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'shiny' Cron Expression Input Widget
A widget for 'shiny' apps to handle schedule expression input, using the 'cron-expression-input' JavaScript component. Note that this does not edit the 'crontab' file, it is just an input element for the schedules. See < https://github.com/DatalabFabriek/shinycroneditor/blob/main/inst/examples/shiny-app.R> for an example implementation.
Gene Cluster Visualizations
Provides tools for plotting gene clusters and transcripts by
importing data from GenBank, FASTA, and GFF files. It performs BLASTP and
MUMmer alignments [Altschul et al. (1990)
Highly Adaptive Lasso Conditional Density Estimation
An algorithm for flexible conditional density estimation based on
application of pooled hazard regression to an artificial repeated measures
dataset constructed by discretizing the support of the outcome variable. To
facilitate non/semi-parametric estimation of the conditional density, the
highly adaptive lasso, a nonparametric regression function shown to reliably
estimate a large class of functions at a fast convergence rate, is utilized.
The pooled hazards data augmentation formulation implemented was first
described by Díaz and van der Laan (2011)
Predictive Power Score
The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two variables. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). PPS can be useful for data exploration purposes, in the same way correlation analysis is. For more information on PPS, see < https://github.com/paulvanderlaken/ppsr>.
Relative Distribution Methods
Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.
Download and Tidy Data from the Australian Prudential Regulation Authority
Download the latest data from the Australian Prudential Regulation Authority < https://www.apra.gov.au/> and import it into R as a tidy data frame.
Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Business Process Analysis in R
Comprehensive Business Process Analysis toolkit. Creates S3-class for event log objects, and related handler functions. Imports related packages for filtering event data, computation of descriptive statistics, handling of 'Petri Net' objects and visualization of process maps. See also packages 'edeaR','processmapR', 'eventdataR' and 'processmonitR'.
Multiple Imputation of Covariates by Substantive Model Compatible Fully Conditional Specification
Implements multiple imputation of missing covariates by Substantive Model Compatible Fully Conditional Specification. This is a modification of the popular FCS/chained equations multiple imputation approach, and allows imputation of missing covariate values from models which are compatible with the user specified substantive model.
The R Package Ada for Stochastic Boosting
Performs discrete, real, and gentle boost under both exponential and logistic loss on a given data set. The package ada provides a straightforward, well-documented, and broad boosting routine for classification, ideally suited for small to moderate-sized data sets.