This package provides a collection of commonly used visualizations of temporal and spatio-temporal health data including case counts, incidence rates, and covariates. The available plot types include time series, heatmaps, seasonality plots, maps and more. The package supports standard data transformations such as temporal and spatial aggregations, while offering extensive customization options for the resulting figures.

Read PLINK 1.9 binary datasets (BED/BIM/FAM) and generate the CSV files required by the Erasmus MC HIrisPlex / HIrisPlex-S webtool <https://hirisplex.erasmusmc.nl/>. It maps PLINK alleles to the webtool's required rsID_Allele columns (0/1/2/NA). No external tools (e.g., PLINK CLI') are required.

This package performs hypothesis testing using the interval estimates (e.g., confidence intervals). The non-overlapping interval estimates indicates the statistical significance. References to these procedures can be found at Noguchi and Marmolejo-Ramos (2016) <doi:10.1080/00031305.2016.1200487>, Bonett and Seier (2003) <doi:10.1198/0003130032323>, and Lemm (2006) <doi:10.1300/J082v51n02_05>.

Converts matrices and lists of matrices into a single vector by interleaving their values. That is, each element of the result vector is filled from the input matrices one row at a time. This is the same as transposing a matrix, then removing the dimension attribute, but is designed to operate on matrices in nested list structures.

Computation of linkage disequilibrium of ancestry (LDA) and linkage disequilibrium of ancestry score (LDAS). LDA calculates the pairwise linkage disequilibrium of ancestry between single nucleotide polymorphisms (SNPs). LDAS calculates the LDA score of SNPs. The methods are described in Barrie W, Yang Y, Irving-Pease E.K, et al (2024) <doi:10.1038/s41586-023-06618-z>.

Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), <arXiv:2005.09301>.

Colour palettes and helper functions for visualising Mycobacterium tuberculosis genomic and epidemiological data with ggplot2 and ggtree'. The package provides predefined palettes, scale functions, tree/cladogram helpers, and convenient preview tools to ensure consistent branding in pathogen-omics visualisations. The palettes were developed as part of the mycolorsTB project <https://github.com/PathoGenOmics-Lab/mycolorsTB>.

This package provides streamlined installation for packages from the natverse', a suite of R packages for computational neuroanatomy built on top of the nat NeuroAnatomy Toolbox package. Installation of the complete natverse suite requires a GitHub user account and personal access token GITHUB_PAT'. natmanager will help the end user set this up if necessary.

Summarizes the taxonomic composition, diversity contribution of the rare and abundant community by using OTU (operational taxonomic unit) table which was generated by analyzing pipeline of QIIME or mothur'. The rare biosphere in this package is subset by the relative abundance threshold (for details about rare biosphere please see Lynch and Neufeld (2015) <doi:10.1038/nrmicro3400>).

This package implements an extension of the Chacko chi-square test for ordered vectors (Chacko, 1966, <https://www.jstor.org/stable/25051572>). Our extension brings the Chacko test to the computer age by implementing a permutation test to offer a numeric estimate of the p-value, which is particularly useful when the analytic solution is not available.

This package provides essential checklists for R package developers, whether you're creating your first package or beginning a new project. This tool guides you through each step of the development process, including specific considerations for submitting your package to the Comprehensive R Archive Network (CRAN). Simplify your workflow and ensure adherence to best practices with packagepal'.

Uses provenance post-execution to help the user understand and debug their script by providing functions to look at intermediate steps and data values, their forwards and backwards lineage, and to understand the steps leading up to warning and error messages. provDebugR uses provenance produced by rdtLite (available on CRAN), stored in PROV-JSON format.

Perform simultaneous estimation and variable selection for correlated bivariate mixed outcomes (one continuous outcome and one binary outcome per cluster) using penalized generalized estimating equations. In addition, clustered Gaussian and binary outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported. Cross-validation can be performed to find the optimal regularization parameter(s).

Quasi-Cauchy quantile regression, proposed by de Oliveira, Ospina, Leiva, Figueroa-Zuniga and Castro (2023) <doi:10.3390/fractalfract7090667>. This regression model is useful for the case where you want to model data of a nature limited to the intervals [0,1], (0,1], [0,1) or (0,1) and you want to use a quantile approach.

Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and van der Linden (2018) <doi:10.1201/9781315117430> to construct fixed, adaptive, and parallel tests. Supports the following mixed-integer programming (MIP) solver packages: Rsymphony', highs', gurobi', lpSolve', and Rglpk'. The gurobi package is not available from CRAN; see <https://www.gurobi.com/downloads/>.

An integrated R interface to several United States Census Bureau APIs (<https://www.census.gov/data/developers/data-sets.html>) and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping and spatial analysis.

MSstats package provide tools for preprocessing, summarization and differential analysis of mass spectrometry (MS) proteomics data. Recently, some MS protocols enable acquisition of data sets that result in larger than memory quantitative data. MSstats functions are not able to process such data. MSstatsBig package provides additional converter functions that enable processing larger than memory data sets.

This package implements functions for comparing strings, sequences and numeric vectors for clustering and record linkage applications. Supported comparison functions include: generalized edit distances for comparing sequences/strings, Monge-Elkan similarity for fuzzy comparison of token sets, and L-p distances for comparing numeric vectors. Where possible, comparison functions are implemented in C/C++ to ensure good performance.

Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided.

Causal mediation analysis for a single exposure/treatment and a single mediator, both allowed to be either continuous or binary. The package implements the difference method and provides point and interval estimates as well as testing for the natural direct and indirect effects and the mediation proportion. Nevo, Xiao and Spiegelman (2017) <doi:10.1515/ijb-2017-0006>.

Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or modelled using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.

Facilitates the incorporation of biological processes in biogeographical analyses. It offers conveniences in fitting, comparing and extrapolating models of biological processes such as physiology and phenology. These spatial extrapolations can be informative by themselves, but also complement traditional correlative species distribution models, by mixing environmental and process-based predictors. Caetano et al (2020) <doi:10.1111/oik.07123>.

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.

Scrapes and cleans data from the NHL and ESPN APIs into data.frames and lists. Wraps 125+ endpoints documented in <https://github.com/RentoSaijo/nhlscraper/wiki> from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays.