This package provides a concise check of the format of one or multiple input arguments (data type, length or value) is provided. Since multiple input arguments can be tested simultaneously, a lengthly list of checks at the beginning of your function can be avoided, hereby enhancing the readability and maintainability of your code.

When visualising changes between two values over time, a strict linear interpolation can look jarring and unnatural. By applying a non-linear easing to the transition, the motion between values can appear smoother and more natural. This package includes functions for applying such non-linear easings to colors and numeric values, and is useful where smooth animated movement and transitions are desired.

The framework provides functions to generate ODEs of reaction networks, parameter transformations, observation functions, residual functions, etc. The framework follows the paradigm that derivative information should be used for optimization whenever possible. Therefore, all major functions produce and can handle expressions for symbolic derivatives. The methods used in dMod were published in Kaschek et al, 2019, <doi:10.18637/jss.v088.i10>.

Assesses data quality in Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) databases. Executes data quality checks and provides an R `shiny` application to view the results.

The df2yaml aims to simplify the process of converting dataframe to YAML <https://yaml.org/>. The dataframe with multiple key columns and one value column will be converted to the multi-level hierarchy.

This package provides a weekly, monthly, yearly summary of dengue cases by state/ province/ country.

Useful functions for various DDI (Data Documentation Initiative) related inputs and outputs. Converts data files to and from DDI, SPSS, Stata, SAS, R and Excel, including user declared missing values.

This package provides a series of functions which aid in both simulating and determining the properties of finite, discrete-time, discrete state markov chains. Two functions (DTMC, MultDTMC) produce n iterations of a Markov Chain(s) based on transition probabilities and an initial distribution. The function FPTime determines the first passage time into each state. The function statdistr determines the stationary distribution of a Markov Chain.

Go beyond standard probability distributions such as the Normal or Exponential by combining, shifting, maximizing, and otherwise transforming distributions with simple, verb-based functions. Provides easy access to a broader space of distributions more representative of real-world systems such as river flows or insurance claims. Part of the probaverse framework of packages to support advanced statistical modeling and simulations with an intuitive workflow.

Differential Item Functioning (DIF) Analysis with shiny application interfaces. You can run the functions in this package without any arguments and perform your DIF analysis using user-friendly interfaces.

This MCMC method takes a data numeric vector (Y) and assigns the elements of Y to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.

Have you ever been tempted to create roxygen2'-style documentation comments for one of your functions that was not part of one of your packages (yet)? This is exactly what this package is about: running roxygen2 on (chunks of) a single code file.

The discrete Laplace exponential family for use in fitting generalized linear models.

This package provides a penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.

Various utilities for the Davies distribution.

Build donut/pie charts with ggplot2 layer by layer, exploiting the advantages of polar symmetry. Leverage layouts to distribute labels effectively. Connect labels to donut segments using pins. Streamline annotation and highlighting.

Implementation of different statistical tools for the description and analysis of gene expression data based on the concept of data depth, namely, the scale curves for visualizing the dispersion of one or various groups of samples (e.g. types of tumors), a rank test to decide whether two groups of samples come from a single distribution and two methods of supervised classification techniques, the DS and TAD methods. All these techniques are based on the Modified Band Depth, which is a recent notion of depth with a low computational cost, what renders it very appropriate for high dimensional data such as gene expression data.

Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.

This package provides 2D and 3D tour animations as HTML widgets. The user can interact with the widgets using orbit controls, tooltips, brushing, and timeline controls. Linked brushing is supported using crosstalk', and widgets can be embedded in Shiny apps or HTML documents.

Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.

This package provides tools to apply Ensemble Empirical Mode Decomposition (EEMD) for cyclostratigraphy purposes. Mainly: a new algorithm, extricate, that performs EEMD in seconds, a linear interpolation algorithm using the greatest rational common divisor of depth or time, different algorithms to compute instantaneous amplitude, frequency and ratios of frequencies, and functions to verify and visualise the outputs. The functions were developed during the CRASH project (Checking the Reproducibility of Astrochronology in the Hauterivian). When using for publication please cite Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, A.C., Zeeden, C., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2022, "A decomposition approach to cyclostratigraphic signal processing". Earth-Science Reviews 225 (103894). <doi:10.1016/j.earscirev.2021.103894>.

Data screening is an important first step of any statistical analysis. dataReporter auto generates a customizable data report with a thorough summary of the checks and the results that a human can use to identify possible errors. It provides an extendable suite of test for common potential errors in a dataset. See Petersen AH, Ekstrøm CT (2019). "dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R." _Journal of Statistical Software_, *90*(6), 1-38 <doi:10.18637/jss.v090.i06> for more information.

Toggles the test and production versions of a large data analysis project.