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HuraultMisc

HuraultMisc is my personal R package regrouping functions used across different projects. The library mostly provides functions for data analysis.

The package can be installed and loaded by typing the following commands in R:

devtools::install_github("ghurault/HuraultMisc")
library(HuraultMisc)

The list of available functions and their documentation can be accessed from:

help(package = HuraultMisc)

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Version

Install

install.packages('HuraultMisc')

Monthly Downloads

551

Version

1.1.1

License

MIT + file LICENSE

Issues

1

Pull Requests

0

Stars

0

Forks

0

Maintainer

Guillem Hurault

Last Published

September 6th, 2021

Functions in HuraultMisc (1.1.1)

approx_equal

Approximate equal
cbbPalette

A colorblind-friendly palette (with black)
extract_pmf

Extract probability mass function from vector of samples
factor_to_numeric

Change the type of the column of a dataframe from factor to numeric
summary_statistics

Extract summary statistics
coverage

Coverage probability
is_wholenumber

Test whether x is a whole number
HuraultMisc-package

HuraultMisc: Guillem Hurault Personal Functions' Library
PPC_group_distribution

Posterior Predictive Check for Stan model
empirical_pval

Compute empirical p-values
extract_parameters_from_draw

Extract parameters from a single draw
extract_pdf

Extract probability density function from vector of samples
illustrate_RPS

Illustration of the Ranked Probability Score
illustrate_forward_chaining

Illustration forward chaining
change_colnames

Change column names of a dataframe
logit

Logit and Inverse logit
compute_RPS

Compute RPS for a single forecast
extract_index_nd

Extract multiple indices inside bracket(s) as a list
extract_draws

Extract parameters' draws
compute_calibration

Estimate calibration given forecasts and corresponding outcomes
post_pred_pval

Posterior Predictive p-value
%>%

Pipe operator
prior_posterior

Compare prior to posterior
process_replications

Extract posterior predictive distribution
compute_resolution

Compute resolution of forecasts, normalised by the uncertainty
extract_distribution

Extract a distribution represented by samples
is_scalar

Test whether x is of length 1
extract_ci

Extract confidence intervals from a vector of samples
is_stanfit

Test whether an object is of class "stanfit"