Created for population health analytics and monitoring. The functions in this package work best when working with patient level Master Patient Index-like datasets . Built to be used by NHS bodies and other health service providers.

This package implements the SparseStep model for solving regression problems with a sparsity constraint on the parameters. The SparseStep regression model was proposed in Van den Burg, Groenen, and Alfons (2017) <arXiv:1701.06967>. In the model, a regularization term is added to the regression problem which approximates the counting norm of the parameters. By iteratively improving the approximation a sparse solution to the regression problem can be obtained. In this package both the standard SparseStep algorithm is implemented as well as a path algorithm which uses golden section search to determine solutions with different values for the regularization parameter.

Analysis and plotting tools for snow profile data produced from manual snowpack observations and physical snowpack models. The functions in this package support snowpack and avalanche research by reading various formats of data (including CAAML, SMET, generic csv, and outputs from the snow cover model SNOWPACK), manipulate the data, and produce graphics such as stratigraphy and time series profiles. Package developed by the Simon Fraser University Avalanche Research Program <http://www.avalancheresearch.ca>. Graphics apply visualization concepts from Horton, Nowak, and Haegeli (2020, <doi:10.5194/nhess-20-1557-2020>).

Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <DOI:10.1086/519795>, Chang et al. 2015 <DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.

This package provides tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package secr'.

Gain seamless access to origin-destination (OD) data from the Spanish Ministry of Transport, hosted at <https://www.transportes.gob.es/ministerio/proyectos-singulares/estudios-de-movilidad-con-big-data/opendata-movilidad>. This package simplifies the management of these large datasets by providing tools to download zone boundaries, handle associated origin-destination data, and process it efficiently with the duckdb database interface. Local caching minimizes repeated downloads, streamlining workflows for researchers and analysts. Extensive documentation is available at <https://ropenspain.github.io/spanishoddata/index.html>, offering guides on creating static and dynamic mobility flow visualizations and transforming large datasets into analysis-ready formats.

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>, Breiman, L. (1996), <doi:10.1007/BF00058655>, Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>, Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>, Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).

This package provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

This package provides a tool for working with SQLite databases. SQLite has some idiosyncrasies and limitations that impose some hurdles to the R developer who is using this database as a repository. For instance, SQLite doesn't have a date type and sqliteutils has some functions to deal with that.

Data simulator including genotype, phenotype, pedigree, selection and reproduction in R. It simulates most of reproduction process of animals or plants and provides data for GS (Genomic Selection), GWAS (Genome-Wide Association Study), and Breeding. For ADI model, please see Kao C and Zeng Z (2002) <doi:10.1093/genetics/160.3.1243>. For build.cov, please see B. D. Ripley (1987) <ISBN:9780470009604>.

Generates data from R or JAGS code for use in simulation studies. The data are returned as an nlist::nlists object and/or saved to file as individual .rds files. Parallelization is implemented using the future package. Progress is reported using the progressr package.

In the past decade, genome-scale metabolic reconstructions have widely been used to comprehend the systems biology of metabolic pathways within an organism. Different GSMs are constructed using various techniques that require distinct steps, but the input data, information conversion and software tools are neither concisely defined nor mathematically or programmatically formulated in a context-specific manner.The tool that quantitatively and qualitatively specifies each reconstruction steps and can generate a template list of reconstruction steps dynamically selected from a reconstruction step reservoir, constructed based on all available published papers.

Execute the self-controlled case series (SCCS) design using observational data in the OMOP Common Data Model. Extracts all necessary data from the database and transforms it to the format required for SCCS. Age and season can be modeled using splines assuming constant hazard within calendar months. Event-dependent censoring of the observation period can be corrected for. Many exposures can be included at once (MSCCS), with regularization on all coefficients except for the exposure of interest. Includes diagnostics for all major assumptions of the SCCS.

Make interactive d3.js sequence sunburst diagrams in R with the convenience and infrastructure of an htmlwidget'.

Take real or simulated data and salt it with errors commonly found in the wild, such as pseudo-OCR errors, Unicode problems, numeric fields with nonsensical punctuation, bad dates, etc.

Simple utilities to design and generate density functions on bounded regions in space and space-time, and simulate independent, identically distributed data therefrom. See Davies & Lawson (2019) <doi:10.1080/00949655.2019.1575066> for example.

Computes a simple blinding index for randomized controlled trials introduced in Petroff, Bacak, Dagres, Dilk, Wachter: A simple blinding index for randomized controlled trials. Contemp Clin Trials Commun. 2024 Nov 26;42:101393. <doi:10.1016/j.conctc.2024.101393>. PMID: 39686958.

Create a side-by-side view of raster(image)s with an interactive slider to switch between regions of the images. This can be especially useful for image comparison of the same region at different time stamps.

This package contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.

Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. The spinebil package contains methods to evaluate the performance of projection pursuit index functions using tour methods. A paper describing the methods can be found at <doi:10.1007/s00180-020-00954-8>.

This package provides a wrapper for sparse VAR (Vector Autoregression) and VECM (Vector Error Correction Model) time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Basu and Michailidis (2015) <doi:10.1214/15-AOS1315>.

This package provides interface to sparsepp - fast, memory efficient hash map. It is derived from Google's excellent sparsehash implementation. We believe sparsepp provides an unparalleled combination of performance and memory usage, and will outperform your compiler's unordered_map on both counts. Only Google's dense_hash_map is consistently faster, at the cost of much greater memory usage (especially when the final size of the map is not known in advance).

This package creates a data specification that describes the columns of a table (data.frame). Provides methods to read, write, and update the specification. Checks whether a table matches its specification. See specification.data.frame(),read.spec(), write.spec(), as.csv.spec(), respecify.character(), and %matches%.data.frame().

Implementations self-normalization (SN) based algorithms for change-points estimation in time series data. This comprises nested local-window algorithms for detecting changes in both univariate and multivariate time series developed in Zhao, Jiang and Shao (2022) <doi:10.1111/rssb.12552>.