An efficient data integration method is provided for multiple spatial transcriptomics data with non-cluster-relevant effects such as the complex batch effects. It unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, requiring only partially shared cell/domain clusters across datasets. More details can be referred to Wei Liu, et al. (2023) <doi:10.1038/s41467-023-35947-w>.

It contains functions to analyze the precipitation
    intensity, concentration and anomaly.

An open-access tool/framework to download, validate, visualize, and
  analyze multi-source precipitation data. More information and an example of implementation can
  be found in Vargas Godoy and Markonis (2023,
  <doi:10.1016/j.envsoft.2023.105711>).

Estimate sample size based on precision rather than power.
    'precisely' is a study planning tool to calculate sample size based on
    precision. Power calculations are focused on whether or not an
    estimate will be statistically significant; calculations of precision
    are based on the same principles as power calculation but turn the
    focus to the width of the confidence interval. 'precisely' is based on
    the work of 'Rothman and Greenland' (2018).

Provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par().

Most of the time floating point arithmetic does
  approximately the right thing.  When adding sums or having products
  of numbers that greatly differ in magnitude, the floating point
  arithmetic may be incorrect.  This package implements the Kahan
  (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum
  <doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from 'NumPy',
  See Castaldo (2008) <doi:10.1137/070679946> for a discussion of
  accuracy), and arbitrary precision sum (adapted from the fsum in
  'Python' ; Shewchuk (1997)
  <https://people.eecs.berkeley.edu/~jrs/papers/robustr.pdf>).  In addition,
  products are changed to long double precision for accuracy, or
  changed into a log-sum for accuracy.