Implements two differentially private algorithms for 
  estimating L2-regularized logistic regression coefficients. A randomized
  algorithm F is epsilon-differentially private (C. Dwork, Differential
  Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if 
     |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
  for any pair D, D' of datasets that differ in exactly one record, any
  measurable set S, and the randomness is taken over the choices F makes.

Estimates adjusted prevalence ratios (PR) and their confidence
    intervals from logistic regression models, addressing the well-known
    limitation of odds ratios (OR) as approximations to PR in cross-sectional
    studies with common outcomes. Supports independent observations (glm()),
    clustered/multilevel data (glmer() from 'lme4'), longitudinal data via
    Generalised Estimating Equations (geeglm() from 'geepack'), and complex
    survey designs (svyglm() from 'survey'). Inference is available via the 
    delta method (conditional and marginal standardisation) and via
    bootstrap (normal-approximation and percentile intervals).
    Continuous covariates are handled through user-specified or
    median-based reference values; flexible baseline specification
    allows any reference category to be chosen for factor predictors.
    Based on the methodology described in Amorim & Ospina (2021)
    <doi:10.1590/0001-3765202120190316>.

Calculates seat allocation using the D-Hondt method, Sainte-Lague method, and Modified Sainte-Lague method, all commonly used in proportional representation electoral systems. For more information on these methods, see Michael Gallagher (1991)<doi:10.1016/0261-3794(91)90004-C>.

Miscellaneous printing of numeric or statistical results in R Markdown or Quarto documents according to guidelines of the "Publication Manual" of the American Psychological Association (2020, ISBN: 978-1-4338-3215-4). These guidelines are usually referred to as APA style (<https://apastyle.apa.org/>) and include specific rules on the formatting of numbers and statistical test results. APA style has to be implemented when submitting scientific reports in a wide range of research fields, especially in the social sciences. The default output of numbers in the R console or R Markdown and Quarto documents does not meet the APA style requirements, and reformatting results manually can be cumbersome and error-prone. This package covers the automatic conversion of R objects to textual representations that meet the APA style requirements, which can be included in R Markdown or Quarto documents. It covers some basic statistical tests (t-test, ANOVA, correlation, chi-squared test, Wilcoxon test) as well as some basic number printing manipulations (formatting p-values, removing leading zeros for numbers that cannot be greater than one, and others). Other packages exist for formatting numbers and tests according to the APA style guidelines, such as 'papaja' (<https://cran.r-project.org/package=papaja>) and 'apa' (<https://cran.r-project.org/package=apa>), but they do not offer all convenience functionality included in 'prmisc'. The vignette has an overview of most of the functions included in the package.