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Linear and Smooth Predictor Modelling with Penalisation and Variable Selection
Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model).
Scalable Gaussian-Process Approximations
Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017)
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.
Trajectory Miner: a Sequence Analysis Toolkit
Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
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
Create Waffle Chart Visualizations
Square pie charts (a.k.a. waffle charts) can be used to communicate parts of a whole for categorical quantities. To emulate the percentage view of a pie chart, a 10x10 grid should be used with each square representing 1% of the total. Modern uses of waffle charts do not necessarily adhere to this rule and can be created with a grid of any rectangular shape. Best practices suggest keeping the number of categories small, just as should be done when creating pie charts. Tools are provided to create waffle charts as well as stitch them together, and to use glyphs for making isotype pictograms.