btb - Beyond the Border - Kernel Density Estimation for Urban Geography

The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid. The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid. The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid. The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid. Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.

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statistical-packagecpp

6.64 score 18 stars 15 scripts 365 downloads

disaggR - Two-Steps Benchmarks for Time Series Disaggregation

The twoStepsBenchmark() and threeRuleSmooth() functions allow you to disaggregate a low-frequency time series with higher frequency time series, using the French National Accounts methodology. The aggregated sum of the resulting time series is strictly equal to the low-frequency time series within the benchmarking window. Typically, the low-frequency time series is an annual one, unknown for the last year, and the high frequency one is either quarterly or monthly. See "Methodology of quarterly national accounts", Insee Méthodes N°126, by Insee (2012, ISBN:978-2-11-068613-8, <https://www.insee.fr/en/information/2579410>).

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disaggregationstatistical-packagetime-series

5.80 score 11 stars 29 scripts 304 downloads

rjd3production - Prepare for Production of Seasonal Adjustment with 'JDemetra+'

A comprehensive tool for setting up seasonal data pipelines using 'JDemetra+' (version 3) and 'rjdverse'. This includes setting up a new working environment, creating and selecting calendar regressors, managing specifications (trading-days regressors and outliers) at the workspace level, making a workspace usable by the 'cruncher', removing insignificant outliers, and comparing workspaces.

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jdemetraseasonal-adjustmentseasonalityquartoopenjdk

5.18 score 436 downloads

JDCruncheR - Interface Between the 'JDemetra+' Cruncher and R, and Quality Report Generator

Tool for generating quality reports from cruncher outputs (and calculating series scores). The latest version of the cruncher can be downloaded here: <https://github.com/jdemetra/jwsacruncher/releases>.

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extensionjdemetrajwsacruncherquality-assessment

4.78 score 5 stars 16 scripts 139 downloads

gustave - A User-Oriented Statistical Toolkit for Analytical Variance Estimation

Provides a toolkit for analytical variance estimation in survey sampling. Apart from the implementation of standard variance estimators, its main feature is to help the sampling expert produce easy-to-use variance estimation "wrappers", where systematic operations (linearization, domain estimation) are handled in a consistent and transparent way.

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official-statisticssamplingsurvey-samplingvariance-estimation

3.91 score 9 stars 18 scripts 275 downloads