This package provides routing based on the path-tree Rust crate. The routing is general purpose in the sense that any type of R object can be associated with a path, not just a handler function.

Post-construction fatality monitoring studies at wind facilities are based on data from searches for bird and bat carcasses in plots beneath turbines. Bird and bat carcasses can fall outside of the search plot. Bird and bat carcasses from wind turbines often fall outside of the searched area. To compensate, area correction (AC) estimations are calculated to estimate the percentage of fatalities that fall within the searched area versus those that fall outside of it. This package provides two likelihood based methods and one physics based method (Hull and Muir (2010) <doi:10.1080/14486563.2010.9725253>, Huso and Dalthorp (2014) <doi:10.1002/jwmg.663>) to estimate the carcass fall distribution. There are also functions for calculating the proportion of area searched within one unit annuli, log logistic distribution functions, and truncated distribution functions.

This package implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.

Generate data frames from templates.

This package implements Weighted-Average Least Squares model averaging for negative binomial regression models of Huynh (2024) <doi:10.48550/arXiv.2404.11324>, generalized linear models of De Luca, Magnus, Peracchi (2018) <doi:10.1016/j.jeconom.2017.12.007> and linear regression models of Magnus, Powell, Pruefer (2010) <doi:10.1016/j.jeconom.2009.07.004>, see also Magnus, De Luca (2016) <doi:10.1111/joes.12094>. Weighted-Average Least Squares for the linear regression model is based on the original MATLAB code by Magnus and De Luca <https://www.janmagnus.nl/items/WALS.pdf>, see also Kumar, Magnus (2013) <doi:10.1007/s13571-013-0060-9> and De Luca, Magnus (2011) <doi:10.1177/1536867X1201100402>.

Estimates the Vevea and Hedges (1995) weight-function model. By specifying arguments, users can also estimate the modified model described in Vevea and Woods (2005), which may be more practical with small datasets. Users can also specify moderators to estimate a linear model. The package functionality allows users to easily extract the results of these analyses as R objects for other uses. In addition, the package includes a function to launch both models as a Shiny application. Although the Shiny application is also available online, this function allows users to launch it locally if they choose.

This package provides a weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc. Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation. Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. <https://ui.adsabs.harvard.edu/abs/2019AGUFMGC41G1267G>.

It generates the roster of turn for an outlet which is flowing (water) 24X7 or 168 hours towards the area under command or agricutural area (to be irrigated). The area under command is differentially owned by different individual farmers. The Outlet runs for free of cost to irrigate the area under command 24X7. So, flow time of the outlet has to be divided based on an area owned by an individual farmer and the location of his land or farm. This roster is known as warabandi and its generation in agriculture practices is a very tedious task. Calculations of time in microseconds are more error-prone, especially whenever it is performed by hands. That division of flow time for an individual farmer can be calculated by warabandi'. However, it generates a full publishable report for an outlet and all the farmers who have farms subjected to be irrigated. It reduces error risk and makes a more reproducible roster. For more details about warabandi system you can found elsewhere in Bandaragoda DJ(1995) <https://publications.iwmi.org/pdf/H_17571i.pdf>.

Toolkit to support and perform discrete event simulations with and without resource constraints in the context of health technology assessments (HTA). The package focuses on cost-effectiveness modelling and aims to be submission-ready to relevant HTA bodies in alignment with NICE TSD 15 <https://sheffield.ac.uk/nice-dsu/tsds/patient-level-simulation>. More details an examples can be found in the package website <https://jsanchezalv.github.io/WARDEN/>.

The word puzzle game requires you to find out the letters in a word within a limited number of guesses. In each round, if your guess hit any letters in the word, they reveal themselves. If all letters are revealed before your guesses run out, you win this game; otherwise you fail. You may run multiple games to guess different words.

Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986).

This package provides a not uncommon task for quants is to create waterfall charts'. There seems to be no simple way to do this in ggplot2 currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a ggplot2 object. Some flexibility is provided, though often the object created will need to be modified through a theme.

Extract features and classify documents with noisy labels given by document-meta data or keyword matching Watanabe & Zhou (2020) <doi:10.1177/0894439320907027>.

Top-Down mass spectrometry aims to identify entire proteins as well as their (post-translational) modifications or ions bound (eg Chen et al (2018) <doi:10.1021/acs.analchem.7b04747>). The pattern of internal fragments (Haverland et al (2017) <doi:10.1007/s13361-017-1635-x>) may reveal important information about the original structure of the proteins studied (Skinner et al (2018) <doi:10.1038/nchembio.2515> and Li et al (2018) <doi:10.1038/nchem.2908>). However, the number of possible internal fragments gets huge with longer proteins and subsequent identification of internal fragments remains challenging, in particular since the the accuracy of measurements with current mass spectrometers represents a limiting factor. This package attempts to deal with the complexity of internal fragments and allows identification of terminal and internal fragments from deconvoluted mass-spectrometry data.

Search and download data from the World Bank Data API.

Select data analysis plots, under a standardized calling interface implemented on top of ggplot2 and plotly'. Plots of interest include: ROC', gain curve, scatter plot with marginal distributions, conditioned scatter plot with marginal densities, box and stem with matching theoretical distribution, and density with matching theoretical distribution.

Computes Bayesian wavelet shrinkage credible intervals for nonparametric regression. The method uses cumulants to derive Bayesian credible intervals for wavelet regression estimates. The first four cumulants of the posterior distribution of the estimates are expressed in terms of the observed data and integer powers of the mother wavelet functions. These powers are closely approximated by linear combinations of wavelet scaling functions at an appropriate finer scale. Hence, a suitable modification of the discrete wavelet transform allows the posterior cumulants to be found efficiently for any data set. Johnson transformations then yield the credible intervals themselves. Barber, S., Nason, G.P. and Silverman, B.W. (2002) <doi:10.1111/1467-9868.00332>.

Analysing convergent evolution using the Wheatsheaf index, described in Arbuckle et al. (2014) <doi: 10.1111/2041-210X.12195>, and some other unrelated but perhaps useful functions.

Apply Wordpiece (<arXiv:1609.08144>) tokenization to input text, given an appropriate vocabulary. The BERT (<arXiv:1810.04805>) tokenization conventions are used by default.

This package provides a convenient data set, a set of helper functions, and a benchmark function for economically (profit) driven wind farm layout optimization. This enables researchers in the field of the NP-hard (non-deterministic polynomial-time hard) problem of wind farm layout optimization to focus on their optimization methodology contribution and also provides a realistic benchmark setting for comparability among contributions. See Croonenbroeck, Carsten & Hennecke, David (2020) <doi:10.1016/j.energy.2020.119244>.

R interface to a W3C Markup Validation service. See <https://validator.w3.org/> for more information.

Time series outlier detection with non parametric test. This is a new outlier detection methodology (washer): efficient for time saving elaboration and implementation procedures, adaptable for general assumptions and for needing very short time series, reliable and effective as involving robust non parametric test. You can find two approaches: single time series (a vector) and grouped time series (a data frame). For other informations: Andrea Venturini (2011) Statistica - Universita di Bologna, Vol.71, pp.329-344. For an informal explanation look at R-bloggers on web.

Mixed effects modeling with warping for functional data using B- spline. Warping coefficients are considered as random effects, and warping functions are general functions, parameters representing the projection onto B- spline basis of a part of the warping functions. Warped data are modelled by a linear mixed effect functional model, the noise is Gaussian and independent from the warping functions.

Streamlines the process of transitioning between data formats commonly used in survival analysis. Functions convert longitudinal data between formats used as input for survival models as well as support overall preparation. Users are able to focus on model building rather than data wrangling.