R-universe search: needs:dygraphs

stan-dev

rstanarm:Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Maintained by Ben Goodrich. Last updated 11 days ago.

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics multilevel-models rstan rstanarm stan statistical-modeling cpp

393 stars 15.70 score 5.0k scripts 13 dependents

facebook

prophet:Automatic Forecasting Procedure

Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Maintained by Sean Taylor. Last updated 5 months ago.

forecasting python cpp

19k stars 15.59 score 976 scripts 13 dependents

stan-dev

shinystan:Interactive Visual and Numerical Diagnostics and Posterior Analysis for Bayesian Models

A graphical user interface for interactive Markov chain Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a posterior sample. The interface is powered by the 'Shiny' web application framework from 'RStudio' and works with the output of MCMC programs written in any programming language (and has extended functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' packages).

Maintained by Jonah Gabry. Last updated 3 years ago.

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics mcmc shiny-apps stan statistical-graphics

200 stars 13.13 score 1.6k scripts 15 dependents

business-science

modeltime:The Tidymodels Extension for Time Series Modeling

The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).

Maintained by Matt Dancho. Last updated 5 months ago.

arima data-science deep-learning ets forecasting machine-learning machine-learning-algorithms modeltime prophet tbats tidymodeling tidymodels time time-series time-series-analysis timeseries timeseries-forecasting

551 stars 10.61 score 1.1k scripts 7 dependents

facebookexperimental

Robyn:Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science

Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.

Maintained by Gufeng Zhou. Last updated 11 days ago.

adstocking budget-allocation cost-response-curve econometrics evolutionary-algorithm gradient-based-optimisation hyperparameter-optimization marketing-mix-modeling marketing-mix-modelling marketing-science mmm ridge-regression

1.3k stars 10.27 score 95 scripts

microsoft

finnts:Microsoft Finance Time Series Forecasting Framework

Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!

Maintained by Mike Tokic. Last updated 1 months ago.

business data-science feature-selection finance finnts forecasting machine-learning microsoft time-series

194 stars 9.30 score 39 scripts

tidymodels

tidyposterior:Bayesian Analysis to Compare Models using Resampling Statistics

Bayesian analysis used here to answer the question: "when looking at resampling results, are the differences between models 'real'?" To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) <https://jmlr.org/papers/v18/16-305.html>.

Maintained by Max Kuhn. Last updated 5 months ago.

102 stars 8.44 score 273 scripts

florale

multilevelcoda:Estimate Bayesian Multilevel Models for Compositional Data

Implement Bayesian Multilevel Modelling for compositional data in a multilevel framework. Compute multilevel compositional data and Isometric log ratio (ILR) at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models. References: Le, Stanford, Dumuid, and Wiley (2024) <doi:10.48550/arXiv.2405.03985>, Le, Dumuid, Stanford, and Wiley (2024) <doi:10.48550/arXiv.2411.12407>.

Maintained by Flora Le. Last updated 3 days ago.

bayesian-inference compositional-data-analysis multilevel-models multilevelcoda

15 stars 8.34 score 118 scripts

business-science

modeltime.ensemble:Ensemble Algorithms for Time Series Forecasting with Modeltime

A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability.

Maintained by Matt Dancho. Last updated 8 months ago.

ensemble ensemble-learning forecast forecasting modeltime stacking stacking-ensemble tidymodels time time-series timeseries

77 stars 8.30 score 143 scripts

jaredlander

coefplot:Plots Coefficients from Fitted Models

Plots the coefficients from model objects. This very quickly shows the user the point estimates and confidence intervals for fitted models.

Maintained by Jared P. Lander. Last updated 3 years ago.

27 stars 8.28 score 744 scripts 1 dependents

bayes-rules

bayesrules:Datasets and Supplemental Functions from Bayes Rules! Book

Provides datasets and functions used for analysis and visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>). The package contains a set of functions that summarize and plot Bayesian models from some conjugate families and another set of functions for evaluation of some Bayesian models.

