Probability surveys often use auxiliary continuous data from administrative records, but the utility of this data is diminished when it is discretized for confidentiality. We provide a set of survey estimators to make full use of information from the discretized variables. See Williams, S.Z., Zou, J., Liu, Y., Si, Y., Galea, S. and Chen, Q. (2024), Improving Survey Inference Using Administrative Records Without Releasing Individual-Level Continuous Data. Statistics in Medicine, 43: 5803-5813. <doi:10.1002/sim.10270> for details.

Provides tools for assessing and selecting auxiliary variables using LASSO. 
    The package includes functions for variable selection and diagnostics, facilitating 
    survey calibration analysis with emphasis on robust auxiliary vector selection. For 
    more details see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x> and 
    Caughrey and Hartman (2017) <doi:10.2139/ssrn.3494436>.

Bindings to 'FFmpeg' <http://www.ffmpeg.org/> AV library for working with 
    audio and video in R. Generates high quality video from images or R graphics with 
    custom audio. Also offers high performance tools for reading raw audio, creating
    'spectrograms', and converting between countless audio / video formats. This package 
    interfaces directly to the C API and does not require any command line utilities.

Check if a given package name is available to use. It checks the
  name's validity. Checks if it is used on 'GitHub', 'CRAN' and 'Bioconductor'. Checks
  for unintended meanings by querying 'Wiktionary' and Wikipedia.

Implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.

Average population attributable fractions are calculated for a set
    of risk factors (either binary or ordinal valued) for both prospective and case-
    control designs. Confidence intervals are found by Monte Carlo simulation. The
    method can be applied to either prospective or case control designs, provided an
    estimate of disease prevalence is provided. In addition to an exact calculation
    of AF, an approximate calculation, based on randomly sampling permutations has
    been implemented to ensure the calculation is computationally tractable when the
    number of risk factors is large.