Fit and explore Drift Diffusion Models (DDMs), a common tool in psychology for describing decision processes in simple tasks. It can handle both time-independent and time-dependent DDMs. You either choose prebuilt models or create your own, and the package takes care of model predictions and parameter estimation. Model predictions are derived via the numerical solutions provided by Richter, Ulrich, and Janczyk (2023, <doi:10.1016/j.jmp.2023.102756>).

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>).

This package provides various tools for analysing density profiles obtained by resistance drilling. It can load individual or multiple files and trim the starting and ending part of each density profile. Tools are also provided to trim profiles manually, to remove the trend from measurements using several methods, to plot the profiles and to detect tree rings automatically. Written with a focus on forestry use of resistance drilling in standing trees.

This package contains functions for estimating the parameters of infiltration and water retention models using the curve-fitting methods as discussed in Omuto and Gumbe (2009) ("Estimating water infiltration and retention characteristics using a computer program in R"<doi:10.1016/j.cageo.2008.08.011>). The models considered are those that are commonly used in soil science. Version 2 of the package has new models for water retention characteristic curves.

Estimation of life expectancy and Life Years Lost (LYL, or lillies for short) for a given population, for example those with a given disease or condition. In addition, the package can be used to compare estimates from different populations, or to estimate confidence intervals. Technical details of the method are available in Plana-Ripoll et al. (2020) <doi:10.1371/journal.pone.0228073> and Andersen (2017) <doi:10.1002/sim.7357>.

This package provides tools to quantify ecological memory in long time-series with Random Forest models (Breiman 2001 <doi:10.1023/A:1010933404324>) fitted with the ranger library (Wright and Ziegler 2017 <doi:10.18637/jss.v077.i01>). Particularly oriented to palaeoecological datasets and simulated pollen curves produced by the virtualPollen package, but also applicable to other long time-series involving a set of environmental drivers and a biotic response.

Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen").

Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions.

Includes: (i) tests and visualisations that can help the modeller explore time series components and perform decomposition; (ii) modelling shortcuts, such as functions to construct lagmatrices and seasonal dummy variables of various forms; (iii) an implementation of the Theta method; (iv) tools to facilitate the design of the forecasting process, such as ABC-XYZ analyses; and (v) "quality of life" functions, such as treating time series for trailing and leading values.

This package provides a Bayesian method for quantifying the liklihood that a given plasma mutation arises from clonal hematopoesis or the underlying tumor. It requires sequencing data of the mutation in plasma and white blood cells with the number of distinct and mutant reads in both tissues. We implement a Monte Carlo importance sampling method to assess the likelihood that a mutation arises from the tumor relative to non-tumor origin.

This package provides a set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3 alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment).

EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of whole blood, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies.

This package implements various estimators of entropy, such as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood and the Millow-Madow estimator, various Bayesian estimators, and the Chao-Shen estimator. It also offers an R interface to the NSB estimator. Furthermore, it provides functions for estimating Kullback-Leibler divergence, chi-squared, mutual information, and chi-squared statistic of independence. In addition there are functions for discretizing continuous random variables.

This package provides a system for embedded scientific computing and reproducible research with R. The OpenCPU server exposes a simple but powerful HTTP API for RPC and data interchange with R. This provides a reliable and scalable foundation for statistical services or building R web applications. The OpenCPU server runs either as a single-user development server within the interactive R session, or as a multi-user stack based on Apache2.

Perform common useful JavaScript operations in Shiny apps that will greatly improve your apps without having to know any JavaScript. Examples include: hiding an element, disabling an input, resetting an input back to its original value, delaying code execution by a few seconds, and many more useful functions for both the end user and the developer. Shinyjs can also be used to easily call your own custom JavaScript functions from R.

This package provides the Open Source Geometry Engine (GEOS) as a C API that can be used to write high-performance C and C++ geometry operations using R as an interface. Headers are provided to make linking to and using these functions from C++ code as easy and as safe as possible. This package contains an internal copy of the GEOS library to guarantee the best possible consistency on multiple platforms.

This package is a collection of ANSI escape code related libraries enabling ANSI colorization and stylization of console output. Included in the library are the Code module, which defines ANSI codes as constants and methods, a Mixin module for including color methods, a Logger, a ProgressBar, and a String subclass. The library also includes a Terminal module which provides information about the current output device.

Rcpp Bindings for the C code of the Corpus Workbench ('CWB'), an indexing and query engine to efficiently analyze large corpora (<https://cwb.sourceforge.io>). RcppCWB is licensed under the GNU GPL-3, in line with the GPL-3 license of the CWB (<https://www.r-project.org/Licenses/GPL-3>). The CWB relies on pcre2 (BSD license, see <https://github.com/PCRE2Project/pcre2/blob/master/LICENCE.md>) and GLib (LGPL license, see <https://www.gnu.org/licenses/lgpl-3.0.en.html>). See the file LICENSE.note for further information. The package includes modified code of the rcqp package (GPL-2, see <https://cran.r-project.org/package=rcqp>). The original work of the authors of the rcqp package is acknowledged with great respect, and they are listed as authors of this package. To achieve cross-platform portability (including Windows), using Rcpp for wrapper code is the approach used by RcppCWB'.

This package provides functions and command-line user interface to generate allocation sequence by response-adaptive randomization for clinical trials. The package currently supports two families of frequentist response-adaptive randomization procedures, Doubly Adaptive Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square (or ANOVA') hypothesis testing methods are also available in the package to facilitate the inference for treatment effect. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. For example, plots for relationship among assumed treatment effects, sample size, and power are provided. Five allocation functions for DBCD and six addition rule functions for SEU are implemented to target allocations such as Neyman', Rosenberger Rosenberger et al. (2001) <doi:10.1111/j.0006-341X.2001.00909.x> and Urn allocations.

Calculate confidence intervals for alpha and standardized alpha using asymptotic theory or the studentized bootstrap, with or without transformations. Supports the asymptotic distribution-free method of Maydeu-Olivares, et al. (2007) <doi:10.1037/1082-989X.12.2.157>, the pseudo-elliptical method of Yuan & Bentler (2002) <doi:10.1007/BF02294845>, and the normal method of van Zyl et al. (1999) <doi:10.1007/BF02296146>, for both coefficient alpha and standardized alpha.

This package provides methods of computerized adaptive testing for survey researchers. See Montgomery and Rossiter (2020) <doi:10.1093/jssam/smz027>. Includes functionality for data fit with the classic item response methods including the latent trait model, Birnbaum`s three parameter model, the graded response, and the generalized partial credit model. Additionally, includes several ability parameter estimation and item selection routines. During item selection, all calculations are done in compiled C++ code.

Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table. The main rule is to replicate the observation if it could be assigned to a few categories. Then using frequencies or statistical methods to approximate the probabilities of being assigned to each of them. This procedure was invented and implemented in the paper by Nasinski, Majchrowska, and Broniatowska (2020) <doi:10.24425/cejeme.2020.134747>.

This package provides constructions of series of partially balanced incomplete block designs (PBIB) based on the combinatory method S, introduced by Rezgui et al. (2014) <doi:10.3844/jmssp.2014.45.48>. This package also offers the associated U-type designs. Version 1.1-1 generalizes the approach to designs with v = wnl treatments. It includes various rectangular and generalized rectangular right angular association schemes with 4, 5, and 7 associated classes.

It is an open source insurance claim simulation engine sponsored by the Casualty Actuarial Society. It generates individual insurance claims including open claims, reopened claims, incurred but not reported claims and future claims. It also includes claim data fitting functions to help set simulation assumptions. It is useful for claim level reserving analysis. Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.