This package provides tools to compute directly age-standardised rates using the 2013 European Standard Population. Includes variance estimation and 95% confidence intervals for population health applications. Functions are flexible to handle any grouping variable and age bands, allowing reproducible and automated analyses.

Create and manage fault-tolerant task queues for the foreach package using the Redis key/value database.

Statistical models fit to compositional data are often difficult to interpret due to the sum to 1 constraint on data variables. DImodelsVis provides novel visualisations tools to aid with the interpretation of models fit to compositional data. All visualisations in the package are created using the ggplot2 plotting framework and can be extended like every other ggplot object.

Designed to support the visualization, numerical computation, qualitative analysis, model-data fusion, and stochastic simulation for autonomous systems of differential equations. Euler and Runge-Kutta methods are implemented, along with tools to visualize the two-dimensional phaseplane. Likelihood surfaces and a simple Markov Chain Monte Carlo parameter estimator can be used for model-data fusion of differential equations and empirical models. The Euler-Maruyama method is provided for simulation of stochastic differential equations. The package was originally written for internal use to support teaching by Zobitz, and refined to support the text "Exploring modeling with data and differential equations using R" by John Zobitz (2021) <https://jmzobitz.github.io/ModelingWithR/index.html>.

This package provides several data sets for use with discrete statistical tests and discrete multiple testing procedures.

Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of DataVisualizations is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, DataVisualizations makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.

Fast & memory-efficient functions to analyze and manipulate large spatial data data sets. It leverages the fast analytical capabilities of DuckDB and its spatial extension (see <https://duckdb.org/docs/stable/core_extensions/spatial/overview>) while maintaining compatibility with Râ s spatial data ecosystem to work with spatial vector data.

Computing and plotting the distance covariance and correlation function of a univariate or a multivariate time series. Both versions of biased and unbiased estimators of distance covariance and correlation are provided. Test statistics for testing pairwise independence are also implemented. Some data sets are also included. References include: a) Edelmann Dominic, Fokianos Konstantinos and Pitsillou Maria (2019). An Updated Literature Review of Distance Correlation and Its Applications to Time Series'. International Statistical Review, 87(2): 237--262. <doi:10.1111/insr.12294>. b) Fokianos Konstantinos and Pitsillou Maria (2018). Testing independence for multivariate time series via the auto-distance correlation matrix'. Biometrika, 105(2): 337--352. <doi:10.1093/biomet/asx082>. c) Fokianos Konstantinos and Pitsillou Maria (2017). Consistent testing for pairwise dependence in time series'. Technometrics, 59(2): 262--270. <doi:10.1080/00401706.2016.1156024>. d) Pitsillou Maria and Fokianos Konstantinos (2016). dCovTS: Distance Covariance/Correlation for Time Series'. R Journal, 8(2):324-340. <doi:10.32614/RJ-2016-049>.

This package implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering big data (gaussian mixture models for both multivariate and univariate datasets). This version implements the faster alternative-EM* that expedites convergence via structure based data segregation. The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma, Hasan Kurban, Mehmet Dalkilic (2022) <doi:10.1016/j.softx.2021.100944>. Hasan Kurban, Mark Jenne, Mehmet Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.

S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).

Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2018, ISBN:9781138741515) by Dobson and Barnett.

Duplicated data can exist in different rows and columns and user may need to treat observations (rows) connected by duplicated data as one observation, e.g. companies can belong to one family (and thus: be one company) by sharing some telephone numbers. This package allows to find connected rows based on data on chosen columns and collapse it into one row.

Builds interactive d3.js hierarchical visualisation easily. D3partitionR makes it easy to build and customize sunburst, circle treemap, treemap, partition chart, ...

Estimate and return the needed parameters for visualizations designed for OpenBudgets.eu <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the OpenBudgets.eu data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to JSON format and use them as input in a different graphical interface.

Perform a test of a simple null hypothesis about a directly standardized rate and obtain the matching confidence interval using a choice of methods.

