Statistical Matching or Data Fusion

Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing with data from complex sample surveys are available too.

Detection of Univariate Outliers

Provides well-known techniques for detecting univariate outliers. Methods for handling skewed distributions are included. The Hidiroglou-Berthelot (1986) method for detecting outliers in ratios of historical data is also implemented. When available, survey weights can be incorporated in the detection process.

Miscellaneous Functions from Alexey Shipunov

A collection of functions for data manipulation, plotting and statistical computing, to use separately or with the book "Visual Statistics. Use R!": Shipunov (2020) < http://ashipunov.info/shipunov/software/r/r-en.htm>. Dr Alexey Shipunov died in December 2022. Most useful functions: Bclust(), Jclust() and BootA() which bootstrap hierarchical clustering; Recode() which does multiple recoding in a fast, simple and flexible way; Misclass() which outputs confusion matrix even if classes are not concerted; Overlap() which measures group separation on any projection; Biarrows() which converts any scatterplot into biplot; and Pleiad() which is fast and flexible correlogram.

Mixed FLP and ML Estimation of ETAS Space-Time Point Processes for Earthquake Description

Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; in version 2.3.0 improved update method. Chiodi, Adelfio (2017).

Statistical Methods for Analytical Method Comparison and Validation

Provides statistical methods for analytical method comparison and validation studies. Implements Bland-Altman analysis for assessing agreement between measurement methods (Bland & Altman (1986) ), Passing-Bablok regression for non-parametric method comparison (Passing & Bablok (1983) ), and Deming regression accounting for measurement error in both variables (Linnet (1993) ). Also includes tools for setting quality goals based on biological variation (Fraser & Petersen (1993) ) and calculating Six Sigma metrics, precision experiments with variance component analysis, precision profiles for functional sensitivity estimation (Kroll & Emancipator (1993) < https://pubmed.ncbi.nlm.nih.gov/8448849/>). Commonly used in clinical laboratory method validation. Provides publication-ready plots and comprehensive statistical summaries.

Code and Obtain Customized Planned Comparisons with 'appRiori'

With 'appRiori' , users upload the research variables and the app guides them to the best set of comparisons fitting the hypotheses, for both main and interaction effects. Through a graphical explanation and empirical examples on reproducible data, it is shown that it is possible to understand both the logic behind the planned comparisons and the way to interpret them when a model is tested.