Univariate and multivariate versions of risk-based control charts. Univariate versions of control charts, such as the risk-based version of X-bar, Moving Average (MA), Exponentially Weighted Moving Average Control Charts (EWMA), and Cumulative Sum Control Charts (CUSUM) charts. The risk-based version of the multivariate T2 control chart. Plot and summary functions. Kosztyan et. al. (2016) <doi:10.1016/j.eswa.2016.06.019>.

R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.

This package provides a simple and efficient way to read data from Paradox database files (.db) directly into R as modern tibble data frames. It uses the underlying pxlib C library, to handle the low-level file format details and provides a clean, user-friendly R interface.

Calculate the probability density functions (PDFs) for two threshold evidence accumulation models (EAMs). These are defined using the following Stochastic Differential Equation (SDE), dx(t) = v(x(t),t)*dt+D(x(t),t)*dW, where x(t) is the accumulated evidence at time t, v(x(t),t) is the drift rate, D(x(t),t) is the noise scale, and W is the standard Wiener process. The boundary conditions of this process are the upper and lower decision thresholds, represented by b_u(t) and b_l(t), respectively. Upper threshold b_u(t) > 0, while lower threshold b_l(t) < 0. The initial condition of this process x(0) = z where b_l(t) < z < b_u(t). We represent this as the relative start point w = z/(b_u(0)-b_l(0)), defined as a ratio of the initial threshold location. This package generates the PDF using the same approach as the python package it is based upon, PyBEAM by Murrow and Holmes (2023) <doi:10.3758/s13428-023-02162-w>. First, it converts the SDE model into the forwards Fokker-Planck equation dp(x,t)/dt = d(v(x,t)*p(x,t))/dt-0.5*d^2(D(x,t)^2*p(x,t))/dx^2, then solves this equation using the Crank-Nicolson method to determine p(x,t). Finally, it calculates the flux at the decision thresholds, f_i(t) = 0.5*d(D(x,t)^2*p(x,t))/dx evaluated at x = b_i(t), where i is the relevant decision threshold, either upper (i = u) or lower (i = l). The flux at each thresholds f_i(t) is the PDF for each threshold, specifically its PDF. We discuss further details of this approach in this package and PyBEAM publications. Additionally, one can calculate the cumulative distribution functions of and sampling from the EAMs.

Sundry discrete probability distributions and helper functions.

Estimation of abundance and other demographic parameters for closed populations, open populations and the robust design in capture-recapture experiments using loglinear models.

Set of classes and methods to read data and metadata documents exchanged through the Statistical Data and Metadata Exchange (SDMX) framework, currently focusing on the SDMX XML standard format (SDMX-ML).

Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. romic takes advantage of these transformations to create interactive shiny apps for exploratory data analysis such as an interactive heatmap.

This package provides a general-purpose optimisation engine that supports i) Monte Carlo optimisation with Metropolis criterion [Metropolis et al. (1953) <doi:10.1063/1.1699114>, Hastings (1970) <doi:10.1093/biomet/57.1.97>] and Acceptance Ratio Simulated Annealing [Kirkpatrick et al. (1983) <doi:10.1126/science.220.4598.671>, Ä erný (1985) <doi:10.1007/BF00940812>] on multiple cores, and ii) Acceptance Ratio Replica Exchange Monte Carlo Optimisation. In each case, the system pseudo-temperature is dynamically adjusted such that the observed acceptance ratio is kept near to the desired (fixed or changing) acceptance ratio.

Package runonce helps automating the saving of long-running code to help running the same code multiple times. If you run some long-running code once, it saves the result in a file on disk. Then, if the result already exists, i.e. if the code has already been run and its output has already been saved, it just reads the result from the stored file instead of running the code again.

Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.

Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the RMarkdown vignette: "'refitME R-package tutorial".

An implementation of Kaplan, Betancourt, Steorts (2022) <doi:10.1080/00031305.2022.2041482> that creates representative records for use in downstream tasks after entity resolution is performed. Multiple methods for creating the representative records (data sets) are provided.

An interface to the Open Tree of Life API to retrieve phylogenetic trees, information about studies used to assemble the synthetic tree, and utilities to match taxonomic names to Open Tree identifiers'. The Open Tree of Life aims at assembling a comprehensive phylogenetic tree for all named species.

This package provides a template model module, tools to help find model modules derived from this template and a programming syntax to use these modules in health economic analyses. These elements are the foundation for a prototype software framework for developing living and transferable models and using those models in reproducible health economic analyses. The software framework is extended by other R libraries. For detailed documentation about the framework and how to use it visit <https://www.ready4-dev.com/>. For a background to the methodological issues that the framework is attempting to help solve, see Hamilton et al. (2024) <doi:10.1007/s40273-024-01378-8>.

This package provides tools for simulating synthetic survival data using a variety of methods, including kernel density estimation, parametric distribution fitting, and bootstrap resampling techniques for a desired sample size.

Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a ptime object (but this needs recompilation with a #define set).

Parser for SQL statements. Currently, it supports parsing of only SELECT statements.

Translation of the MATLAB program Carb (Nathan and Mauz 2008 <DOI:10.1016/j.radmeas.2007.12.012>; Mauz and Hoffmann 2014) for dose rate modelling for carbonate-rich samples in the context of trapped charged dating (e.g., luminescence dating) applications.

Rapid7 collects cybersecurity data and makes it available via their Open Data <http://opendata.rapid7.com> portal which has an API. Tools are provided to assist in querying for available data sets and downloading any data set authorized to a free, registered account.

Uses convolution-based techniques to generate simulated camera bokeh, depth of field, and other camera effects, using an image and an optional depth map. Accepts both filename inputs and in-memory array representations of images and matrices. Includes functions to perform 2D convolutions, reorient and resize images/matrices, add image and text overlays, generate camera vignette effects, and add titles to images.

Render scenes using pathtracing. Build 3D scenes out of spheres, cubes, planes, disks, triangles, cones, curves, line segments, cylinders, ellipsoids, and 3D models in the Wavefront OBJ file format or the PLY Polygon File Format. Supports several material types, textures, multicore rendering, and tone-mapping. Based on the "Ray Tracing in One Weekend" book series. Peter Shirley (2018) <https://raytracing.github.io>.

Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) <doi:10.1016/S1574-0099(05)02017-6> for DCCV.