Maintained by Mine Dogucu. Last updated 3 years ago.

bayesian-statistics data

72 stars 8.06 score 466 scripts

nmecsys

BETS:Brazilian Economic Time Series

It provides access to and information about the most important Brazilian economic time series - from the Getulio Vargas Foundation <http://portal.fgv.br/en>, the Central Bank of Brazil <http://www.bcb.gov.br> and the Brazilian Institute of Geography and Statistics <http://www.ibge.gov.br>. It also presents tools for managing, analysing (e.g. generating dynamic reports with a complete analysis of a series) and exporting these time series.

Maintained by Talitha Speranza. Last updated 4 years ago.

38 stars 7.82 score 108 scripts

spsanderson

healthyR.ts:The Time Series Modeling Companion to 'healthyR'

Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

Maintained by Steven Sanderson. Last updated 6 months ago.

ai arima-forecasting arima-model ets forecasting ggplot2 machine-learning modeling prophet time-series time-series-analysis workflows

19 stars 7.58 score 56 scripts 1 dependents

spsanderson

healthyR.ai:The Machine Learning and AI Modeling Companion to 'healthyR'

Hospital machine learning and ai data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include predicting length of stay, and readmits. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

Maintained by Steven Sanderson. Last updated 2 months ago.

ai artificial-intelligence healthcareanalytics healthyr healthyverse machine-learning

16 stars 7.37 score 36 scripts 1 dependents

rchlumsk

RavenR:Raven Hydrological Modelling Framework R Support and Analysis

Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. The 'RavenR' package is also archived at Chlumsky et al. (2020) <doi:10.5281/zenodo.4248183>. The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) <doi:10.1016/j.envsoft.2020.104728>.

Maintained by Robert Chlumsky. Last updated 5 months ago.

diagnostics hydrology modeling modelling visualization water water-resources watershed cpp

36 stars 7.06 score 20 scripts

asael697

bayesforecast:Bayesian Time Series Modeling with Stan

Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Maintained by Asael Alonzo Matamoros. Last updated 1 years ago.

bayesian-inference forecasting-models mcmc stan time-series-analysis cpp

45 stars 6.92 score 62 scripts

rte-antares-rpackage

antaresViz:Antares Visualizations

Visualize results generated by Antares, a powerful open source software developed by RTE to simulate and study electric power systems (more information about 'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). This package provides functions that create interactive charts to help 'Antares' users visually explore the results of their simulations.

Maintained by Tatiana Vargas. Last updated 3 months ago.

adequacy bilan dygraphs electric energy leaflet linear-programming manipulatewidge monte-carlo-simulation optimization plotly previsionnel renewable-energy rte shiny shiny-apps simulation stochastic-simulation-algorithm tyndp

21 stars 6.83 score 32 scripts

business-science

modeltime.resample:Resampling Tools for Time Series Forecasting

A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis.

Maintained by Matt Dancho. Last updated 1 years ago.

accuracy-metrics backtesting bootstrap bootstrapping cross-validation forecasting modeltime modeltime-resample resampling statistics tidymodels time-series

19 stars 6.64 score 38 scripts 1 dependents

mazamascience

AirMonitor:Air Quality Data Analysis

Utilities for working with hourly air quality monitoring data with a focus on small particulates (PM2.5). A compact data model is structured as a list with two dataframes. A 'meta' dataframe contains spatial and measuring device metadata associated with deployments at known locations. A 'data' dataframe contains a 'datetime' column followed by columns of measurements associated with each "device-deployment". Algorithms to calculate NowCast and the associated Air Quality Index (AQI) are defined at the US Environmental Projection Agency AirNow program: <https://document.airnow.gov/technical-assistance-document-for-the-reporting-of-daily-air-quailty.pdf>.

Maintained by Jonathan Callahan. Last updated 6 months ago.

7 stars 6.57 score 178 scripts

mitchelloharawild

fable.prophet:Prophet Modelling Interface for 'fable'

Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools.

Maintained by Mitchell OHara-Wild. Last updated 1 years ago.

56 stars 6.48 score 107 scripts

thinkr-open

shinipsum:Lorem-Ipsum-like Helpers for fast Shiny Prototyping

Prototype your shiny apps quickly with these Lorem-Ipsum-like Helpers.

