Pretty Summaries of Generalized Linear Model Coefficients
person(given = "Jared",
family = "Fowler",
role = c("cre", "aut"),
email = "jared.fowler8@gmail.com")
One of the main advantages of using Generalised
Linear Models is their interpretability.  The goal of 'prettyglm' is to
provide a set of functions which easily create beautiful coefficient
summaries which can readily be shared and explained.
broom, car, dplyr, forcats, kableExtra, knitr, plotly,
stringr, tibble, tidycat, tidyr, tidyselect, vip

Scale Bar, North Arrow, and Pretty Margins in R
Automates the process of creating a scale bar and north arrow in
any package that uses base graphics to plot in R. Bounding box tools help find and manipulate
extents. Finally, there is a function to automate the process of setting margins, plotting
the map, scalebar, and north arrow, and resetting graphic parameters upon completion.

Automates the process of creating a scale bar and north arrow in
any package that uses base graphics to plot in R. Bounding box tools help find and manipulate
extents. Finally, there is a function to automate the process of setting margins, plotting
the map, scale bar, and north arrow, and resetting graphic parameters upon completion.

Automates the process of creating a scale bar and north arrow in
any package that uses base graphics to plot in R. Bounding box tools help find
and manipulate extents. Finally, there is a function to automate the process
of setting margins, plotting the map, scale bar, and north arrow, and resetting
graphic parameters upon completion.

maptools, raster, rosm, testthat, rgdal, sp

Pretty, Human Readable Formatting of Quantities
Pretty, human readable formatting of quantities.
Time intervals: 1337000 -> 15d 11h 23m 20s.
Vague time intervals: 2674000 -> about a month ago.
Bytes: 1337 -> 1.34 kB.

Pretty, human readable formatting of quantities.
Time intervals: '1337000' -> '15d 11h 23m 20s'.
Vague time intervals: '2674000' -> 'about a month ago'.
Bytes: '1337' -> '1.34 kB'.

Estimating regional trends of a prevalence from a DHS
Joseph Larmarange - CEPED (Universite Paris Descartes Ined IRD)
IRD, with fundings from ANRS and IRD, and technical support
from LYSIS (info@lysis-consultants.fr)
This package performs spatial estimation of a prevalence
surface or a relative risks surface, using data from a
Demographic and Health Survey (DHS) or an analog survey.
R (>= 2.10), methods, sp, gstat, maptools, fields, GenKern,
rgdal (>= 0.7-4), geoR, tcltk