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Intensity Analysis of Spatial Point Patterns on Complex Networks
Tools to analyze point patterns in space occurring over planar network structures derived from graph-related intensity measures for undirected, directed, and mixed networks.
This package is based on the following research: Eckardt and Mateu (2018)
Aggregate Numeric, Date and Categorical Variables
Convenience functions for aggregating a data frame or data table. Currently mean, sum and variance are supported. For Date variables, the recency and duration are supported. There is also support for dummy variables in predictive contexts. Code has been completely re-written in data.table for computational speed.
Multiple Primary Endpoints
Functions for calculating sample size and power for clinical trials with multiple (co-)primary endpoints.
Functional Rarity Indices Computation
Computes functional rarity indices as proposed by Violle et al.
(2017)
Estimation in Adaptive Group Sequential Trials
Calculation of repeated confidence intervals as well as confidence intervals based on the stage-wise ordering in group sequential designs and adaptive group sequential designs. For adaptive group sequential designs the confidence intervals are based on the conditional rejection probability principle. Currently the procedures do not support the use of futility boundaries or more than one adaptive interim analysis.
Quaternions Splines
Provides routines to create some quaternions splines:
Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels
algorithm. The implementations are based on the Python library
'splines'. Quaternions splines allow to construct spherical curves.
References: Barry and Goldman
A Time Series Toolbox for Official Statistics
Plot official statistics' time series conveniently: automatic legends, highlight windows, stacked bar chars with positive and negative contributions, sum-as-line option, two y-axes with automatic horizontal grids that fit both axes and other popular chart types. 'tstools' comes with a plethora of defaults to let you plot without setting an abundance of parameters first, but gives you the flexibility to tweak the defaults. In addition to charts, 'tstools' provides a super fast, 'data.table' backed time series I/O that allows the user to export / import long format, wide format and transposed wide format data to various file types.
A Sound Interface for R
Basic functions for dealing with wav files and sound samples.
Dropout Analysis by Condition
Analysis and visualization of dropout between conditions in surveys and (online) experiments. Features include computation of dropout statistics, comparing dropout between conditions (e.g. Chi square), analyzing survival (e.g. Kaplan-Meier estimation), comparing conditions with the most different rates of dropout (Kolmogorov-Smirnov) and visualizing the result of each in designated plotting functions. Sources: Andrea Frick, Marie-Terese Baechtiger & Ulf-Dietrich Reips (2001) < https://www.researchgate.net/publication/223956222_Financial_incentives_personal_information_and_drop-out_in_online_studies>; Ulf-Dietrich Reips (2002) "Standards for Internet-Based Experimenting"
Item Pool Visualization
Generate plots based on the Item Pool Visualization concept for
latent constructs. Item Pool Visualizations are used to display the
conceptual structure of a set of items (self-report or psychometric).
Dantlgraber, Stieger, & Reips (2019)