Tool for statistical simulations that have two components. One component generates the data and the other one analyzes the data. The main aims of the package are the reduction of the administrative source code (mainly loops and management code for the results) and a simple applicability of the package that allows the user to quickly learn how to work with it. Parallel computing is also supported. Finally, convenient functions are provided to summarize the simulation results.

Determining potential output and the output gap - two inherently unobservable variables - is a major challenge for macroeconomists. sectorgap features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying economic output fluctuations consistent with subsectors of the economy. The proposed model is able to capture various correlations between output and a set of aggregate as well as subsector indicators. Estimation of the latent states and parameters is achieved using a simple Gibbs sampling procedure and various plotting options facilitate the assessment of the results. For details on the methodology and an illustrative example, see Streicher (2024) <https://www.research-collection.ethz.ch/handle/20.500.11850/653682>.

Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a prior on the coefficients that controls over-fitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Additive hazards (relative survival) models, waning treatment effects, and mixture cure models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using Stan', and outputs are arranged in a tidy format. See Jackson (2023) <doi:10.1186/s12874-023-02094-1>.

Reconstructs forest stand basal area and stem density at user-specified reference years using tree-level inventory data. The method estimates missing tree ages, back-calculates diameters using species-specific growth rates, and incorporates decay-class-based decomposition to infer mortality timing for dead trees. Results are returned in a tidy long format suitable for analysis and visualization.

Apply the spectral residual algorithm to data, such as a time series, to detect anomalies. Anomaly scores can be used to determine outliers based upon a threshold or fed into more sophisticated prediction models. Methods are based upon "Time-Series Anomaly Detection Service at Microsoft", Ren, H., Xu, B., Wang, Y., et al., (2019) <doi:10.48550/arXiv.1906.03821>.

Miscellaneous supplementary functions designed to follow idiomatic R conventions. Some functions are simple wrappers that reduce repetitive code, while others address common tasks or extend existing R functions.

This package provides functions to calculate exact critical values, statistical power, expected time to signal, and required sample sizes for performing exact sequential analysis. All these calculations can be done for either Poisson or binomial data, for continuous or group sequential analyses, and for different types of rejection boundaries. In case of group sequential analyses, the group sizes do not have to be specified in advance and the alpha spending can be arbitrarily settled. For regression versions of the methods, Monte Carlo and asymptotic methods are used.

Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).

Proxy forward modelling for sediment archived climate proxies such as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, such as output from a climate model, and details of the sedimentation rate and sampling scheme of a sediment core. Sedproxy returns simulated proxy records. Implements the methods described in Dolman and Laepple (2018) <doi:10.5194/cp-14-1851-2018>.

Collection (syllogi in greek) of real and fictitious data sets for teaching purposes. The datasets were manually entered by the author from the respective references as listed in the individual dataset documentation. The fictions datasets are the creation of the author, that he has found useful for teaching statistics.

This package provides tools for applying Sklar's Omega (Hughes, 2022) <doi:10.1007/s11222-022-10105-2> methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e., non-negative real numbers), and balances (i.e., any real number). The framework can accommodate any number of units, any number of coders, and missingness; and can be used to measure agreement with a gold standard, intra-coder agreement, and/or inter-coder agreement. Frequentist inference is supported for all levels of measurement. Bayesian inference is supported for continuous scores only.

Message translation is often managed with po files and the gettext programme, but sometimes another solution is needed. In contrast to po files, a more flexible approach is used as in the Fluent <https://projectfluent.org/> project with R Markdown snippets. The key-value approach allows easier handling of the translated messages.

This package provides functions for analysis of network objects, which are imported or simulated by the package. The non-parametric methods of analysis center on snowball and bootstrap sampling for estimating functions of network degree distribution. For other parameters of interest, see, e.g., bootnet package.

This package provides functions for sample size estimation and simulation in clinical trials. Includes methods for selecting the best group using the Indifference-zone approach, as well as designs for non-inferiority, equivalence, and negative binomial models. For the sample size calculation for non-inferiority of vaccines, the approach is based on Fleming, Powers, and Huang (2021) <doi:10.1177/1740774520988244>. The Indifference-zone approach is based on Sobel and Huyett (1957) <doi:10.1002/j.1538-7305.1957.tb02411.x> and Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9).

Cellular population mapping (CPM) a deconvolution algorithm in which single-cell genomics is required in only one or a few samples, where in other samples of the same tissue, only bulk genomics is measured and the underlying fine resolution cellular heterogeneity is inferred.

Creating a great user interface for your Shiny apps can be a hassle, especially if you want to work purely in R and don't want to use, for instance HTML templates. This package adds support for a powerful UI library Fomantic UI - <https://fomantic-ui.com/> (before Semantic). It also supports universal UI input binding that works with various DOM elements.

This package provides a tool to calculate sky illuminance values (in lux) for both sun and moon. The model is a translation of the Fortran code by Janiczek and DeYoung (1987) <https://archive.org/details/DTIC_ADA182110>.

This package provides functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Single and two-stage sampling designs are considered. Some approximations for the second-order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets.

This package provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the mapproj package.

Models with skewâ normally distributed and thus asymmetric error terms, implementing the methods developed in Badunenko and Henderson (2023) "Production analysis with asymmetric noise" <doi:10.1007/s11123-023-00680-5>. The package provides tools to estimate regression models with skewâ normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms.

Collect your data on digital marketing campaigns from Snapchat Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

Computes the entire solution paths for Support Vector Regression(SVR) with respect to the regularization parameter, lambda and epsilon in epsilon-intensive loss function, efficiently. We call each path algorithm svrpath and epspath. See Wang, G. et al (2008) <doi:10.1109/TNN.2008.2002077> for details regarding the method.

Reimplementation of the svDialogs dialog boxes in Tcl/Tk.

This package provides a set of tools dedicated to modeling food web transfer based on an initial ground raster. It provides a directed acyclic graph structure for a set of rasters representing the flow of elements (e.g., food, energy, contaminants). It also includes tools for working with dispersal algorithms, enabling the combination of flux data with population movement.