Rank Preserving Structural Failure Time Models
Implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.

c(person("Simon","Bond", email="simon.bond@addenbrookes.nhs.uk", role = c("aut","cre"), comment = "primary author of code, secondary author of vignette"),person("Annabel", "Allison", role = "aut", comment = "primary author of vignette, secondary author of code"))
Simon Bond [aut, cre] (primary author of code, secondary author of
vignette),
Annabel Allison [aut] (primary author of vignette, secondary author of
code)

An implementation of Recursive Partitioning Survival Trees via a node-splitting rule that builds decision tree models that reflected within-node and within-treatment responses. The algorithm aims to find the maximal difference in survival time among different treatments.

The rpsychi offers a number of functions for psychiatry,
psychiatric nursing, clinical psychology. Functions are
primarily for statistical significance testing using published
work. For example, you can conduct a factorial analysis of
variance (ANOVA), which requires only the mean, standard
deviation, and sample size for each cell, rather than the
individual data. This package covers fundamental statistical
tests such as t-test, chi-square test, analysis of variance,
and multiple regression analysis. With some exceptions, you can
obtain effect size and its confidence interval. These functions
help you to obtain effect size from published work, and then to
conduct a priori power analysis or meta-analysis, even if a
researcher do not report effect size in a published work.

The rpsychi offers a number of functions for psychiatry, psychiatric nursing, clinical psychology. Functions are primarily for statistical significance testing using published work. For example, you can conduct a factorial analysis of variance (ANOVA), which requires only the mean, standard deviation, and sample size for each cell, rather than the individual data. This package covers fundamental statistical tests such as t-test, chi-square test, analysis of variance, and multiple regression analysis. With some exceptions, you can obtain effect size and its confidence interval. These functions help you to obtain effect size from published work, and then to conduct a priori power analysis or meta-analysis, even if a researcher do not report effect size in a published work.