Generation of bioclimatic rasters that are complementary to the typical 19 bioclim variables.

This package provides a comprehensive toolkit for single-cell annotation with the CellMarker2.0 database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi: 10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA<https://portal.gdc.cancer.gov/> and GEO<https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi: 10.1101/2024.09.14.609619> for more details.

Predicts enrollment and events assumed enrollment and treatment-specific time-to-event models, and calculates test statistics for time-to-event data with cured population based on the simulation.Methods for prediction event in the existence of cured population are as described in : Chen, Tai-Tsang(2016) <doi:10.1186/s12874-016-0117-3>.

Implementation of a modular framework for ecosystem risk assessments, combining existing risk assessment approaches tailored to semi-quantitative and quantitative analyses.

Get high-resolution (1 km) daily climate data (precipitation, minimum and maximum temperatures) for points and polygons within Europe.

It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.

Error-driven learning (based on the Widrow & Hoff (1960)<https://isl.stanford.edu/~widrow/papers/c1960adaptiveswitching.pdf> learning rule, and essentially the same as Rescorla-Wagner's learning equations (Rescorla & Wagner, 1972, ISBN: 0390718017), which are also at the core of Naive Discrimination Learning, (Baayen et al, 2011, <doi:10.1037/a0023851>) can be used to explain bottom-up human learning (Hoppe et al, <doi:10.31234/osf.io/py5kd>), but is also at the core of artificial neural networks applications in the form of the Delta rule. This package provides a set of functions for building small-scale simulations to investigate the dynamics of error-driven learning and it's interaction with the structure of the input. For modeling error-driven learning using the Rescorla-Wagner equations the package ndl (Baayen et al, 2011, <doi:10.1037/a0023851>) is available on CRAN at <https://cran.r-project.org/package=ndl>. However, the package currently only allows tracing of a cue-outcome combination, rather than returning the learned networks. To fill this gap, we implemented a new package with a few functions that facilitate inspection of the networks for small error driven learning simulations. Note that our functions are not optimized for training large data sets (no parallel processing), as they are intended for small scale simulations and course examples. (Consider the python implementation pyndl <https://pyndl.readthedocs.io/en/latest/> for that purpose.).

Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.

Fits dose-response models using an evolutionary algorithm to estimate the model parameters. The procedure currently can fit 3-parameter, 4-parameter, and 5-parameter log-logistic models as well as exponential models. Functions are also provided to plot, make predictions, and calculate confidence intervals for the resulting models. For details see "Nonlinear Dose-response Modeling of High-Throughput Screening Data Using an Evolutionary Algorithm", Ma, J., Bair, E., Motsinger-Reif, A.; Dose-Response 18(2):1559325820926734 (2020) <doi:10.1177/1559325820926734>.

Automatic generation of exams based on exercises in Markdown or LaTeX format, possibly including R code for dynamic generation of exercise elements. Exercise types include single-choice and multiple-choice questions, arithmetic problems, string questions, and combinations thereof (cloze). Output formats include standalone files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2, QTI 2.1, Blackboard, Canvas, OpenOlat, ILIAS, TestVision, Particify, ARSnova, Kahoot!, Grasple, and TCExam. In addition to fully customizable PDF exams, a standardized PDF format (NOPS) is provided that can be printed, scanned, and automatically evaluated.

Estimate the effective reproduction number from wastewater and clinical data sources.

Testing for and dating periods of explosive dynamics (exuberance) in time series using the univariate and panel recursive unit root tests proposed by Phillips et al. (2015) <doi:10.1111/iere.12132> and Pavlidis et al. (2016) <doi:10.1007/s11146-015-9531-2>.The recursive least-squares algorithm utilizes the matrix inversion lemma to avoid matrix inversion which results in significant speed improvements. Simulation of a variety of periodically-collapsing bubble processes. Details can be found in Vasilopoulos et al. (2022) <doi:10.18637/jss.v103.i10>.

Implementation of the Edge Selection Algorithm for undirected graph selection. The least angle regression-based algorithm selects edges of an undirected graph based on the projection of the current residuals on the two dimensional edge-planes. The algorithm selects symmetric adjacency matrix, which many other regression-based undirected graph selection procedures cannot do.

Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two families of functions that allow you to download and interactively check all countries and rounds available.

This package provides functions for treatment effect estimation, hypothesis testing, and future study design for settings where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully measured in the remaining patients. More details are available in: Knowlton, R., Parast, L. (2024) ``Efficient Testing Using Surrogate Information," Biometrical Journal, 67(6): e70086, <doi:10.1002/bimj.70086>. A tutorial for this package can be found at <https://www.laylaparast.com/etsi>.

An ensemble method for the statistical detection of a rare class in two-class classification problems. The method uses an ensemble of classifiers where the constituent models of the ensemble use disjoint subsets (phalanxes) of explanatory variables. We provide an implementation of the phalanx-formation algorithm. Please see Tomal et al. (2015) <doi:10.1214/14-AOAS778>, Tomal et al. (2016) <doi:10.1021/acs.jcim.5b00663>, and Tomal et al. (2019) <arXiv:1706.06971> for more details.

Enables simulation of water piping networks using EPANET'. The package provides functions from the EPANET programmer's toolkit as R functions so that basic or customized simulations can be carried out from R. The package uses EPANET version 2.2 from Open Water Analytics <https://github.com/OpenWaterAnalytics/EPANET/releases/tag/v2.2>.

Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2018) <doi:10.1111/2041-210X.12918>. The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation.

This package provides a collection of nice plotting functions directly from a data.frame with limited customisation possibilities.

Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

This package provides all electivity algorithms (including Vanderploeg and Scavia electivity) that were examined in Lechowicz (1982) <doi:10.1007/BF00349007>, plus the example data that were provided for moth resource utilisation.

An extension of knitr that adds flexibility in several ways. One common source of frustration with knitr is that it assumes the directory where the source file lives should be the working directory, which is often not true. ezknitr addresses this problem by giving you complete control over where all the inputs and outputs are, and adds several other convenient features to make rendering markdown/HTML documents easier.

This package provides a data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance.

Analyses EuFMDiS output files in a Shiny App. The distributions of relevant output parameters are described in form of tables (quantiles) and plots. The App is called using eufmdis.adapt::run_adapt().