.
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2025-04-22 |
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r-modeltime.resample
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A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis.
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2025-04-22 |
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r-modeltime.ensemble
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A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability. Refer to papers such as "Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) <doi:10.3390>.
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2025-04-22 |
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r-modelsummary
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Create beautiful and customizable tables to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and correlation matrices. This package supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr' dynamic documents. Details can be found in Arel-Bundock (2022) <doi:10.18637/jss.v103.i01>.
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2025-04-22 |
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r-modeltests
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Provides a number of testthat tests that can be used to verify that tidy(), glance() and augment() methods meet consistent specifications. This allows methods for the same generic to be spread across multiple packages, since all of those packages can make the same guarantees to users about returned objects.
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2025-04-22 |
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r-modeltime
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The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).
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2025-04-22 |
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r-modelenv
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An developer focused, low dependency package in 'tidymodels' that provides functions to register how models are to be used. Functions to register models are complimented with accessor functions to retrieve registered model information to aid in model fitting and error handling.
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2025-04-22 |
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r-modelmap
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Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.
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2025-04-22 |
