Quickly make tables of descriptive statistics (i.e., counts, 
    means, confidence intervals) for continuous variables. This 
    package is designed to work in a Tidyverse pipeline, and consideration
    has been given to get results from R to 'Microsoft Word' ® with minimal pain.

Estimate diagnostic classification models (also called cognitive
    diagnostic models) with 'Stan'. Diagnostic classification models are 
    confirmatory latent class models, as described by Rupp et al.
    (2010, ISBN: 978-1-60623-527-0). Automatically generate 'Stan' code for the
    general loglinear cognitive diagnostic diagnostic model proposed by
    Henson et al. (2009) <doi:10.1007/s11336-008-9089-5> and other subtypes that
    introduce additional model constraints. Using the generated 'Stan' code,
    estimate the model evaluate the model's performance using model fit indices,
    information criteria, and reliability metrics.

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 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.