Maintained by Colin Fay. Last updated 1 years ago.

dygraph ggplot golemverse hacktoberfest lorem-ipsum

125 stars 6.45 score 50 scripts 1 dependents

ashbaldry

designer:'Shiny' UI Prototype Builder

A 'shiny' application that enables the user to create a prototype UI, being able to drag and drop UI components before being able to save or download the equivalent R code.

Maintained by Ashley Baldry. Last updated 2 years ago.

shiny

149 stars 6.17 score 3 scripts

bbtomas

rPraat:Interface to Praat

Read, write and manipulate 'Praat' TextGrid, PitchTier, Pitch, IntensityTier, Formant, Sound, and Collection files <https://www.fon.hum.uva.nl/praat/>.

Maintained by Tomas Boril. Last updated 3 years ago.

29 stars 6.13 score 156 scripts 2 dependents

giopogg

webSDM:Including Known Interactions in Species Distribution Models

A collection of tools to fit and work with trophic Species Distribution Models. Trophic Species Distribution Models combine knowledge of trophic interactions with Bayesian structural equation models that model each species as a function of its prey (or predators) and environmental conditions. It exploits the topological ordering of the known trophic interaction network to predict species distribution in space and/or time, where the prey (or predator) distribution is unavailable. The method implemented by the package is described in Poggiato, Andréoletti, Pollock and Thuiller (2022) <doi:10.22541/au.166853394.45823739/v1>.

Maintained by Giovanni Poggiato. Last updated 9 months ago.

17 stars 5.71 score 9 scripts

christophsax

seasonalview:Graphical User Interface for Seasonal Adjustment

A graphical user interface to the 'seasonal' package and 'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software.

Maintained by Christoph Sax. Last updated 5 months ago.

seasonal-adjustment shiny time-series

22 stars 5.65 score 105 scripts

pheymanss

chronicle:Grammar for Creating R Markdown Reports

A system for creating R Markdown reports with a sequential syntax.

Maintained by Philippe Heymans Smith. Last updated 1 years ago.

29 stars 5.54 score 24 scripts

rpuggaardrode

praatpicture:'Praat Picture' Style Plots of Acoustic Data

Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in 'Praat' using either derived signals generated directly in R with 'wrassp' or imported derived signals from 'Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of flexibility. Also provides options for embedding audio in figures and animating figures.

Maintained by Rasmus Puggaard-Rode. Last updated 1 months ago.

31 stars 5.19 score 3 scripts

spsanderson

healthyverse:Easily Install and Load the 'healthyverse'

The 'healthyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. This package is designed to make it easy to install and load multiple 'healthyverse' packages in a single step.

Maintained by Steven Sanderson. Last updated 6 months ago.

analytics healthcare healthcare-application installation installer meta packages

11 stars 5.12 score 24 scripts

vinhdizzo

IRexamples:Collection of Practical Institutional Research Examples and Tutorials

Provides examples of code for analyzing data or accomplishing tasks that may be useful to institutional or educational researchers.

Maintained by Vinh Nguyen. Last updated 2 years ago.

4 stars 5.00 score 4 scripts

mundl

lfstat:Calculation of Low Flow Statistics for Daily Stream Flow Data

The "Manual on Low-flow Estimation and Prediction", published by the World Meteorological Organisation (WMO), gives a comprehensive summary on how to analyse stream flow data focusing on low-flows. This packages provides functions to compute the described statistics and produces plots similar to the ones in the manual.

Maintained by Tobias Gauster. Last updated 2 years ago.

11 stars 4.94 score 159 scripts

cran

airGRteaching:Teaching Hydrological Modelling with the GR Rainfall-Runoff Models ('Shiny' Interface Included)

Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables.

Maintained by Olivier Delaigue. Last updated 3 days ago.

6 stars 4.86 score

genentech

BayesERtools:Bayesian Exposure-Response Analysis Tools

Suite of tools that facilitate exposure-response analysis using Bayesian methods. The package provides a streamlined workflow for fitting types of models that are commonly used in exposure-response analysis - linear and Emax for continuous endpoints, logistic linear and logistic Emax for binary endpoints, as well as performing simulation and visualization. Learn more about the workflow at <https://genentech.github.io/BayesERbook/>.

