Model estimation, dispersion testing and diagnosis of hyper-Poisson Saez-Castillo, A.J. and Conde-Sanchez, A. (2013) <doi:10.1016/j.csda.2012.12.009> and Conway-Maxwell-Poisson Huang, A. (2017) regression models.

It allows to learn the structure of univariate time series, learning parameters and forecasting. Implements a model of Dynamic Bayesian Networks with temporal windows, with collections of linear regressors for Gaussian nodes, based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.

Tool to print out the value of R objects/expressions while running an R script. Outputs can be made dependent on user-defined conditions/criteria. Debug messages only appear when a global option for debugging is set. This way, debugr code can even remain in the debugged code for later use without any negative effects during normal runtime.

Estimates probabilistic phylogenetic Principal Component Analysis (PCA) and non-phylogenetic probabilistic PCA. Provides methods to implement alternative models of trait evolution including Brownian motion (BM), Ornstein-Uhlenbeck (OU), Early Burst (EB), and Pagel's lambda. Also provides flexible biplot functions.

Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function det.construct translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function det.query is available. The functions det1 and det2 provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.

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>.

Set of functions for Data Envelopment Analysis, including classical, fuzzy, cross-efficiency, bootstrapping, and Malmquist models. See: Banker, R.; Charnes, A.; Cooper, W.W. (1984). <doi:10.1287/mnsc.30.9.1078>, Charnes, A.; Cooper, W.W.; Rhodes, E. (1978). <doi:10.1016/0377-2217(78)90138-8> and Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). <doi:10.1287/mnsc.27.6.668>.

This package provides a data augmentation based sampler for conducting privacy-aware Bayesian inference. The dapper_sample() function takes an existing sampler as input and automatically constructs a privacy-aware sampler. The process of constructing a sampler is simplified through the specification of four independent modules, allowing for easy comparison between different privacy mechanisms by only swapping out the relevant modules. Probability mass functions for the discrete Gaussian and discrete Laplacian are provided to facilitate analyses dealing with privatized count data. The output of dapper_sample() can be analyzed using many of the same tools from the rstan ecosystem. For methodological details on the sampler see Ju et al. (2022) <doi:10.48550/arXiv.2206.00710>, and for details on the discrete Gaussian and discrete Laplacian distributions see Canonne et al. (2020) <doi:10.48550/arXiv.2004.00010>.

This package implements a system of linear equations to recover unreported diagnostic test accuracy cell counts from commonly reported measures such as sensitivity, specificity, predictive values, prevalence, and sample size. The package is intended for applied researchers who require complete 2x2 table counts for downstream analyses.

S4-classes for setting up a coherent framework for simulation within the distr family of packages.

This package provides tools for constructing, manipulating and using distance metrics.

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.

While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

Implement download buttons in HTML output from rmarkdown without the need for runtime:shiny'.

Explore neural networks in a layer oriented way, the framework is intended to give the user total control of the internals of a net without much effort. Use classes like PerceptronLayer to create a layer of Percetron neurons, and specify how many you want. The package does all the tricky stuff internally leaving you focused in what you want. I wrote this package during a neural networks course to help me with the problem set.

This package provides a general framework using mixture Weibull distributions to accurately predict biomarker-guided trial duration accounting for heterogeneous population. Extensive simulations are performed to evaluate the impact of heterogeneous population and the dynamics of biomarker characteristics and disease on the study duration. Several influential parameters including median survival time, enrollment rate, biomarker prevalence and effect size are identified. Efficiency gains of biomarker-guided trials can be quantitatively compared to the traditional all-comers design. For reference, see Zhang et al. (2024) <arXiv:2401.00540>.

An implementation of Dcifer (Distance for complex infections: fast estimation of relatedness), an identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data. The package includes functions that format and preprocess the data, implement the method, and visualize the results. Gerlovina et al. (2022) <doi:10.1093/genetics/iyac126>.

This package provides tools for describing parameters of algorithms in an abstract way. Description can include an id, a description, a domain (range or list of values), and a default value. dynparam can also convert parameter sets to a ParamHelpers format, in order to be able to use dynparam in conjunction with mlrMBO'.

Fast computation of the distance covariance dcov and distance correlation dcor'. The computation cost is only O(n log(n)) for the distance correlation (see Chaudhuri, Hu (2019) <arXiv:1810.11332> <doi:10.1016/j.csda.2019.01.016>). The functions are written entirely in C++ to speed up the computation.

This package provides a datetime range picker widget for usage in Shiny'. It creates a calendar allowing to select a start date and an end date as well as two fields allowing to select a start time and an end time.

The truncated factor model is a statistical model designed to handle specific data structures in data analysis. DTFM is a powerful tool designed to efficiently process and analyze distributed datasets. The philosophy of the package is described in Guo et al. (2023) <doi:10.1007/s00180-022-01270-z>.

Kevin Dowd's book Measuring Market Risk is a widely read book in the area of risk measurement by students and practitioners alike. As he claims, MATLAB indeed might have been the most suitable language when he originally wrote the functions, but, with growing popularity of R it is not entirely valid. As Dowd's code was not intended to be error free and were mainly for reference, some functions in this package have inherited those errors. An attempt will be made in future releases to identify and correct them. Dowd's original code can be downloaded from www.kevindowd.org/measuring-market-risk/. It should be noted that Dowd offers both MMR2 and MMR1 toolboxes. Only MMR2 was ported to R. MMR2 is more recent version of MMR1 toolbox and they both have mostly similar function. The toolbox mainly contains different parametric and non parametric methods for measurement of market risk as well as backtesting risk measurement methods.

This package provides a suite of loon related packages providing data analytic tools for Direct Interactive Visual Exploration in R ('diveR'). These tools work with and complement those of the tidyverse suite, extending the grammar of ggplot2 to become a grammar of interactive graphics. The suite provides many visual tools designed for moderately (100s of variables) high dimensional data analysis, through zenplots and novel tools in loon', and extends the ggplot2 grammar to provide parallel coordinates, Andrews plots, and arbitrary glyphs through ggmulti'. The diveR package gathers together and installs all these related packages in a single step.

This package contains a single function dclust() for divisive hierarchical clustering based on recursive k-means partitioning (k = 2). Useful for clustering large datasets where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records). For further information see Steinbach, Karypis and Kumar (2000) <http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf>.