This package provides functions to compute upper Clopper-Pearson confidence limits of early life failure probabilities and required sample sizes of burn-in studies under further available information, e.g. from other products or technologies.

Estimation and inference methods for bounding average treatment effects (on the treated) that are valid under an unconfoundedness assumption. The bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. For more details, see the paper by Lee and Weidner (2021), "Bounding Treatment Effects by Pooling Limited Information across Observations," <arXiv:2111.05243>.

Simulation and pricing routines for rare-event options using Adaptive Multilevel Splitting and standard Monte Carlo under Black-Scholes and Heston models. Core routines are implemented in C++ via Rcpp and RcppArmadillo with lightweight R wrappers.

The Genetic Algorithm (GA) is a type of optimization method of Evolutionary Algorithms. It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. The hybrid GAs combine the exploration strength of a stochastic GAs with the exact convergence ability of any type of deterministic local search algorithms such as simulated-annealing, in addition to other nature-inspired algorithms such as ant colony optimization, particle swarm optimization etc. The package adana includes a rich working environment with its many functions that make possible to build and work regular GA, adaptive GA, hybrid GA and hybrid adaptive GA for any kind of optimization problems. Cebeci, Z. (2021, ISBN: 9786254397448).

This package provides a lightweight, dependency-free toolbox for pre-processing XY data from experimental methods (i.e. any signal that can be measured along a continuous variable). This package provides methods for baseline estimation and correction, smoothing, normalization, integration and peaks detection. Baseline correction methods includes polynomial fitting as described in Lieber and Mahadevan-Jansen (2003) <doi:10.1366/000370203322554518>, Rolling Ball algorithm after Kneen and Annegarn (1996) <doi:10.1016/0168-583X(95)00908-6>, SNIP algorithm after Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, 4S Peak Filling after Liland (2015) <doi:10.1016/j.mex.2015.02.009> and more.

Generates data for challenging machine learning models in Arena <https://arena.drwhy.ai> - an interactive web application. You can start the server with XAI (Explainable Artificial Intelligence) plots to be generated on-demand or precalculate and auto-upload data file beside shareable Arena URL.

In social and educational settings, the use of Artificial Intelligence (AI) is a challenging task. Relevant data is often only available in handwritten forms, or the use of data is restricted by privacy policies. This often leads to small data sets. Furthermore, in the educational and social sciences, data is often unbalanced in terms of frequencies. To support educators as well as educational and social researchers in using the potentials of AI for their work, this package provides a unified interface for neural nets in PyTorch to deal with natural language problems. In addition, the package ships with a shiny app, providing a graphical user interface. This allows the usage of AI for people without skills in writing python/R scripts. The tools integrate existing mathematical and statistical methods for dealing with small data sets via pseudo-labeling (e.g. Cascante-Bonilla et al. (2020) <doi:10.48550/arXiv.2001.06001>) and imbalanced data via the creation of synthetic cases (e.g. Islam et al. (2012) <doi:10.1016/j.asoc.2021.108288>). Performance evaluation of AI is connected to measures from content analysis which educational and social researchers are generally more familiar with (e.g. Berding & Pargmann (2022) <doi:10.30819/5581>, Gwet (2014) <ISBN:978-0-9708062-8-4>, Krippendorff (2019) <doi:10.4135/9781071878781>). Estimation of energy consumption and CO2 emissions during model training is done with the python library codecarbon'. Finally, all objects created with this package allow to share trained AI models with other people.

Estimate ideal efficiencies of aerosol sampling through sample lines. Functions were developed consistent with the approach described in Hogue, Mark; Thompson, Martha; Farfan, Eduardo; Hadlock, Dennis, (2014), "Hand Calculations for Transport of Radioactive Aerosols through Sampling Systems" Health Phys 106, 5, S78-S87, <doi:10.1097/HP.0000000000000092>.

Some convenient functions to work with arrays.

Gives some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson [see Fay and Kim <doi:10.1002/bimj.201600111>], sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test [with effect estimates and confidence intervals, see Fay and Malinovsky <doi:10.1002/sim.7890>], two-sample melding tests [see Fay, Proschan, and Brittain <doi:10.1111/biom.12231>], one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]), prevalence confidence intervals that adjust for sensitivity and specificity [see Lang and Reiczigel, 2014 <doi:10.1016/j.prevetmed.2013.09.015>] or Bayer, Fay, and Graubard, 2023 <doi:10.48550/arXiv.2205.13494>). The focus is on hypothesis tests that have compatible confidence intervals, but some functions only have confidence intervals (e.g., prevSeSp).

Calculate AZTIâ s Marine Biotic Index - AMBI. The included list of benthic fauna species according to their sensitivity to pollution. Matching species in sample data to the list allows the calculation of fractions of individuals in the different sensitivity categories and thereafter the AMBI index. The Shannon Diversity Index H and the Danish benthic fauna quality index DKI (Dansk Kvalitetsindeks) can also be calculated, as well as the multivariate M-AMBI index. Borja, A., Franco, J. ,Pérez, V. (2000) "A marine biotic index to establish the ecological quality of soft bottom benthos within European estuarine and coastal environments" <doi:10.1016/S0025-326X(00)00061-8>.

An unofficial companion to the textbook "Applied Regression Analysis" by N.R. Draper and H. Smith (3rd Ed., 1998) including all the accompanying datasets.

Plot party trees in left-right orientation instead of the classical top-down layout.

This package provides tools for downloading and extracting data from the Copernicus "Agrometeorological indicators from 1979 to present derived from reanalysis" <https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview> (AgERA5).

Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. AntAngioCOOL exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, AntAngioCOOL reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset.

Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.

This package provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself.

This package provides convenience functions for programming with magrittr pipes. Conditional pipes, a string prefixer and a function to pipe the given object into a specific argument given by character name are currently supported. It is named after the dadaist Hans Arp, a friend of Rene Magritte.

Functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. Part of the indices are expressed in terms of equivalent numbers of species. The package also provides ways to partition biodiversity across spatial or temporal scales (alpha, beta, gamma diversities). In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities.

Package that simulates adaptive (multi-arm, multi-stage) clinical trials using adaptive stopping, adaptive arm dropping, and/or adaptive randomisation. Developed as part of the INCEPT (Intensive Care Platform Trial) project (<https://incept.dk/>), primarily supported by a grant from Sygeforsikringen "danmark" (<https://www.sygeforsikring.dk/>).

Made to make your life simpler with packages, by installing and loading a list of packages, whether they are on CRAN, Bioconductor or github. For github, if you do not have the full path, with the maintainer name in it (e.g. "achateigner/topReviGO"), it will be able to load it but not to install it.

Visual exploration and presentation of networks should not be difficult. This package includes functions for plotting networks and network-related metrics with sensible and pretty defaults. It includes ggplot2'-based plot methods for many popular network package classes. It also includes some novel layout algorithms, and options for straightforward, consistent themes.

Stepwise Uncertainty Reduction criterion and algorithm for sequentially learning a Gaussian Process Classifier as described in Menz et al. (2025).

This package provides a developer-facing interface to the Arrow Database Connectivity ('ADBC') SQLite driver for the purposes of building high-level database interfaces for users. ADBC <https://arrow.apache.org/adbc/> is an API standard for database access libraries that uses Arrow for result sets and query parameters.