Maintained by Kenta Yoshida. Last updated 1 months ago.

2 stars 4.78 score 20 scripts

vivienroussez

autoTS:Automatic Model Selection and Prediction for Univariate Time Series

Offers a set of functions to easily make predictions for univariate time series. 'autoTS' is a wrapper of existing functions of the 'forecast' and 'prophet' packages, harmonising their outputs in tidy dataframes and using default values for each. The core function getBestModel() allows the user to effortlessly benchmark seven algorithms along with a bagged estimator to identify which one performs the best for a given time series.

Maintained by Vivien Roussez. Last updated 5 years ago.

10 stars 4.78 score 12 scripts

zjg540066169

AuxSurvey:Survey Analysis with Auxiliary Discretized Variables

Probability surveys often use auxiliary continuous data from administrative records, but the utility of this data is diminished when it is discretized for confidentiality. We provide a set of survey estimators to make full use of information from the discretized variables. See Williams, S.Z., Zou, J., Liu, Y., Si, Y., Galea, S. and Chen, Q. (2024), Improving Survey Inference Using Administrative Records Without Releasing Individual-Level Continuous Data. Statistics in Medicine, 43: 5803-5813. <doi:10.1002/sim.10270> for details.

Maintained by Jungang Zou. Last updated 3 months ago.

auxilary-variables categorical-variables survey-analysis

1 stars 4.70 score 5 scripts

giocomai

castarter:Content Analysis Starter Toolkit

Consistent approaches for basic web scraping, text mining and word frequency analysis of textual datasets.

Maintained by Giorgio Comai. Last updated 16 hours ago.

tada text-mining

3 stars 4.59 score 2 scripts

jeanbertinr

shinyML:Compare Supervised Machine Learning Models Using Shiny App

Implementation of a shiny app to easily compare supervised machine learning model performances. You provide the data and configure each model parameter directly on the shiny app. Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks. Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148).

Maintained by Jean Bertin. Last updated 4 years ago.

59 stars 4.58 score 13 scripts

yanrong-stacy-song

backtestGraphics:Interactive Graphics for Portfolio Data

Creates an interactive graphics interface to visualize backtest results of different financial instruments, such as equities, futures, and credit default swaps. The package does not run backtests on the given data set but displays a graphical explanation of the backtest results. Users can look at backtest graphics for different instruments, investment strategies, and portfolios. Summary statistics of different portfolio holdings are shown in the left panel, and interactive plots of profit and loss (P&L), net market value (NMV) and gross market value (GMV) are displayed in the right panel.

Maintained by Yanrong Song. Last updated 1 months ago.

4.40 score 4 scripts

danielkovtun

rpredictit:Interface to the 'PredictIt' API

Wrapper to retrieve market data, explore available markets, and plot historical price data from the 'PredictIt' public API (<https://www.predictit.org/api/marketdata/all/>). The package comes with a demo 'shiny' application for illustrating example use cases. License to use data made available via the API is for non-commercial use and 'PredictIt' is the sole source of such data.

Maintained by Daniel Kovtun. Last updated 3 years ago.

4 stars 4.30 score 8 scripts

estadisticaun

UnalR:Una implementación de funciones de uso interno

Una herramienta rápida y consistente para la disposición de microdatos y la visualización de las cifras y estadísticas oficiales de la Universidad Nacional de Colombia <https://unal.edu.co>. Contiene una biblioteca de funciones gráficas, tanto estáticas como interactivas, que ofrece numerosos tipos de gráficos con una sintaxis altamente configurable y simple. Entre estos encontramos la visualización de tablas HTML, series, gráficos de barras y circulares, mapas, etc. Todo lo anterior apoyado en bibliotecas de JavaScript. English: A fast and consistent tool for the arrangement of microdata and the visualization of official figures and statistics from the National University of Colombia <https://unal.edu.co>. It includes a library of graphical functions, both static and interactive, offering numerous types of charts with a highly configurable and simple syntax. Among these, we find the visualization of HTML tables, series, bar and pie charts, maps, etc. It provides the capability to transition from the interactive to the dynamic world and from one library to another without changing function or syntax.

Maintained by Jeison Alarcón. Last updated 7 months ago.

