This package provides a collection of small text corpora of interesting data. It contains all data sets from dariusk/corpora'. Some examples: names of animals: birds, dinosaurs, dogs; foods: beer categories, pizza toppings; geography: English towns, rivers, oceans; humans: authors, US presidents, occupations; science: elements, planets; words: adjectives, verbs, proverbs, US president quotes.

Integrated tools to support rigorous and well documented data harmonization based on Maelstrom Research guidelines. The package includes functions to assess and prepare input elements, apply specified processing rules to generate harmonized datasets, validate data processing and identify processing errors, and document and summarize harmonized outputs. The harmonization process is defined and structured by two key user-generated documents: the DataSchema (specifying the list of harmonized variables to generate across datasets) and the Data Processing Elements (specifying the input elements and processing algorithms to generate harmonized variables in DataSchema formats). The package was developed to address key challenges of retrospective data harmonization in epidemiology (as described in Fortier I and al. (2017) <doi:10.1093/ije/dyw075>) but can be used for any data harmonization initiative.

Two tests for the well-specification of the linear instrumental variable model. The first test is based on trying to predict the residuals of a two-stage least-squares regression using a random forest. The second test is robust to weak-identification and based on trying to predict the residuals for a particular candidate parameter and can also be used to construct confidence sets with an Anderson-Rubin-type inversion. Details can be found in Scheidegger, Londschien and Bühlmann (2025) "Machine-learning-powered specification testing in linear instrumental variable models" <doi:10.48550/arXiv.2506.12771>.

Download and parse public files released by B3 and convert them into useful formats and data structures common to data analysis practitioners.

Handle climate data from the DWD ('Deutscher Wetterdienst', see <https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html> for more information). Choose observational time series from meteorological stations with selectDWD()'. Find raster data from radar and interpolation according to <https://brry.github.io/rdwd/raster-data.html>. Download (multiple) data sets with progress bars and no re-downloads through dataDWD()'. Read both tabular observational data and binary gridded datasets with readDWD()'.

Robustness -- eXperimental', eXtraneous', or eXtraordinary Functionality for Robust Statistics. Hence methods which are not well established, often related to methods in package robustbase'. Amazingly, BACON()', originally by Billor, Hadi, and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2> has become established in places. The "barrow wheel" `rbwheel()` is from Stahel and Mächler (2009) <doi:10.1111/j.1467-9868.2009.00706.x>.

Calculates and compares various reliability coefficients for unidimensional and multidimensional scales. Supported unidimensional estimators include coefficient alpha, congeneric reliability, the Gilmer-Feldt coefficient, Feldt's classical congeneric reliability, Hancock's H, Heise-Bohrnstedt's omega, Kaiser-Caffrey's alpha, and Ten Berge and Zegers mu series. Multidimensional estimators include stratified alpha, maximal reliability, correlated factors reliability, second-order factor reliability, and bifactor reliability. See Cho (2021) <doi:10.1007/s11336-021-09801-1>, Cho (2024) <doi:10.1037/met0000475>, Cho (2025) <doi:10.1037/met0000525>.

An ODBC database interface.

This package provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention). Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making. Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be high risk by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies. Curves can be estimated using data from a prospective cohort. In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available. Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.

By placing on a circle 10 points numbered from 1 to 10, and connecting them by a straight line to the point corresponding to its multiplication by 2. (1 must be connected to 1 * 2 = 2, point 2 must be set to 2 * 2 = 4, point 3 to 3 * 2 = 6 and so on). You will obtain an amazing geometric figure that complicates and beautifies itself by varying the number of points and the multiplication table you use.

DBI/RJDBC interface to h2 database. h2 version 2.3.232 is included.

Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.

Modified Poisson, logistic and least-squares regression analyses for binary outcomes of Zou (2004) <doi:10.1093/aje/kwh090>, Noma (2026)<doi:10.1016/j.spl.2026.110698>, and Cheung (2007) <doi:10.1093/aje/kwm223> have been standard multivariate analysis methods to estimate risk ratio and risk difference in clinical and epidemiological studies. This R package involves an easy-to-handle function to implement these analyses by simple commands. Missing data analysis tools (multiple imputation) are also involved. In addition, recent studies have shown the ordinary robust variance estimator possibly has serious bias under small or moderate sample size situations for these methods. This package also provides computational tools to calculate alternative accurate confidence intervals.

An R command interface to the MLwiN multilevel modelling software package.

Mathematical and statistical tools for computational biology in drug discovery. Functions are designed for high performance. Implements the hierarchical fuzzy multi-linkage partitioning method proposed by Huang et al. (2007) <doi:10.1186/gb-2007-8-9-r183>.

This package provides functions to calculate several ecological indices of individual and population niche width (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis W, and Roughgarden's WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be quantified by counts of categories, measures of mass or length, or proportions. Monte Carlo resampling procedures are available for hypothesis testing against multinomial null models. Details are provided in Zaccarelli et al. (2013) <doi:10.1111/2041-210X.12079> and associated references.

Optimization of any Black-Box/Non-Convex Function on Hyper-Rectangular Parameter Space. It uses a Variation of Pattern Search Technique. Described in the paper : Das (2016) <arXiv:1604.08616> .

Uses an indirect method based on truncated quantile-quantile plots to estimate reference limits from routine laboratory data: Georg Hoffmann and colleagues (2024) <doi: 10.3390/jcm13154397>. The principle of the method was developed by Robert G Hoffmann (1963) <doi:10.1001/jama.1963.03060110068020> and modified by Georg Hoffmann and colleagues (2015) <doi:10.1515/labmed-2015-0104>, and Frank Klawonn and colleagues (2020) <doi:10.1515/labmed-2020-0005>, (2022) <doi:10.1007/978-3-031-15509-3_31>.

This package provides methods to easily build requests in the non-standard JSON schema required by the National Institute of Health (NIH)'s RePORTER Project API <https://api.reporter.nih.gov/#/Search/post_v2_projects_search>. Also retrieve and process result sets as either a ragged or flattened tibble'.

Enables researchers to conduct multivariate statistical analyses of survey data with randomized response technique items from several designs, including mirrored question, forced question, and unrelated question. This includes regression with the randomized response as the outcome and logistic regression with the randomized response item as a predictor. In addition, tools for conducting power analysis for designing randomized response items are included. The package implements methods described in Blair, Imai, and Zhou (2015) Design and Analysis of the Randomized Response Technique, Journal of the American Statistical Association <https://graemeblair.com/papers/randresp.pdf>.

Statistical tools based on the probabilistic properties of the record occurrence in a sequence of independent and identically distributed continuous random variables. In particular, tools to prepare a time series as well as distribution-free trend and change-point tests and graphical tools to study the record occurrence. Details about the implemented tools can be found in Castillo-Mateo et al. (2023a) <doi:10.18637/jss.v106.i05> and Castillo-Mateo et al. (2023b) <doi:10.1016/j.atmosres.2023.106934>.

This package provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).

This package provides a robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.

Downloading, customizing, and processing time series of satellite images for a region of interest. rsat functions allow a unified access to multispectral images from Landsat, MODIS and Sentinel repositories. rsat also offers capabilities for customizing satellite images, such as tile mosaicking, image cropping and new variables computation. Finally, rsat covers the processing, including cloud masking, compositing and gap-filling/smoothing time series of images (Militino et al., 2018 <doi:10.3390/rs10030398> and Militino et al., 2019 <doi:10.1109/TGRS.2019.2904193>).