While analyzing geospatial data, easy visualization is often
needed that allows for quick plotting, and simple, but easy interactivity.
Additionally, visualizing geospatial data in projected coordinates is also
desirable. The 'quickmapr' package provides a simple method to visualize
'sp', 'sf' (via coercion to 'sp'), and 'raster' objects, allows for basic
zooming, panning, identifying,labeling, selecting, and measuring spatial
objects.  Importantly, it does not require that the data be in geographic
coordinates.
raster, sp, rgeos, rgdal, httr, stats, grDevices, graphics,
methods, sf

c(
person("Fredrik", "Savje", email = "fredrik.savje@berkeley.edu", role = c("aut", "cre")),
person("Jasjeet", "Sekhon", role = "aut"),
person("Michael", "Higgins", role = "aut"))
Provides functions for constructing near-optimal generalized full matching.
Generalized full matching is an extension of the original full matching method
to situations with more intricate study designs. The package is made with
large data sets in mind and derives matches more than an order of magnitude
quicker than other methods.

Fits Psychometric Functions for Multiple Groups
c(
person("Linares", "Daniel", email = "danilinares@gmail.com", role = c("aut","cre")),
person("López-Moliner", "Joan", email = "j.lopezmoliner@gmail.com", role = "aut"))
Quickly fits and plots psychometric functions (normal, logistic, Weibull or any or any function defined by the user) for multiple groups.
R (>= 3.1.1), boot, DEoptim, dplyr, ggplot2, tidyr
Linares Daniel [aut, cre],
López-Moliner Joan [aut]

Quickly fits and plots psychometric functions (normal, logistic,
Weibull or any or any function defined by the user) for multiple groups.

c(
person("Linares", "Daniel", email = "danilinares@gmail.com", role = c("aut","cre")),
person("L<U+00F3>pez-Moliner", "Joan", email = "j.lopezmoliner@gmail.com", role = "aut"))
Linares Daniel [aut, cre],
L<U+00F3>pez-Moliner Joan [aut]

person("Darshan", "Maniyar", email = "maniyar.darshan@gmail.com", role = c("aut", "cre"))
Helps to perform linear regression analysis by reducing manual effort. Reduces the independent variables based on specified p-value and Variance Inflation Factor (VIF) level.