2 stars 4.28 score 21 scripts

joshcullen

bayesmove:Non-Parametric Bayesian Analyses of Animal Movement

Methods for assessing animal movement from telemetry and biologging data using non-parametric Bayesian methods. This includes features for pre- processing and analysis of data, as well as the visualization of results from the models. This framework does not rely on standard parametric density functions, which provides flexibility during model fitting. Further details regarding part of this framework can be found in Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.

Maintained by Joshua Cullen. Last updated 1 years ago.

cpp

9 stars 4.18 score 34 scripts

abeith

retimer:Retime and Analyse Speech Signals

Retime speech signals with a native Waveform Similarity Overlap-Add (WSOLA) implementation translated from the 'TSM toolbox' by Driedger & Müller (2014) <https://www.audiolabs-erlangen.de/content/resources/MIR/TSMtoolbox/2014_DriedgerMueller_TSM-Toolbox_DAFX.pdf>. Design retimings and pitch (f0) transformations with tidy data and apply them via 'Praat' interface. Produce spectrograms, spectra, and amplitude envelopes. Includes implementation of vocalic speech envelope analysis (fft_spectrum) technique and example data (mm1) from Tilsen, S., & Johnson, K. (2008) <doi:10.1121/1.2947626>.

Maintained by Alistair Beith. Last updated 3 months ago.

4.18 score 5 scripts

kwb-r

aquanes.report:Automated Reporting Tool for Water Suppliers

Collects, aggregates and visualises operational and analytical data from water suppliers (including a standardised reporting document).

Maintained by Michael Rustler. Last updated 6 years ago.

automated-reporting data-aggregation data-export data-import data-visualisation pilot-plant project-aquanes shiny-app

4.01 score 23 scripts

kwb-r

kwb.pilot:Importing, Aggregating and Visualising Data From KWB Pilot Plants

Collects, aggregates and visualises operational and analytical data from water suppliers (including a standardised reporting document).

Maintained by Michael Rustler. Last updated 2 years ago.

data-aggregation data-import data-visualisation project-aquanes project-mbr40 project-suleman project-ultimate

1 stars 4.01 score 17 scripts

sg4r

rblt:Bio-Logging Toolbox

An R-shiny application to visualize bio-loggers time series at a microsecond precision as Acceleration, Temperature, Pressure, Light intensity. It is possible to link behavioral labels extracted from 'BORIS' software <http://www.boris.unito.it> or manually written in a csv file.

Maintained by Sebastien Geiger. Last updated 2 months ago.

2 stars 4.00 score 4 scripts

mhealthgroup

MIMSunit:Algorithm to Compute Monitor Independent Movement Summary Unit (MIMS-Unit)

The MIMS-unit algorithm is developed to compute Monitor Independent Movement Summary Unit, a measurement to summarize raw accelerometer data while ensuring harmonized results across different devices. It also includes scripts to reproduce results in the related publication (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) <doi:10.1123/jmpb.2018-0068>).

Maintained by Qu Tang. Last updated 3 years ago.

12 stars 3.98 score 16 scripts

mcanigueral

evsim:Electric Vehicle Charging Sessions Simulation

Simulation of Electric Vehicles charging sessions using Gaussian models, together with time-series power demand calculations. The simulation methodology is published in Cañigueral et al. (2023, ISBN:0957-4174) <doi:10.1016/j.eswa.2023.120318>.

Maintained by Marc Cañigueral. Last updated 17 days ago.

1 stars 3.98 score 19 scripts

tweedell

motoRneuron:Analyzing Paired Neuron Discharge Times for Time-Domain Synchronization

The temporal relationship between motor neurons can offer explanations for neural strategies. We combined functions to reduce neuron action potential discharge data and analyze it for short-term, time-domain synchronization. Even more so, motoRneuron combines most available methods for the determining cross correlation histogram peaks and most available indices for calculating synchronization into simple functions. See Nordstrom, Fuglevand, and Enoka (1992) <doi:10.1113/jphysiol.1992.sp019244> for a more thorough introduction.

Maintained by Andrew Tweedell. Last updated 6 years ago.

