Clean the MS/MS spectrum, calculate spectral entropy, unweighted entropy similarity, and entropy similarity for mass spectrometry data. The entropy similarity is a novel similarity measure for MS/MS spectra which outperform the widely used dot product similarity in compound identification. For more details, please refer to the paper: Yuanyue Li et al. (2021) "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification" <doi:10.1038/s41592-021-01331-z>.

Allows the user to estimate transition probabilities for migratory animals between any two phases of the annual cycle, using a variety of different data types. Also quantifies the strength of migratory connectivity (MC), a standardized metric to quantify the extent to which populations co-occur between two phases of the annual cycle. Includes functions to estimate MC and the more traditional metric of migratory connectivity strength (Mantel correlation) incorporating uncertainty from multiple sources of sampling error. For cross-species comparisons, methods are provided to estimate differences in migratory connectivity strength, incorporating uncertainty. See Cohen et al. (2018) <doi:10.1111/2041-210X.12916>, Cohen et al. (2019) <doi:10.1111/ecog.03974>, Roberts et al. (2023) <doi:10.1002/eap.2788>, and Hostetler et al. (2025) <doi:10.1111/2041-210X.14467> for details on some of these methods.

Compute the multiple Grubbs-Beck low-outlier test on positively distributed data and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow data processing discussed in Cohn et al. (2013) <doi:10.1002/wrcr.20392> and England et al. (2017) <doi:10.3133/tm4B5>.

This package provides functions to access data from public RESTful APIs including REST Countries API', World Bank API', and Nager.Date API', covering Mexico's economic indicators, population statistics, literacy rates, international geopolitical information and official public holidays. The package also includes curated datasets related to Mexico such as air quality monitoring stations, pollution zones, income surveys, postal abbreviations, election studies, forest productivity and demographic data by state. It supports research and analysis focused on Mexico by integrating reliable global APIs with structured national datasets drawn from open and academic sources. For more information on the APIs, see: REST Countries API <https://restcountries.com/>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, and Nager.Date API <https://date.nager.at/Api>.

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.

This package implements an algorithm for computing multiple sparse principal components of a dataset. The method is based on Cory-Wright and Pauphilet "Sparse PCA with Multiple Principal Components" (2022) <doi:10.48550/arXiv.2209.14790>. The algorithm uses an iterative deflation heuristic with a truncated power method applied at each iteration to compute sparse principal components with controlled sparsity.

This package provides the ability to perform "Marginal Mediation"--mediation wherein the indirect and direct effects are in terms of the average marginal effects (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package. This framework allows the use of categorical mediators and outcomes with little change in interpretation from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details on the method.

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. mcbette allows to do a Bayesian model comparison over some site and clock models, using babette (<https://github.com/ropensci/babette/>).

This package provides fast and accurate inference for the parameter estimation problem in Ordinary Differential Equations, including the case when there are unobserved system components. Implements the MAGI method (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) <doi:10.1073/pnas.2020397118>. A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) <doi:10.18637/jss.v109.i04>.

High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach.

This package provides tools to create a layout for figures made of multiple panels, and to fill the panels with base, lattice', ggplot2 and ComplexHeatmap plots, grobs, as well as content from all image formats supported by ImageMagick (accessed through magick').

Fast implementations of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon indicator, inverted generational distance, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) <doi:10.1007/3-540-44719-9_15>, and Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.

This package provides tools for spectral clustering of weighted directed networks using motif adjacency matrices. Methods perform well on large and sparse networks, and random sampling methods for generating weighted directed networks are also provided. Based on methodology detailed in Underwood, Elliott and Cucuringu (2020) <arXiv:2004.01293>.

Values below the limit of detection (LOD) are a problem in several fields of science, and there are numerous approaches for replacing the missing data. We present a new mathematical solution for maximum likelihood estimation that allows us to estimate the true values of the mean and standard deviation for normal distributions and is significantly faster than previous implementations. The article with the details was submitted to JSS and can be currently seen on <https://www2.arnes.si/~tverbo/LOD/Verbovsek_Sega_2_Manuscript.pdf>.

