This package provides functions to query databases and notes in Notion', using the official REST API. To learn more about the functionality of the Notion API, see <https://developers.notion.com/>.

This package provides tools for converting Open-Source Tools for Training Resources (OTTR) courses into Leanpub or Coursera courses. ottrpal is for use with the OTTR Template repository to create courses.

Allows code to be run only once on a given computer, using lockfiles. Typical use cases include startup messages shown only when a package is loaded for the very first time.

Permute treatment labels for taxa and environmental gradients to generate an empirical distribution of change points. This is an extension for the TITAN2 package <https://cran.r-project.org/package=TITAN2>.

This package provides a set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example to export to a PLINK set.

Character vector to numerical translation in Euros from Spanish spelled monetary quantities. Reverse translation from integer to Spanish. Upper limit is up to the millions range. Geocoding via Cadastral web site.

Data processing and visualizations for rodent vocalizations exported from DeepSqueak'. These functions are compatible with the SqueakR Shiny Dashboard, which can be used to visualize experimental results and analyses.

Unofficial client for Sentry <https://sentry.io>, a self-hosted or cloud-based error-monitoring service. It will inform about errors in real-time, and includes integration with the Plumber package.

This package provides a spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates.

Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.

Forecasting univariate time series with Variational Mode Decomposition (VMD) based time delay neural network models.For method details see Konstantin, D.and Dominique, Z. (2014). <doi:10.1109/TSP.2013.2288675>.

Testing the equality of two means using Ranked Set Sampling and Median Ranked Set Sampling are provided under normal distribution. Data generation functions are also given RSS and MRSS. Also, data generation functions are given under imperfect ranking data for Ranked Set Sampling and Median Ranked Set Sampling. Ozdemir Y.A., Ebegil M., & Gokpinar F. (2019), <doi:10.1007/s40995-018-0558-0> Ozdemir Y.A., Ebegil M., & Gokpinar F. (2017), <doi:10.1080/03610918.2016.1263736>.

To facilitate using cereal with R via cpp11 or Rcpp'. cereal is a header-only C++11 serialization library. cereal takes arbitrary data types and reversibly turns them into different representations, such as compact binary encodings, XML', or JSON'. cereal was designed to be fast, light-weight, and easy to extend - it has no external dependencies and can be easily bundled with other code or used standalone. Please see <https://uscilab.github.io/cereal/> for more information.

This package provides a set of functions to facilitate building formatted strings under various replacement rules: C-style formatting, variable-based formatting, and number-based formatting. C-style formatting is basically identical to built-in function sprintf'. Variable-based formatting allows users to put variable names in a formatted string which will be replaced by variable values. Number-based formatting allows users to use index numbers to represent the corresponding argument value to appear in the string.

An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065>. It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <doi:10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <doi:10.1016/j.csda.2007.11.006>).

This package provides a tool that allows to download and save historical time series data for future use offline. The intelligent updating functionality will only download the new available information; thus, saving you time and Internet bandwidth. It will only re-download the full data-set if any inconsistencies are detected. This package supports following data provides: Yahoo (finance.yahoo.com), FRED (fred.stlouisfed.org), Quandl (data.nasdaq.com), AlphaVantage (www.alphavantage.co), Tiingo (www.tiingo.com).

S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time series of temperature and precipitation. These tools make use of Vector AutoRegressive models (VARs). The weather generator model is then saved as an object and is calibrated by daily instrumental "Gaussianized" time series through the vars package tools. Once obtained this model, it can it can be used for weather generations and be adapted to work with several climatic monthly time series.

This package performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

This package provides fast and accurate convolution-type smoothed quantile regression, implemented using Barzilai-Borwein gradient descent with a Huber regression warm start. Confidence intervals for regression coefficients are constructed using multiplier bootstrap.

This package provides functions to fit kernel density functions to data on temporal activity patterns of animals; estimate coefficients of overlapping of densities for two species; and calculate bootstrap estimates of confidence intervals.

The feature package contains functions to display and compute kernel density estimates, significant gradient and significant curvature regions. Significant gradient and/or curvature regions often correspond to significant features (e.g. local modes).

This package provides a common interface to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. R, Spark, Stan, etc).

This package provides the asynchronous RPC client-server framework and message specification for Rigetti Quantum Cloud Services (QCS). It implements an efficient transport protocol by using ZeroMQ (ZMQ) sockets and MessagePack (msgpack) serialization.

This package provides a novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.