1 stars 3.74 score 11 scripts

ncsoft

promotionImpact:Analysis & Measurement of Promotion Effectiveness

Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified variables (e.g. start of a month).

Maintained by Nahyun Kim. Last updated 5 years ago.

47 stars 3.67 score 2 scripts

rkbauer

RchivalTag:Analyzing and Interactive Visualization of Archival Tagging Data

A set of functions to generate, access and analyze standard data products from archival tagging data.

Maintained by Robert K. Bauer. Last updated 2 months ago.

data-visuali depth depth-temperature-profiles dygraphs ggpot leaflet minipat pelagic plotly satellite sensor spatial star-oddi temperature time-series tracks wildlife-computers

1 stars 3.59 score 26 scripts

danielollech

dsa:Seasonal Adjustment of Daily Time Series

Seasonal- and calendar adjustment of time series with daily frequency using the DSA approach developed by Ollech, Daniel (2018): Seasonal adjustment of daily time series. Bundesbank Discussion Paper 41/2018.

Maintained by Daniel Ollech. Last updated 4 years ago.

openjdk

1 stars 3.19 score 52 scripts 1 dependents

frankiethull

kantime:Nixtla's KAN Time Series Model In R

This package is a binding between Nixtla's neuralforecast Library, specifically KANs, and R's {modeltime} package. Nixtla's KAN is bound using {reticulate}, which is then ported into {parsnip} and bridged to {modeltime}.

Maintained by Frankie T. Hull. Last updated 3 months ago.

11 stars 3.04 score

kumes

seasonalityPlot:Seasonality Variation Plots of Stock Prices and Cryptocurrencies

The price action at any given time is determined by investor sentiment and market conditions. Although there is no established principle, over a long period of time, things often move with a certain periodicity. This is sometimes referred to as anomaly. The seasonPlot() function in this package calculates and visualizes the average value of price movements over a year for any given period. In addition, the monthly increase or decrease in price movement is represented with a colored background. This seasonPlot() function can use the same symbols as the 'quantmod' package (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc).

Maintained by Satoshi Kume. Last updated 6 months ago.

2 stars 3.00 score 6 scripts

rikoke

fbst:The Full Bayesian Evidence Test, Full Bayesian Significance Test and the e-Value

Provides access to a range of functions for computing and visualizing the Full Bayesian Significance Test (FBST) and the e-value for testing a sharp hypothesis against its alternative, and the Full Bayesian Evidence Test (FBET) and the (generalized) Bayesian evidence value for testing a composite (or interval) hypothesis against its alternative. The methods are widely applicable as long as a posterior MCMC sample is available.

Maintained by Riko Kelter. Last updated 1 years ago.

1 stars 2.95 score 6 scripts 1 dependents

kwb-r

kwb.flusshygiene:Functions used within FLUSSHYGIENE project (BMBF)

Easy and transferable functions for creating and managing models for hygiene data in rivers. This package is developed within the FLUSSHYGIENE project. See https://bmbf.nawam-rewam.de/en/projekt/flusshygiene/ for details.

Maintained by Hauke Sonnenberg. Last updated 5 years ago.

modelling project-flusshygiene

1 stars 2.70 score 1 scripts

jasonjfoster

rolleda:Rolling Exploratory Data Analysis

Web application for rolling exploratory data analysis of time-series data.

Maintained by Jason Foster. Last updated 9 months ago.

analysis application shiny

3 stars 2.48 score 1 scripts

riko-k

brada:Bayesian Response-Adaptive Design Analysis

Provides access to a range of functions for analyzing, applying and visualizing Bayesian response-adaptive trial designs for a binary endpoint. Includes the predictive probability approach and the predictive evidence value designs for binary endpoints.

Maintained by Riko Kelter. Last updated 2 years ago.