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 tools to solve real-world problems with multiple classes classifications by computing the areas under ROC and PR curve via micro-averaging and macro-averaging. The vignettes of this package can be found via <https://github.com/WandeRum/multiROC>. The methodology is described in V. Van Asch (2013) <https://www.clips.uantwerpen.be/~vincent/pdf/microaverage.pdf> and Pedregosa et al. (2011) <http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html>.

Transforms, calculates, and presents results from the Mental Health Quality of Life Questionnaire (MHQoL), a measure of health-related quality of life for individuals with mental health conditions. Provides scoring functions, summary statistics, and visualization tools to facilitate interpretation. For more details see van Krugten et al.(2022) <doi:10.1007/s11136-021-02935-w>.

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>.

The microplot function writes a set of R graphics files to be used as microplots (sparklines) in tables in either LaTeX', HTML', Word', or Excel files. For LaTeX', we provide methods for the Hmisc::latex() generic function to construct latex tabular environments which include the graphs. These can be used directly with the operating system pdflatex or latex command, or by using one of Sweave', knitr', rmarkdown', or Emacs org-mode as an intermediary. For MS Word', the msWord() function uses the flextable package to construct Word tables which include the graphs. There are several distinct approaches for constructing HTML files. The simplest is to use the msWord() function with argument filetype="html". Alternatively, use either Emacs org-mode or the htmlTable::htmlTable() function to construct an HTML file containing tables which include the graphs. See the documentation for our as.htmlimg() function. For Excel use on Windows', the file examples/irisExcel.xls includes VBA code which brings the individual panels into individual cells in the spreadsheet. Examples in the examples and demo subdirectories are shown with lattice graphics, ggplot2 graphics, and base graphics. Examples for LaTeX include Sweave (both LaTeX'-style and Noweb'-style), knitr', emacs org-mode', and rmarkdown input files and their pdf output files. Examples for HTML include org-mode and Rmd input files and their webarchive HTML output files. In addition, the as.orgtable() function can display a data.frame in an org-mode document. The examples for MS Word (with either filetype="docx" or filetype="html") work with all operating systems. The package does not require the installation of LaTeX or MS Word to be able to write .tex or .docx files.

There are three different modules: (1) model fitting and selection using a set of the most commonly used equations describing developmental responses to temperature helped by already existing R packages ('rTPC') and nonlinear regression model functions from nls.multstart (Padfield et al. 2021, <doi:10.1111/2041-210X.13585>), with visualization of model predictions to guide ecological criteria for model selection; (2) calculation of suitability thermal limits, which consist on a temperature interval delimiting the optimal performance zone or suitability; and (3) climatic data extraction and visualization inspired on previous research (Taylor et al. 2019, <doi:10.1111/1365-2664.13455>), with either exportable rasters, static map images or html, interactive maps.

Provide a suite of functions for conducting and automating Latent Growth Modeling (LGM) in Mplus', including Growth Curve Model (GCM), Growth-Based Trajectory Model (GBTM) and Latent Class Growth Analysis (LCGA). The package builds upon the capabilities of the MplusAutomation package (Hallquist & Wiley, 2018) to streamline large-scale latent variable analyses. âMplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.â Structural Equation Modeling, 25(4), 621â 638. <doi:10.1080/10705511.2017.1402334> The workflow implemented in this package follows the recommendations outlined in Van Der Nest et al. (2020). â An Overview of Mixture Modeling for Latent Evolutions in Longitudinal Data: Modeling Approaches, Fit Statistics, and Software.â Advances in Life Course Research, 43, Article 100323. <doi:10.1016/j.alcr.2019.100323>.

This package provides a companion to the Chinese book ``Modern Statistical Graphics''.

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using emmeans'.

Implementation of the sampling and aggregation method for the covariate shift maximin effect, which was proposed in <arXiv:2011.07568>. It constructs the confidence interval for any linear combination of the high-dimensional maximin effect.