Build piecewise exponential survival model for study design (planning) and event/timeline prediction.

A function 'PWI()' that calculates prize winner indices based on bibliometric data is provided. The default is the 'Derek de Solla Price Memorial Medal'. Users can provide recipients of other prizes.

Estimates two-level multilevel linear model and two-level multivariate linear multilevel model with weights following Probability Weighted Iterative Generalised Least Squares approach. For details see Veiga et al.(2014) <doi:10.1111/rssc.12020>.

Power analysis functions along the lines of Cohen (1988).

User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.

Statistical power analysis for designs including t-tests, correlations, 
    multiple regression, ANOVA, mediation, and logistic regression. Functions accompany 
    Aberson (2019) <doi:10.4324/9781315171500>.

Provides tools for calculating statistical power and determining sample size
  for a variety of experimental designs used in agricultural and biological research,
  including completely randomized, block, and split-plot designs. Supports customized
  designs and allows specification of main effects, interactions, and contrasts for
  accurate power analysis.

Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test,
  which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are
  unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give 
  biased results due to using the pooled standard deviation, unlike the Welch's t-test.

This is a package for calculating two differing notions of power, or deriving sample
 sizes for specified requisite power in multiple testing experiments under a variety of methods for
 control of the distribution of the False Discovery Proportion (FDP). More specifically, one can
 choose to control the FDP distribution according to control of its (i) mean, e.g. the usual BH-FDR
 procedure, or via the probability that it exceeds a given value, delta, via (ii) the Romano
 procedure, or via (iii) my procedure based upon asymptotic approximation. Likewise, we can think of
 the power in multiple testing experiments in terms of a summary of the distribution of the
 True Positive Proportion (TPP). The package will compute power, sample size or any other missing
 parameter required for power based upon (i) the mean of the TPP which is the average power
 (ii) the probability that the TPP exceeds a given value, lambda, via my asymptotic approximation
 procedure. The theoretical results are described in Izmirlian, G. (2020) Stat. and Prob. letters,
 <doi:10.1016/j.spl.2020.108713>, and an applied  paper describing the methodology with a
 simulation study is in preparation.