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Data Structures, Summaries, and Visualisations for Missing Data
Missing values are ubiquitous in data and need to be explored and
handled in the initial stages of analysis. 'naniar' provides data
structures and functions that facilitate the plotting of missing values and
examination of imputations. This allows missing data dependencies to be
explored with minimal deviation from the common work patterns of 'ggplot2'
and tidy data. The work is fully discussed at Tierney & Cook (2023)
Get XKCD Comic Data
Download data from individual XKCD comics, written by Randall Munroe < https://xkcd.com/>.
Spatial Temporal Analysis of Moving Polygons
Perform spatial temporal analysis of moving polygons; a longstanding analysis problem in Geographic Information Systems. Facilitates directional analysis, distance analysis, and some other simple functionality for examining spatial-temporal patterns of moving polygons.
Automatic Fixed Rank Kriging
Automatic fixed rank kriging for (irregularly located)
spatial data using a class of basis functions with multi-resolution features
and ordered in terms of their resolutions. The model parameters are estimated
by maximum likelihood (ML) and the number of basis functions is determined
by Akaike's information criterion (AIC). For spatial data with either one
realization or independent replicates, the ML estimates and AIC are efficiently
computed using their closed-form expressions when no missing value occurs. Details
regarding the basis function construction, parameter estimation, and AIC calculation
can be found in Tzeng and Huang (2018)
Weighted Portmanteau Tests for Time Series Goodness-of-Fit
An implementation of the Weighted Portmanteau Tests described in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing" published by the Journal of the American Statistical Association, Volume 107, Issue 498, pages 777-787, 2012.
Overdispersion in Count Data Multiple Regression Analysis
Detection of overdispersion in count data for multiple regression analysis.
Log-linear count data regression is one of the most popular techniques for predictive
modeling where there is a non-negative discrete quantitative dependent variable. In
order to ensure the inferences from the use of count data models are appropriate,
researchers may choose between the estimation of a Poisson model and a negative binomial
model, and the correct decision for prediction from a count data estimation is directly
linked to the existence of overdispersion of the dependent variable, conditional to the
explanatory variables. Based on the studies of Cameron and Trivedi (1990)
Censoring Data and Likelihood-Based Correlation Estimation
A profile likelihood based method of estimation and inference on the correlation coefficient of bivariate data with different types of censoring and missingness.
Spectral Modularity Clustering
Implements the network clustering algorithm described in
Newman (2006)
Companion Software for the Coursera Statistics with R Specialization
Data and functions to support Bayesian and frequentist inference and decision making for the Coursera Specialization "Statistics with R". See < https://github.com/StatsWithR/statsr> for more information.
This is a Collection of Functions to Analyse Gender Differences
Implementation of functions, which combines binomial calculation
and data visualisation, to analyse the differences in publishing authorship
by gender described in Day et al. (2020)