This package provides two functions frameableWidget()', and frameWidget()'. The frameableWidget() is used to add extra code to a htmlwidget which allows is to be rendered correctly inside a responsive iframe'. The frameWidget() is a htmlwidget which displays content of another htmlwidget inside a responsive iframe'. These functions allow for easier embedding of htmlwidgets in content management systems such as wordpress', blogger etc. They also allow for separation of widget content from main HTML content where CSS of the main HTML could interfere with the widget.

It shows the connections between selected clusters from the latest time point and the clusters from all the previous time points. The transition matrices between time point t and t+1 are obtained from Waddington-OT analysis <https://github.com/ScialdoneLab/WOTPLY>.

Converts weekly data to monthly data. Users can use three types of week formats: ISO week, epidemiology week (epi week) and calendar date.

Four filters have been chosen namely haar', c6', la8', and bl14 (Kindly refer to wavelets in CRAN repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results are stored in input'. First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as MIN and other values are denoted as NA'. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as MAX and other values are denoted as NA'. output contains the similar number of rows (which is 8) and columns (which is number filter-level combinations) as of input'. Values in output are corresponding NA', MIN or MAX'. Finally, the column containing minimum number of NA values is denoted as the best ('FL'). In special case, if two columns having equal NA', it has been checked among these two columns which one is having least NA in first five rows and has been inferred as the best. FL_metrics_values are the corresponding metrics values. WARIGAANbest is the data frame (dimension: 1*8) containing different metrics of the best filter-level combination. More details can be found in Garai and others (2023) <doi:10.13140/RG.2.2.11977.42087>.

This package provides functions are collected to analyse weather data for agriculture purposes including to read weather records in multiple formats, calculate extreme climate index. Demonstration data are included the SILO daily climate data (licensed under CC BY 4.0, <https://www.longpaddock.qld.gov.au/silo/>).

Import WIG data into R in long format.

Dynamic interaction refers to spatial-temporal associations in the movements of two (or more) animals. This package provides tools for calculating a suite of indices used for quantifying dynamic interaction with wildlife telemetry data. For more information on each of the methods employed see the references within. The package (as of version >= 0.3) also has new tools for automating contact analysis in large tracking datasets. The package (as of version 1.0) uses the move2 class of objects for working with tracking dataset.

Robust and reliable functions to return informative outputs to console with the run or source location of a command. This can be from the RScript'/R terminal commands or RStudio console, source editor, Rmarkdown document and a Shiny application.

First, we provide functions to calculate the partial derivative of the first-passage time diffusion probability density function (PDF) and cumulative distribution function (CDF) with respect to the first-passage time t (only for PDF), the upper barrier a, the drift rate v, the relative starting point w, the non-decision time t0, the inter-trial variability of the drift rate sv, the inter-trial variability of the rel. starting point sw, and the inter-trial variability of the non-decision time st0. In addition the PDF and CDF themselves are also provided. Most calculations are done on the logarithmic scale to make it more stable. Since the PDF, CDF, and their derivatives are represented as infinite series, we give the user the option to control the approximation errors with the argument precision'. For the numerical integration we used the C library cubature by Johnson, S. G. (2005-2013) <https://github.com/stevengj/cubature>. Numerical integration is required whenever sv, sw, and/or st0 is not zero. Note that numerical integration reduces speed of the computation and the precision cannot be guaranteed anymore. Therefore, whenever numerical integration is used an estimate of the approximation error is provided in the output list. Note: The large number of contributors (ctb) is due to copying a lot of C/C++ code chunks from the GNU Scientific Library (GSL). Second, we provide methods to sample from the first-passage time distribution with or without user-defined truncation from above. The first method is a new adaptive rejection sampler building on the works of Gilks and Wild (1992; <doi:10.2307/2347565>) and Hartmann and Klauer (in press). The second method is a rejection sampler provided by Drugowitsch (2016; <doi:10.1038/srep20490>). The third method is an inverse transformation sampler. The fourth method is a "pseudo" adaptive rejection sampler that builds on the first method. For more details see the corresponding help files.

Conducts single coefficient tests and multiple-contrast hypothesis tests of meta-regression models using cluster wild bootstrapping, based on methods examined in Joshi, Pustejovsky, and Beretvas (2022) <DOI:10.1002/jrsm.1554>.

This package provides access to various word embedding methods (GloVe, fasttext and word2vec) to extract word vectors using a unified framework to increase reproducibility and correctness.

This package provides a collection of implementations of classical and novel algorithms for weighted sampling without replacement. Implementations include the algorithm of Efraimidis and Spirakis (2006) <doi:10.1016/j.ipl.2005.11.003> and Wong and Easton (1980) <doi:10.1137/0209009>.

Query Wikidata and get facts from current and historic Wikipedia main pages.

Students learning both econometrics and R may find the introduction to both challenging. The wooldridge data package aims to lighten the task by efficiently loading any data set found in the text with a single command. Data sets have been compressed to a fraction of their original size. Documentation files contain page numbers, the original source, time of publication, and notes from the author suggesting avenues for further analysis and research. If one needs an introduction to R model syntax, a vignette contains solutions to examples from chapters of the text. Data sets are from the 7th edition (Wooldridge 2020, ISBN-13 978-1-337-55886-0), and are backwards compatible with all previous versions of the text.

ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.

This package provides a flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.

Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

Explore data in the wpp2019 (or 2017, 2015, ...) package using a shiny interface.

Implementation of the methodologies described in 1) Alexander Petersen, Xi Liu and Afshin A. Divani (2021) <doi:10.1214/20-aos1971>, including global F tests, partial F tests, intrinsic Wasserstein-infinity bands and Wasserstein density bands, and 2) Chao Zhang, Piotr Kokoszka and Alexander Petersen (2022) <doi:10.1111/jtsa.12590>, including estimation, prediction, and inference of the Wasserstein autoregressive models.

This package provides the functions to perform a Welch's one-way Anova with fixed effects based on summary statistics (sample size, means, standard deviation) and the Games-Howell post hoc test for multiple comparisons and provides the effect size estimator adjusted omega squared. In addition sample size estimation can be computed based on Levy's method, and a Monte Carlo simulation is included to bootstrap residual normality and homoscedasticity Welch, B. L. (1951) <doi:10.1093/biomet/38.3-4.330> Kirk, R. E. (1996) <doi:10.1177/0013164496056005002> Carroll, R. M., & Nordholm, L. A. (1975) <doi:10.1177/001316447503500304> Albers, C., & Lakens, D. (2018) <doi:10.1016/j.jesp.2017.09.004> Games, P. A., & Howell, J. F. (1976) <doi:10.2307/1164979> Levy, K. J. (1978a) <doi:10.1080/00949657808810246> Show-Li, J., & Gwowen, S. (2014) <doi:10.1111/bmsp.12006>.

This package provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) <doi:10.1016/j.amc.2017.05.046>), scale index and windowed scale index (Benitez et al. (2010) <doi:10.1016/j.camwa.2010.05.010>).

This package provides data from the United Nation's World Population Prospects 2019.

This package provides API access to the Walmart Open API <https://developer.walmartlabs.com/>, that contains data about stores, Value of the day and products which includes names, sale prices, shipping rates and taxonomies.

Generates random data sets including: data.frames, lists, and vectors.