Utilize the shiny interface for visualizing results from a pyDarwin (<https://certara.github.io/pyDarwin/>) machine learning pharmacometric model search. It generates Goodness-of-Fit plots and summary tables for selected models, allowing users to customize diagnostic outputs within the interface. The underlying R code for generating plots and tables can be extracted for use outside the interactive session. Model diagnostics can also be incorporated into an R Markdown document and rendered in various output formats.

Implementation of the Coarsened Exact Matching algorithm discussed along with its properties in Iacus, King, Porro (2011) <DOI:10.1198/jasa.2011.tm09599>; Iacus, King, Porro (2012) <DOI:10.1093/pan/mpr013> and Iacus, King, Porro (2019) <DOI:10.1017/pan.2018.29>.

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The cmsaf R-package includes a shiny based interface for an easy application of the cmsafops and cmsafvis packages - the CM SAF R Toolbox. The Toolbox offers an easy way to prepare, manipulate, analyse and visualize CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, longitude and latitude dimension should be applicable, but there is no guarantee for an error-free application. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (<http://www.cmsaf.eu/R_toolbox>).

This package provides datasets containing preformatted maps of Norway at the county, municipality, and ward (Oslo only) level for redistricting in 2024, 2020, 2018, and 2017. Multiple layouts are provided (normal, split, and with an insert for Oslo), allowing the user to rapidly create choropleth maps of Norway without any geolibraries.

Semiparametric estimation for censored time series with lower detection limit. The latent response is a sequence of stationary process with Markov property of order one. Estimation of copula parameter(COPC) and Conditional quantile estimation are included for five available copula functions. Copula selection methods based on L2 distance from empirical copula function are also included.

There are diverse purposes such as biomarker confirmation, novel biomarker discovery, constructing predictive models, model-based prediction, and validation. It handles binary, continuous, and time-to-event outcomes at the sample or patient level. - Biomarker confirmation utilizes established functions like glm() from stats', coxph() from survival', surv_fit(), and ggsurvplot() from survminer'. - Biomarker discovery and variable selection are facilitated by three LASSO-related functions LASSO2(), LASSO_plus(), and LASSO2plus(), leveraging the glmnet R package with additional steps. - Eight versatile modeling functions are offered, each designed for predictive models across various outcomes and data types. 1) LASSO2(), LASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable selection using LASSO methods and construct predictive models based on selected variables. 2) XGBtraining() employs XGBoost for model building and is the only function not involving variable selection. 3) Functions like LASSO2_XGBtraining(), LASSOplus_XGBtraining(), and LASSO2plus_XGBtraining() combine LASSO-related variable selection with XGBoost for model construction. - All models support prediction and validation, requiring a testing dataset comparable to the training dataset. Additionally, the package introduces XGpred() for risk prediction based on survival data, with the XGpred_predict() function available for predicting risk groups in new datasets. The methodology is based on our new algorithms and various references: - Hastie et al. (1992, ISBN 0 534 16765-9), - Therneau et al. (2000, ISBN 0-387-98784-3), - Kassambara et al. (2021) <https://CRAN.R-project.org/package=survminer>, - Friedman et al. (2010) <doi:10.18637/jss.v033.i01>, - Simon et al. (2011) <doi:10.18637/jss.v039.i05>, - Harrell (2023) <https://CRAN.R-project.org/package=rms>, - Harrell (2023) <https://CRAN.R-project.org/package=Hmisc>, - Chen and Guestrin (2016) <doi:10.48550/arXiv.1603.02754>, - Aoki et al. (2023) <doi:10.1200/JCO.23.01115>.

In the context of high-throughput genetic data, CoDaCoRe identifies a set of sparse biomarkers that are predictive of a response variable of interest (Gordon-Rodriguez et al., 2021) <doi:10.1093/bioinformatics/btab645>. More generally, CoDaCoRe can be applied to any regression problem where the independent variable is Compositional (CoDa), to derive a set of scale-invariant log-ratios (ILR or SLR) that are maximally associated to a dependent variable.

This package provides a multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects, based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero effects on which subsets of response variables, motivated and designed for colocalization analysis across genome-wide association studies (GWAS) and quantitative trait loci (QTL) studies. The ColocBoost model is described in Cao et. al. (2025) <doi:10.1101/2025.04.17.25326042>.