2.30 score 4 scripts

cran

quarks:Simple Methods for Calculating and Backtesting Value at Risk and Expected Shortfall

Enables the user to calculate Value at Risk (VaR) and Expected Shortfall (ES) by means of various types of historical simulation. Currently plain-, age-, volatility-weighted- and filtered historical simulation are implemented in this package. Volatility weighting can be carried out via an exponentially weighted moving average model (EWMA) or other GARCH-type models. The performance can be assessed via Traffic Light Test, Coverage Tests and Loss Functions. The methods of the package are described in Gurrola-Perez, P. and Murphy, D. (2015) <https://EconPapers.repec.org/RePEc:boe:boeewp:0525> as well as McNeil, J., Frey, R., and Embrechts, P. (2015) <https://ideas.repec.org/b/pup/pbooks/10496.html>.

Maintained by Sebastian Letmathe. Last updated 10 months ago.

2 stars 2.00 score

atanubhattacharjee

jmBIG:Joint Longitudinal and Survival Model for Big Data

Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences.

Maintained by Atanu Bhattacharjee. Last updated 2 months ago.

1.78 score 1 dependents

skranz

dyplot:Simple wrapper to use dygraphs directly with data frame (no xts object needed)

Simple wrapper to use dygraphs directly with data frame (no xts object needed)

Maintained by Sebastian Kranz. Last updated 3 years ago.

1.70 score

kwb-r

kwb.dygraph:Additional Functions to be Used with dygraph-Objects

Additional functions to be used with dygraph-objects e.g. shading of areas or drawing of lines representing time periods within events (given by begin and end times).

Maintained by Hauke Sonnenberg. Last updated 3 years ago.

data-visualisation dygraph project-kuras

1.70 score

rvhulst

evidence:Analysis of Scientific Evidence Using Bayesian and Likelihood Methods

Bayesian (and some likelihoodist) functions as alternatives to hypothesis-testing functions in R base using a user interface patterned after those of R's hypothesis testing functions. See McElreath (2016, ISBN: 978-1-4822-5344-3), Gelman and Hill (2007, ISBN: 0-521-68689-X) (new edition in preparation) and Albert (2009, ISBN: 978-0-387-71384-7) for good introductions to Bayesian analysis and Pawitan (2002, ISBN: 0-19-850765-8) for the Likelihood approach. The functions in the package also make extensive use of graphical displays for data exploration and model comparison.

Maintained by Robert van Hulst. Last updated 7 years ago.

1.63 score 43 scripts

cran

ZRA:Dynamic Plots for Time Series Forecasting

Combines a forecast of a time series, using the function forecast(), with the dynamic plots from dygraphs.

Maintained by David Beiner. Last updated 10 years ago.

1.60 score

bpoconnor

SIMPLE.REGRESSION:OLS, Moderated, Logistic, and Count Regressions Made Simple

Provides SPSS- and SAS-like output for least squares multiple regression, logistic regression, and count variable regressions. Detailed output is also provided for OLS moderated regression, interaction plots, and Johnson-Neyman regions of significance. The output includes standardized coefficients, partial and semi-partial correlations, collinearity diagnostics, plots of residuals, and detailed information about simple slopes for interactions. The output for some functions includes Bayes Factors and, if requested, regression coefficients from Bayesian Markov Chain Monte Carlo analyses. There are numerous options for model plots. The REGIONS_OF_SIGNIFICANCE function also provides Johnson-Neyman regions of significance and plots of interactions for both lm and lme models.

Maintained by Brian P. OConnor. Last updated 5 days ago.

1.48 score

cran

iClick:A Button-Based GUI for Financial and Economic Data Analysis

A GUI designed to support the analysis of financial-economic time series data.

Maintained by Ho Tsung-wu. Last updated 6 years ago.

1.00 score

danielollech

tssim:Simulation of Daily and Monthly Time Series

Flexible simulation of time series using time series components, including seasonal, calendar and outlier effects. Main algorithm described in Ollech, D. (2021) <doi:10.1515/jtse-2020-0028>.

Maintained by Daniel Ollech. Last updated 4 months ago.

openjdk

1.00 score

atanubhattacharjee

JMbdirect:Joint Model for Longitudinal and Multiple Time to Events Data

Provides model fitting, prediction, and plotting for joint models of longitudinal and multiple time-to-event data, including methods from Rizopoulos (2012) <doi:10.1201/b12208>. Useful for handling complex survival and longitudinal data in clinical research.

Maintained by Atanu Bhattacharjee. Last updated 18 days ago.

1.00 score