Thematic Maps

Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.

Treemap Visualization

A treemap is a space-filling visualization of hierarchical structures. This package offers great flexibility to draw treemaps.

Thematic Map Tools

Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps.

Tableplot, a Visualization of Large Datasets

A tableplot is a visualisation of a (large) dataset with a dozen of variables, both numeric and categorical. Each column represents a variable and each row bin is an aggregate of a certain number of records. Numeric variables are visualized as bar charts, and categorical variables as stacked bar charts. Missing values are taken into account. Also supports large 'ffdf' datasets from the 'ff' package.

Prediction Model Selection and Performance Evaluation in Multiple Imputed Datasets

Provides functions to apply pooling or backward selection for logistic or Cox regression prediction models in multiple imputed datasets. Backward selection can be done from the pooled model using Rubin's Rules (RR), the total covariance matrix (D1 method), pooling chi-square values (D2 method), pooling likelihood ratio statistics (D3) or pooling the median p-values. The model can contain continuous, dichotomous, categorical predictors and interaction terms between all type of these predictors. Continuous predictors can also be introduced as restricted cubic spline coefficients. It is also possible to force (spline) predictors or interaction terms in the model during predictor selection. The package also contains functions to generate apparent model performance measures over imputed datasets as ROC/AUC, R-squares, fit test values and calibration plots. A wrapper function over Frank Harrell's validate function is used for that. Bootstrap internal validation is performed in each imputed dataset and results are pooled. Backward selection as part of internal validation is optional and recommended. Also a function to externally validate logistic prediction models in multiple imputed datasets is available. Eekhout (2017) . Wiel (2009) . Marshall (2009) .

Routines for Performing Empirical Calibration of Observational Study Estimates

Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls.

Rendering Parameterized SQL and Translation to Dialects

A rendering tool for parameterized SQL that also translates into different SQL dialects. These dialects include 'Microsoft Sql Server', 'Oracle', 'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', and 'SQLite'.

Support for Parallel Computation, Logging, and Function Automation

Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel).

Tabplotd3, interactive inspection of large data

A tableplot is a visualisation of a (large) dataset with a dozen of variables, both numeric and categorical. This package contains an interactive version of tableplot working in your browser.

Interpreting Time Series and Autocorrelated Data Using GAMMs

GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).