Package to query the Twitter Academic Research Product Track,
    providing access to full-archive search and other v2 API endpoints. Functions
    are written with academic research in mind. They provide flexibility in how 
    the user wishes to store collected data, and encourage regular storage of data
    to mitigate loss when collecting large volumes of tweets. They also provide
    workarounds to manage and reshape the format in which data is provided on
    the client side.

Processes accelerometer data from uni-axial and tri-axial devices,
    and generates data summaries. Also includes functions to plot, analyze, and
    simulate accelerometer data.

It performs Canonical Correlation Analysis and provides inferential guaranties on the correlation components. The p-values are computed following the resampling method developed in Winkler, A. M., Renaud, O., Smith, S. M., & Nichols, T. E. (2020). Permutation inference for canonical correlation analysis. NeuroImage, <doi:10.1016/j.neuroimage.2020.117065>. Furthermore, it provides plotting tools to visualize the results.

A collection of functions that perform operations on time-series accelerometer data, such as identify non-wear time, flag minutes that are part of an activity bout, and find the maximum 10-minute average count value. The functions are generally very flexible, allowing for a variety of algorithms to be implemented. Most of the functions are written in C++ for efficiency.

We present a statistical method for imputing missing values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputation under the zero-inflated Poisson lognormal model. It also offers several functions to preprocess accelerometer data before imputation. These include detecting wear and non-wear time, selecting valid days and subjects, and generating plots.

Estimate the Šesták–Berggren kinetic model (degradation model) from experimental data.
  A closed-form (analytic) solution to the degradation model is implemented as a non-linear fit,
  allowing for the extrapolation of the degradation of a drug product - both in time and 
  temperature. Parametric bootstrap, with kinetic parameters drawn from the multivariate
  t-distribution, and analytical formulae (the delta method) are available options to calculate
  the confidence and prediction intervals.
  The results (modelling, extrapolations and statistical intervals) can be visualised
  with multiple plots. The examples illustrate the accelerated stability modelling in drugs and
  vaccines development.