An extension of the Fisher Scoring Algorithm to combine PLS regression with GLM estimation in the multivariate context. Covariates can also be grouped in themes.

Allows you to make clean, good-looking scatter plots with the option to easily add marginal density or box plots on the axes. It is also available as a module for jamovi (see <https://www.jamovi.org> for more information). Scatr is based on the cowplot package by Claus O. Wilke and the ggplot2 package by Hadley Wickham.

Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE, heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.

Calculate Kernel Density Estimation (KDE) for spatial data. The algorithm is inspired by the tool Heatmap from QGIS'. The method is described by: Hart, T., Zandbergen, P. (2014) <doi:10.1108/PIJPSM-04-2013-0039>, Nelson, T. A., Boots, B. (2008) <doi:10.1111/j.0906-7590.2008.05548.x>, Chainey, S., Tompson, L., Uhlig, S.(2008) <doi:10.1057/palgrave.sj.8350066>.

Generate objects that simulate survival times. Random values for the distributions are generated using the method described by Bender (2003) <https://epub.ub.uni-muenchen.de/id/eprint/1716> and Leemis (1987) in Operations Research, 35(6), 892â 894.

This package implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk simulation (Kenett & Austerweil, 2016 <http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf>), and a function to compute global network measures. Significance tests and plotting features are also implemented.

Calculate and compare lower confidence bounds for binomial series system reliability. The R shiny application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.

Multi-generational pedigree inference from incomplete data on hundreds of SNPs, including parentage assignment and sibship clustering. See Huisman (2017) (<DOI:10.1111/1755-0998.12665>) for more information.

This package contains functions and datasets for math taught in school. A main focus is set to prime-calculation.

This package provides a bridge is created between existing robust open-source record linkage algorithms and an urgently needed user-friendly platform that removes financial and technical barriers, setting a new standard for data interoperability in public health and bioinformatics. The fastLink algorithms are used for matching. Ted Enamorado et al. (2019) <doi:10.1017/S0003055418000783>.

Create carousels using the JavaScript library Swiper and the package htmlwidgets'. The carousels can be displayed in the RStudio viewer pane, in Shiny applications and in R markdown documents. The package also provides a RStudio addin allowing to choose image files and to display them in the viewer pane.

Enables instrumentation of Shiny apps for tracking user session events such as input changes, browser type, and session duration. These events can be sent to any of the available storage backends and analyzed using the included Shiny app to gain insights about app usage and adoption.

Fetch data on targeted public investments from Plataforma +Brasil (SICONV) <http://plataformamaisbrasil.gov.br/>, the responsible system for requests, execution, and monitoring of federal discretionary transfers in Brazil.

This package provides basic functionality for labeling iso- & anisotropic percolation clusters on 2D & 3D square lattices with various lattice sizes, occupation probabilities, von Neumann & Moore (1,d)-neighborhoods, and random variables weighting the percolation lattice sites.

Browser notifications in Shiny apps, using toastr': <https://github.com/CodeSeven/toastr#readme>.

This package performs repeated nested cross-validation for Cox Proportionate Hazards, Cox Lasso, Survival Random Forest, and their ensemble. Returns internally validated concordance index, time-dependent area under the curve, Brier score, calibration slope, and statistical testing of non-linear ensemble outperforming the baseline Cox model. In this, it helps researchers to quantify the gain of using a more complex survival model, or justify its redundancy. Equally, it shows the performance value of the non-linear and interaction terms, and may highlight the need of further feature transformation. Further details can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) "Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes" <doi:10.1007/978-3-031-08337-2_15>, where the method is described as Ensemble 1.

Convert a time series of observations to a time series of standardised indices that can be used to monitor variables on a common and probabilistically interpretable scale. The indices can be aggregated and rescaled to different time scales, visualised using plot capabilities, and calculated using a range of distributions. This includes flexible non-parametric and non-stationary methods.

This package provides a platform for computing competition indices and experimenting with spatially explicit individual-based vegetation models.

This package provides a collection of data processing, visualization, and export functions to support soil survey operations. Many of the functions build on the `SoilProfileCollection` S4 class provided by the aqp package, extending baseline visualization to more elaborate depictions in the context of spatial and taxonomic data. While this package is primarily developed by and for the USDA-NRCS, in support of the National Cooperative Soil Survey, the authors strive for generalization sufficient to support any soil survey operation. Many of the included functions are used by the SoilWeb suite of websites and movile applications. These functions are provided here, with additional documentation, to enable others to replicate high quality versions of these figures for their own purposes.

Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate the difference between two binomial proportions. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of binomial observations are provided. In all cases, estimation of the difference between two binomial proportions is considered. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L., du Berger R. and Bélisle P. (1997) <doi:10.1002/(sici)1097-0258(19970415)16:7%3C769::aid-sim495%3E3.0.co;2-v>.

Generates/modifies RNA-seq data for use in simulations. We provide a suite of functions that will add a known amount of signal to a real RNA-seq dataset. The advantage of using this approach over simulating under a theoretical distribution is that common/annoying aspects of the data are more preserved, giving a more realistic evaluation of your method. The main functions are select_counts(), thin_diff(), thin_lib(), thin_gene(), thin_2group(), thin_all(), and effective_cor(). See Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the implemented methods.

This package contains functions for estimating the STARTS model of Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>, <DOI:10.1037/10409-008>. Penalized maximum likelihood estimation and Markov Chain Monte Carlo estimation are also provided, see Luedtke, Robitzsch and Wagner (2018) <DOI:10.1037/met0000155>.

This package creates a numeric guide for writing the formula for the determinant of a square matrix (a detguide) as a function of the elements of the matrix and writes out that formula, the symbolic representation.

Data and functions to support Bayesian and frequentist inference and decision making for the Coursera Specialization "Statistics with R". See <https://github.com/StatsWithR/statsr> for more information.