Helpers for preparing ecommerce product photo planning tables,
    prompt sheets, and public ProductShot AI workflow URLs. The package works
    offline and focuses on data-frame preparation for product image batches.

Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations.
    More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.

Programs to determine student grades and create
  examinations from Question banks.  Programs will create numerous
  multiple choice exams, randomly shuffled, for different versions of same question list.

Computes profile extrema functions for arbitrary functions. If the function is expensive-to-evaluate it computes profile extrema by emulating the function with a Gaussian process (using package 'DiceKriging'). In this case uncertainty quantification on the profile extrema can also be computed. The different plotting functions for profile extrema give the user a tool to better locate excursion sets.

Like similar profiling tools,
  the 'proffer' package automatically detects
  sources of slowness in R code.
  The distinguishing feature of 'proffer' is its utilization of
  'pprof', which supplies interactive visualizations
  that are efficient and easy to interpret.
  Behind the scenes, the 'profile' package converts
  native Rprof() data to a protocol buffer
  that 'pprof' understands.
  For the documentation of 'proffer',
  visit <https://r-prof.github.io/proffer/>.
  To learn about the implementations and methodologies of
  'pprof', 'profile', and protocol buffers,
  visit <https://github.com/google/pprof>.
  <https://protobuf.dev>,
  and <https://github.com/r-prof/profile>, respectively.

Defines a data structure for profiler data, and methods to read and
    write from the 'Rprof' and 'pprof' file formats.

Provides tools for profiling a user-supplied log-likelihood 
    function to calculate confidence intervals for model parameters. Speed of 
    computation can be improved by adjusting the step sizes in the profiling 
    and/or starting the profiling from limits based on the approximate large 
    sample normal distribution for the maximum likelihood estimator of a 
    parameter. The accuracy of the limits can be set by the user. A plot method 
    visualises the log-likelihood and confidence interval. Cases where the 
    profile log-likelihood flattens above the value at which a confidence limit
    is defined can be handled, leading to a limit at plus or minus infinity.
    Disjoint confidence intervals will not be found.