R-universe - leilamarvian (Leilamarvian)

AutoGenAI - Adaptive Optimization of Prompts, Models and Generation Strategies

Provides provider-agnostic tools for jointly comparing and optimizing prompts, language-model providers, and generation strategies for generative artificial intelligence workflows. Candidate configurations can be evaluated using user-supplied scoring functions, cost and latency measurements, robustness perturbations, Pareto-front screening, budget and latency constraints, prompt evolution, adaptive routing, self-consistency, and text-output ensembles. The core workflow is designed to run offline with deterministic mock providers, while external model application programming interfaces can be connected through user-defined provider functions. Evolutionary search concepts are described by Goldberg (1989, ISBN:0201157675), and multi-objective optimization concepts are related to Deb, Pratap, Agarwal and Meyarivan (2002) <doi:10.1109/4235.996017>.

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2.70 score 456 downloads

interSAE - Intersectional Small Area Estimation from Survey and Census Data

Implements design-aware, margin-constrained estimation of population indicators for geographic areas crossed with demographic subgroups. Complex-survey microdata supply outcomes and association information, while one or more aggregate census tables supply overlapping population margins. A generalized iterative proportional fitting engine reconstructs a coherent latent population table, and an augmented model-assisted estimator produces domain means, proportions, and totals for sampled and unsampled intersections. Tools diagnose non-identification, compute linear-programming sensitivity bounds, propagate sampling and model uncertainty with replicate-weight or multiplier bootstrap procedures, enforce structural zeros, and benchmark estimates to official totals. The framework extends calibration ideas from Deville and Sarndal (1992) <doi:10.1080/01621459.1992.10475217> and small area estimation ideas from Fay and Herriot (1979) <doi:10.1080/01621459.1979.10482505>.

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2.70 score 215 downloads

compIndexBuilder - Composite Index Builder & Analytics 'shiny' App

Provides an interactive 'shiny' application for constructing, analysing, comparing, and visualising composite indices from tabular multidimensional data. Supports multi-sheet 'Excel' workbooks with active-sheet selection, refresh controls, per-sheet and workbook-wide exports, automatic reshaping of wide indicator-year columns such as 'IN1-2019' into panel form, configurable missing-value code handling, indicator direction and normalisation controls, equal and custom weighting, entity-level ranking, time-series analysis and forecasting, entity comparisons, pillar-based sub-indices with equal, custom, correlation-based, or principal-component weights, and diagnostic tools including internal-consistency reliability assessment, coefficient of variation, principal component analysis, sensitivity analysis, correlation heatmaps, and weighted flow visualizations.

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2.18 score 561 downloads

AI4OfficialStats - Audit Statistical Fidelity of AI-Mediated Official Statistics

Provides deterministic tools for auditing whether artificial intelligence systems preserve the numerical, semantic, contextual, temporal, geographic, unit, provenance, revision, transformation, and uncertainty properties of official statistics. Structured reference statistics and machine-generated claims can be compared using non-compensatory critical-error rules, weakest-link and geometric fidelity summaries, provenance graphs, and portable SHA-256 proof bundles. The package provides bounded connectors for official statistical services, an easy schema-detection and file-import layer for arbitrary official organisations, extensible provider registries, and a search-first natural- language verification layer that classifies statistical claims, selects suitable official sources, retrieves candidate evidence, matches statistical dimensions, and compares claimed values. If no reference year is stated, verification uses the latest available matching official observation and discloses the resolved year. Source attribution is optional: automatic routing can choose suitable providers when none is named, while explicitly named supported sources are respected by default. Automatic catalogue-to-observation verification is implemented for the World Bank, WHO, the United Nations Statistics Division Sustainable Development Goals service, and the European Commission statistical service, while other providers remain available through bounded direct connectors or generic official-data import. Prompt perturbation, statistical red-team generation, minimal-pair tests, and benchmark data support reproducible evaluation of generative, retrieval-augmented, and agentic statistical systems. An embedded alignment layer maps claim-level controls to relevant activities of the Generic Statistical Business Process Model (GSBPM) 5.2, including Analyse, Disseminate, Evaluate, Quality Management, and Metadata Management.