Quality control and formatting tools developed for the Copernicus Data Rescue Service. The package includes functions to handle the Station Exchange Format (SEF), various statistical tests for climate data at daily and sub-daily resolution, as well as functions to plot the data. For more information and documentation see <https://datarescue.climate.copernicus.eu/st_data-quality-control>.

This package provides functions and data sets used in examples and exercises in the text Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data Analysis and Graphics Using R", and in an upcoming Maindonald, Braun, and Andrews text that builds on this earlier text.

Implementation of the Decorrelated Local Linear estimator proposed in <arxiv:1907.12732>. It constructs the confidence interval for the derivative of the function of interest under the high-dimensional sparse additive model.

Basic time series functionalities such as listing of missing values, application of arbitrary aggregation as well as rolling (asymmetric) window functions and automatic detection of periodicity. As it is mainly based on data.table', it is fast and (in combination with the R6 package) offers reference semantics. In addition to its native R6 interface, it provides an S3 interface for those who prefer the latter. Finally yet importantly, its functional approach allows for incorporating functionalities from many other packages.

Motifs within biological sequences show a significant role. This package utilizes a user-defined threshold value (window size and similarity) to create consensus segments or motifs through local alignment of dynamic programming with gap and it calculates the frequency of each identified motif, offering a detailed view of their prevalence within the dataset. It allows for thorough exploration and understanding of sequence patterns and their biological importance.

Calculates key indicators such as fertility rates (Total Fertility Rate (TFR), General Fertility Rate (GFR), and Age Specific Fertility Rate (ASFR)) using Demographic and Health Survey (DHS) women/individual data, childhood mortality probabilities and rates such as Neonatal Mortality Rate (NNMR), Post-neonatal Mortality Rate (PNNMR), Infant Mortality Rate (IMR), Child Mortality Rate (CMR), and Under-five Mortality Rate (U5MR), and adult mortality indicators such as the Age Specific Mortality Rate (ASMR), Age Adjusted Mortality Rate (AAMR), Age Specific Maternal Mortality Rate (ASMMR), Age Adjusted Maternal Mortality Rate (AAMMR), Age Specific Pregnancy Related Mortality Rate (ASPRMR), Age Adjusted Pregnancy Related Mortality Rate (AAPRMR), Maternal Mortality Ratio (MMR) and Pregnancy Related Mortality Ratio (PRMR). In addition to the indicators, the DHS.rates package estimates sampling errors indicators such as Standard Error (SE), Design Effect (DEFT), Relative Standard Error (RSE) and Confidence Interval (CI). The package is developed according to the DHS methodology of calculating the fertility indicators and the childhood mortality rates outlined in the "Guide to DHS Statistics" (Croft, Trevor N., Aileen M. J. Marshall, Courtney K. Allen, et al. 2018, <https://dhsprogram.com/Data/Guide-to-DHS-Statistics/index.cfm>) and the DHS methodology of estimating the sampling errors indicators outlined in the "DHS Sampling and Household Listing Manual" (ICF International 2012, <https://dhsprogram.com/pubs/pdf/DHSM4/DHS6_Sampling_Manual_Sept2012_DHSM4.pdf>).

Using these tools to simplify the research process of political science and other social sciences. The current version can create folder system for academic project in political science, calculate psychological trait scores, visualize experimental and spatial data, set up color-blind palette, and test for Type I error (false positives) in Qualitative Comparative Analysis (QCA) for crisp-set, multi-value, and fuzzy-set variants.

The distributed online expectation maximization algorithms are used to solve parameters of Poisson mixture models. The philosophy of the package is described in Guo, G. (2022) <doi:10.1080/02664763.2022.2053949>.

Computational tools for meta-analysis of diagnostic accuracy test. Bootstrap-based computational methods of the confidence interval for AUC of summary ROC curve and some related AUC-based inference methods are available (Noma et al. (2021) <doi:10.1080/23737484.2021.1894408>).