Diagnostics of list of codes based on concepts from the domains measurement and observation. This package works for data mapped to the Observational Medical Outcomes Partnership Common Data Model.

Send server-side tracking data from R.
  The Measurement Protocol version 2 
  <https://developers.google.com/analytics/devguides/collection/protocol/ga4>
  allows sending HTTP tracking events from R code.

Collection of tools to make working with physical measurements
		easier. Convert between metric and imperial units, or calculate a dimension's
		unknown value from other dimensions' measurements.

'Provides an interactive toolkit for educational and psychological measurement implemented using the 'shiny' framework. The package supports content validity analysis, dimensionality assessment, and Classical Test Theory using the 'CTT' package (Willse, 2018) <doi:10.32614/CRAN.package.CTT>. Item Response Theory (IRT) analyses are conducted via 'mirt' (Chalmers, 2012) <doi:10.18637/jss.v048.i06>. Exploratory Factor Analysis is performed using 'psych' (Revelle, 2025), while Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) are based on the 'lavaan' framework (Rosseel, 2012) <doi:10.18637/jss.v048.i02>. The CFA/SEM module features interactive model specification, automatic model comparison, modification indices, comprehensive fit diagnostics, path diagram visualization, and HTML report generation. The application allows users to upload data, evaluate statistical models, visualize results, and export outputs through an intuitive graphical interface without requiring programming experience.

Provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the 'mlr' package and were programed by several 'mlr' developers.

Identification of ring borders on scanned image sections from dendrochronological samples. Processing of image reflectances to produce gray matrices and time series of smoothed gray values. Luminance data is plotted on segmented images for users to perform both: visual identification of ring borders or control of automatic detection. Routines to visually include/exclude ring borders on the R graphical devices, or automatically detect ring borders using a linear detection algorithm. This algorithm detects ring borders according to positive/negative extreme values in the smoothed time-series of gray values. Most of the in-package routines can be recursively implemented using the multiDetect() function.