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1.70 score 31 downloads

statfidelity - Audit Statistical Fidelity of AI-Mediated Official Statistics

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1.70 score 319 downloads

qtsa - Quantum Time Series Analysis: Drift, Noise Spectroscopy and Calibration Forecasting

Tools for exploratory statistical analysis of quantum-hardware calibration time series. The package provides simulators for random telegraph noise (RTN), power-law noise, and Ornstein-Uhlenbeck dephasing; Welch and sine-multitaper power spectral density estimators; a lightweight two-state hidden Markov model for switching signals; cumulative sum (CUSUM) and binary-segmentation diagnostics for calibration drift; residual-quantile interval forecasts; and filter-function calculations for illustrative coherence curves. The package includes a reproducible generator of simulated superconducting-qubit calibration records; it does not retrieve authenticated live provider data. Methodological background is provided by Welch (1967) <doi:10.1109/TAU.1967.1161901>, Thomson (1982) <doi:10.1109/PROC.1982.12433>, Rabiner (1989) <doi:10.1109/5.18626>, Page (1954) <doi:10.1093/biomet/41.1-2.100>, Paladino et al. (2014) <doi:10.1103/RevModPhys.86.361>, and Cywinski et al. (2008) <doi:10.1103/PhysRevB.77.174509>.

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1.70 score 197 downloads

DisasterAlert - Disaster Alert and Sentiment Analysis

By systematically aggregating and processing textual reports from earthquakes, floods, storms, wildfires, and other natural disasters, the framework enables a holistic assessment of crisis narratives. Intelligent cleaning and normalization techniques transform raw commentary into structured data, ensuring precise extraction of disaster-specific insights. Collective sentiments of affected communities are quantitatively scored and qualitatively categorized, providing a multifaceted view of societal responses under duress. Interactive geographic maps and temporal charts illustrate the evolution and spatial dispersion of emotional reactions and impact indicators.

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1.00 score 230 downloads

WeatherSentiment - Comprehensive Analysis of Tweet Sentiments and Weather Data

A comprehensive suite of functions for processing, analyzing, and visualizing textual data from tweets is offered. Users can clean tweets, analyze their sentiments, visualize data, and examine the correlation between sentiments and environmental data such as weather conditions. Main features include text processing, sentiment analysis, data visualization, correlation analysis, and synthetic data generation. Text processing involves cleaning and preparing tweets by removing textual noise and irrelevant words. Sentiment analysis extracts and accurately analyzes sentiments from tweet texts using advanced algorithms. Data visualization creates various charts like word clouds and sentiment polarity graphs for visual representation of data. Correlation analysis examines and calculates the correlation between tweet sentiments and environmental variables such as weather conditions. Additionally, random tweets can be generated for testing and evaluating the performance of analyses, empowering users to effectively analyze and interpret 'Twitter' data for research and commercial purposes.

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1.00 score 236 downloads

TimeVizPro - Dynamic Data Explorer: Visualize and Forecast with 'TimeVizPro'

Unleash the power of time-series data visualization with ease using our package. Designed with simplicity in mind, it offers three key features through the 'shiny' package output. The first tab shows time- series charts with forecasts, allowing users to visualize trends and changes effortlessly. The second one displays Averages per country presented in tables with accompanying sparklines, providing a quick and attractive overview of the data. The last tab presents A customizable world map colored based on user-defined variables for any chosen number of countries, offering an advanced visual approach to understanding geographical data distributions. This package operates with just a few simple arguments, enabling users to conduct sophisticated analyses without the need for complex programming skills. Transform your time-series data analysis experience with our user-friendly tool.

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1.00 score 279 downloads

GISINTEGRATION - GIS Integration

Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases <https://www.sciencedirect.com/topics/computer-science/gis-database>, the R package 'GISINTEGRATION' offers a robust solution for efficiently preparing GIS data for advanced spatial analyses. This package excels in simplifying intrica procedures like data cleaning, normalization, and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.

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1.00 score 251 downloads

ADTSA - Time Series Analysis

Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated intervals and estimates partial autocorrelations using Durbin-Levinson. This package calculates the autocorrelation power spectrum, computes cross-correlations between two time series, computes bandwidth for any time series, and performs autocorrelation frequency analysis. It also calculates the periodicity of a time series.

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1.00 score 1 stars 284 downloads

PreProcessRecordLinkage - Preprocessing Record Linkage

In this record linkage package, data preprocessing has been meticulously executed to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. This approach facilitates seamless data integration and analysis across various datasets. While users have the flexibility to modify variable names, the system intelligently ensures that changes are only permitted when they do not compromise data consistency or essential variable essence.

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1.00 score 214 downloads

Hassani.SACF - Computing Lower Bound of Ljung-Box Test

The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. The Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to the number of lags involved in the test and therefore it should be used with extra caution. The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) <doi:10.1016/j.physa.2018.12.028>.

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1.00 score 284 downloads

Hassani.Silva - A Test for Comparing the Predictive Accuracy of Two Sets of Forecasts

A non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a statistically significant difference between the distribution of forecast errors, and secondly it exploits the principles of stochastic dominance to determine whether the forecasts with the lower error also reports a stochastically smaller error than forecasts from a competing model, and thereby enables distinguishing between the predictive accuracy of forecasts. KSPA test has been described in : Hassani and Silva (2015) <doi:10.3390/econometrics3030590>.

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1.00 score 1 stars 169 downloads