Make optimal decisions for your personal or
    household finances. Use tools and methods that are selected carefully
    to align with academic consensus, bridging the gap between theoretical
    knowledge and practical application. They help you find
    your own personalized optimal discretionary spending or
    optimal asset allocation, and prepare you for retirement 
    or financial independence.
    The optimal solution to this problems is extremely complex,
    and we only have a single lifetime to get it right.
    Fortunately, we now have the user-friendly tools implemented,
    that integrate life-cycle models 
    with single-period net-worth mean-variance optimization models. 
    Those tools can be used by anyone who wants to see 
    what highly-personalized optimal decisions can look like.
    For more details see: 
    Idzorek T., Kaplan P. (2024, ISBN:9781952927379),
    Haghani V., White J. (2023, ISBN:9781119747918).

Interface for the 'Google Ads API'. 'Google Ads' is an online
    advertising service that enables advertisers to display advertising to web
    users (see <https://developers.google.com/google-ads/> for more information).

A collection of datasets that accompany the forthcoming book "R for Health Care Research".

Since the early 1970s eyewitness testimony researchers have recognised the 
    importance of estimating properties such as lineup bias (is the lineup biased against 
    the suspect, leading to a rate of choosing higher than one would expect by chance?), 
    and lineup size (how many reasonable choices are in fact available to the witness? 
    A lineup is supposed to consist of a suspect and a number of additional members, 
    or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup 
    measures are descriptive, in the first instance, but since the earliest articles in 
    the literature researchers have recognised the importance of reasoning inferentially 
    about them. This package contains functions to compute various properties of 
    laboratory or police lineups, and is intended for use by researchers in forensic 
    psychology and/or eyewitness testimony research. Among others, the r4lineups package 
    includes functions for calculating lineup proportion, functional size, various 
    estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity 
    ratio, ROC curves for confidence x accuracy data and the degree of similarity of 
    faces in a lineup.