Survey Data Cleaning, Weighting and Analysis

Provides utilities for cleaning survey data, computing weights, and performing descriptive statistical analysis. Methods follow Lohr (2019, ISBN:978-0367272454) "Sampling: Design and Analysis" and Lumley (2010) .

Data Cleaning Center for Survey and Assessment Data

Rule-driven, auditable cleaning of survey and assessment response data, implementing the WeianData Detect-Execute-Report workflow. Provides a multi-format, multi-encoding input layer (CSV, 'Excel', 'SPSS', 'Stata', 'SAS', Parquet, JSON), the dcc_data container with a provenance chain, level-0 structural diagnostics, five built-in response-quality detectors (missing items, straight-lining, response time, trap items, score anomalies), a declarative YAML rule engine, an execution engine with a cell-level audit log, answer-key scoring, multi-form to master item bank mapping, a normalized report model rendered as bilingual staff workbooks and HTML, complete statistical bundles, and versioned machine JSON/JSONL with findings-to-changes reconciliation, cell-level lineage tracing, and manifest-based one-command reproduction. Includes a protected bilingual strict project workbook and matching JSON contract with cell-addressed validation, non-mutating preflight, preview-first execution, and localized staff guidance. All formally supported input backends install with the package; PDF is optional rather than a fixed report output.

Surveillance Data Cleaning and Preparation for Public Health

Clean, prepare, and aggregate surveillance data for public health analysis. Provides structural data cleaning and standardisation (clean_the_nest()), age categorisation against ~50 published schemes with publication-ready labelling (preening()), time-unit aggregation with zero-filling and seasonal awareness (roost()), joint aggregation of several linked event dates (e.g. onset, admission, ICU, complication, fatality) into one table of comparable rate columns (flyway()), under-ascertainment correction via a stratified, time-varying multiplier factor supplied directly, derived by the ratio (multiplier) method, or derived by inverting an externally sourced severity rate (e.g. an infection-fatality-rate anchor) against an observed severity ratio (corncrake()), comorbidity detection from ICD-10-AM clinical coding (plumage()), vaccine coverage data construction (brood()), hash-based de-identification (molting()), and relinking of previously de-identified data (homing()). brood() produces a brood_df object supporting two population models: pre-aggregated denominators (population_model = "pre_aggregated") and record-level cohort designs (population_model = "cohort"). The cohort model handles single time-point coverage snapshots, interrupted time series analysis via a built-in sweep returning monthly coverage rates (time_series = TRUE), and birth cohort designs with person-time computation. This cohort/time-series coverage model was applied in Roughan et al. (2026) to estimate infant immunisation coverage against respiratory syncytial virus over an 18-month period. Both wide format (one row per person with dose columns, from 'starling'::murmuration()) and long format (one row per dose) are accepted. corncrake() returns both a point-corrected count and uncertainty bounds wherever they can be derived, including the inverse relationship between a severity-anchored factor and the bounds of its own reference rate. Built for Australian public health surveillance practice but not specific to it -- see individual function documentation for notes on non-Australian use (e.g. Northern Hemisphere season boundaries).

Scrubbing and Other Data Cleaning Routines for fMRI

Data-driven fMRI denoising with projection scrubbing (Pham et al (2022) ). Also includes routines for DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) ), motion scrubbing (Power et al (2012) ), aCompCor (anatomical Components Correction) (Muschelli et al (2014) ), detrending, and nuisance regression. Projection scrubbing is also applicable to other outlier detection tasks involving high-dimensional data.

Clean Data Frames

Provides a friendly interface for modifying data frames with a sequence of piped commands built upon the 'tidyverse' Wickham et al., (2019) . The majority of commands wrap 'dplyr' mutate statements in a convenient way to concisely solve common issues that arise when tidying small to medium data sets. Includes smart defaults and allows flexible selection of columns via 'tidyselect'.

LLM-Assisted Data Cleaning with Multi-Provider Support

Detects and suggests fixes for semantic inconsistencies in data frames by calling large language models (LLMs) through a unified, provider-agnostic interface. Supported providers include 'OpenAI' ('GPT-4o', 'GPT-4o-mini') < https://platform.openai.com>, 'Anthropic' ('Claude') < https://www.anthropic.com>, 'Google' ('Gemini') < https://ai.google.dev>, 'Groq' (free-tier 'LLaMA' and 'Mixtral') < https://groq.com>, and local 'Ollama' models < https://ollama.com>. The package identifies issues that rule-based tools cannot detect: abbreviation variants, typographic errors, case inconsistencies, and malformed values. Results are returned as tidy data frames with column, row index, detected value, issue type, suggested fix, and confidence score. An offline fallback using statistical and fuzzy-matching methods is provided for use without any application programming interface (API) key. Interactive fix application with human review is supported via 'apply_fixes()'. Methods follow de Jonge and van der Loo (2013) < https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf> and Chaudhuri et al. (2003) .

A User-Friendly Biodiversity Data Cleaning App for the Inexperienced R User

Provides features to manage the complete workflow for biodiversity data cleaning. Uploading data, gathering input from users (in order to adjust cleaning procedures), cleaning data and finally, generating various reports and several versions of the data. Facilitates user-level data cleaning, designed for the inexperienced R user. T Gueta et al (2018) . T Gueta et al (2017) .

Weighted Correlation Network Analysis

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) . Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.

Clean and Standardize Epidemiological Data

Cleaning and standardizing tabular data package, tailored specifically for curating epidemiological data. It streamlines various data cleaning tasks that are typically expected when working with datasets in epidemiology. It returns the processed data in the same format, and generates a comprehensive report detailing the outcomes of each cleaning task.

Cleaning Text Data with an AI Assistant

Provides functions to clean and standardize messy data, including textual categories and free-text addresses, using Large Language Models. The package corrects typos, expands abbreviations, and maps inconsistent entries to standardized values. Ideal for Bioinformatics, business, and general data cleaning tasks.