Chinese numerals processing in R, such as conversion between Chinese numerals and Arabic numerals as well as detection and extraction of Chinese numerals in character objects and string. This package supports the casual scale naming system and the respective SI prefix systems used in mainland China and Taiwan: "The State Council's Order on the Unified Implementation of Legal Measurement Units in Our Country" The State Council of the People's Republic of China (1984) "Names, Definitions and Symbols of the Legal Units of Measurement and the Decimal Multiples and Submultiples" Ministry of Economic Affairs (2019) <https://gazette.nat.gov.tw/egFront/detail.do?metaid=108965>.

Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows.

This small library contains a series of simple tools for constructing and manipulating confounded and fractional factorial designs.

This package implements algorithms for analyzing Cayley graphs of permutation groups, with a focus on the TopSpin puzzle and similar permutation-based combinatorial puzzles. Provides methods for cycle detection, state space exploration, and finding optimal operation sequences in permutation groups generated by shift and reverse operations.

Intended to analyse recordings from multiple microphones (e.g., backpack microphones in captive setting). It allows users to align recordings even if there is non-linear drift of several minutes between them. A call detection and assignment pipeline can be used to find vocalisations and assign them to the vocalising individuals (even if the vocalisation is picked up on multiple microphones). The tracing and measurement functions allow for detailed analysis of the vocalisations and filtering of noise. Finally, the package includes a function to run spectrographic cross correlation, which can be used to compare vocalisations. It also includes multiple other functions related to analysis of vocal behaviour.

Estimate survival using data mapped to the Observational Medical Outcomes Partnership common data model. Survival can be estimated based on user-defined study cohorts.

P-values and no/lowest observed (adverse) effect concentration values derived from the closure principle computational approach test (Lehmann, R. et al. (2015) <doi:10.1007/s00477-015-1079-4>) are provided. The package contains functions to generate intersection hypotheses according to the closure principle (Bretz, F., Hothorn, T., Westfall, P. (2010) <doi:10.1201/9781420010909>), an implementation of the computational approach test (Ching-Hui, C., Nabendu, P., Jyh-Jiuan, L. (2010) <doi:10.1080/03610918.2010.508860>) and the combination of both, that is, the closure principle computational approach test.

General functions for performing extreme value analysis on a circular domain as part of the statistical methodology in the paper by Konzen, E., Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667.

Plots calibration curves and computes statistics for assessing calibration performance. See Lasai et al. (2025) <doi:10.48550/arXiv.2503.08389>, De Cock Campo (2023) <doi:10.48550/arXiv.2309.08559> and Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>.

This package provides a consistent interface for connecting R to various data sources including file systems and databases. Designed for clinical research, connector streamlines access to ADAM', SDTM for example. It helps to deal with multiple data formats through a standardized API and centralized configuration.

This package provides a covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. More details can be referred to Liu et al. (2024) <doi:10.1093/biomtc/ujae031>.

Compute price indices using various Hedonic and multilateral methods, including Laspeyres, Paasche, Fisher, and HMTS (Hedonic Multilateral Time series re-estimation with splicing). The central function calculate_price_index() offers a unified interface for running these methods on structured datasets. This package is designed to support index construction workflows for real estate and other domains where quality-adjusted price comparisons over time are essential. The development of this package was funded by Eurostat and Statistics Netherlands (CBS), and carried out by Statistics Netherlands. The HMTS method implemented here is described in Ishaak, Ouwehand and Remøy (2024) <doi:10.1177/0282423X241246617>. For broader methodological context, see Eurostat (2013, ISBN:978-92-79-25984-5, <doi:10.2785/34007>).

Pull raw and pre-cleaned versions of national and state-level COVID-19 time-series data from covid19india.org <https://www.covid19india.org>. Easily obtain and merge case count data, testing data, and vaccine data. Also assists in calculating the time-varying effective reproduction number with sensible parameters for COVID-19.

Clique percolation community detection for weighted and unweighted networks as well as threshold and plotting functions. For more information see Farkas et al. (2007) <doi:10.1088/1367-2630/9/6/180> and Palla et al. (2005) <doi:10.1038/nature03607>.

Are you spending too much time fetching and managing clinical trial data? Struggling with complex queries and bulk data extraction? What if you could simplify this process with just a few lines of code? Introducing clintrialx - Fetch clinical trial data from sources like ClinicalTrials.gov <https://clinicaltrials.gov/> and the Clinical Trials Transformation Initiative - Access to Aggregate Content of ClinicalTrials.gov database <https://aact.ctti-clinicaltrials.org/>, supporting pagination and bulk downloads. Also, you can generate HTML reports based on the data obtained from the sources!

This package provides functions for classical test theory analysis, following methods presented by Wu et al. (2006) <doi:10.1007/978-981-10-3302-5>.