Provides functions to perform the Sequential Probability Ratio Test (SPRT) 
  for hypothesis testing in Binomial, Poisson and Normal distributions. 
  The package allows users to specify Type I and Type II error probabilities, 
  decision thresholds, and compare null and alternative hypotheses sequentially 
  as data accumulate. It includes visualization tools for plotting the likelihood 
  ratio path and decision boundaries, making it easier to interpret results. 
  The methods are based on Wald (1945) <doi:10.1214/aoms/1177731118>, 
  who introduced the SPRT as one of the earliest and most powerful sequential 
  analysis techniques. This package is useful in quality control, clinical trials, 
  and other applications requiring early decision-making.The term 'SPRT' is an abbreviation and used intentionally.

A toolbox for Sequential Probability Ratio Tests (SPRT) based on
    Wald (1945) <doi:10.2134/agronj1947.00021962003900070011x>.
    SPRTs are applied during the sampling process, ideally after each
    observation, and at every stage return a decision to either continue
    sampling or terminate and accept one of the specified hypotheses.
    The `seq_ttest()` function performs one-sample, two-sample, and paired
    t-tests for one- and two-sided hypotheses (Schnuerch & Erdfelder (2019)
    <doi:10.1037/met0000234>). The `seq_anova()` function performs a sequential
    one-way fixed effects ANOVA (Steinhilber et al. (2024)
    <doi:10.1037/met0000677>). The `plan_sample_size()` function helps plan
    sequential studies by simulating required sample sizes across a range of
    effect sizes. For more information, see the vignettes
    browseVignettes(package = "sprtt") or the package website
    <https://meikesteinhilber.github.io/sprtt/>.

Sequential Poisson sampling is a variation of Poisson sampling for
    drawing probability-proportional-to-size samples with a given number of
    units, and is commonly used for price-index surveys. This package gives
    functions to draw stratified sequential Poisson samples according to the
    method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample
    designs by Rosén (1997, <doi:10.1016/S0378-3758(96)00186-3>), and generate
    approximate bootstrap replicate weights according to the generalized
    bootstrap method by Beaumont and Patak
    (2012, <doi:10.1111/j.1751-5823.2011.00166.x>).