R-universe search: trend

ebird

ebirdst:Access and Analyze eBird Status and Trends Data Products

Tools for accessing and analyzing eBird Status and Trends Data Products (<https://science.ebird.org/en/status-and-trends>). eBird (<https://ebird.org/home>) is a global database of bird observations collected by member of the public. eBird Status and Trends uses these data to model global bird distributions, abundances, and population trends at a high spatial and temporal resolution.

Maintained by Matthew Strimas-Mackey. Last updated 20 days ago.

47.4 match 26 stars 8.85 score 228 scripts

cran

trend:Non-Parametric Trend Tests and Change-Point Detection

The analysis of environmental data often requires the detection of trends and change-points. This package includes tests for trend detection (Cox-Stuart Trend Test, Mann-Kendall Trend Test, (correlated) Hirsch-Slack Test, partial Mann-Kendall Trend Test, multivariate (multisite) Mann-Kendall Trend Test, (Seasonal) Sen's slope, partial Pearson and Spearman correlation trend test), change-point detection (Lanzante's test procedures, Pettitt's test, Buishand Range Test, Buishand U Test, Standard Normal Homogeinity Test), detection of non-randomness (Wallis-Moore Phase Frequency Test, Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test) and the two sample Robust Rank-Order Distributional Test.

Maintained by Thorsten Pohlert. Last updated 1 years ago.

fortran

73.8 match 3 stars 5.31 score 9 dependents

reconverse

trending:Model Temporal Trends

Provides a coherent interface to multiple modelling tools for fitting trends along with a standardised approach for generating confidence and prediction intervals.

Maintained by Thibaut Jombart. Last updated 2 years ago.

53.9 match 8 stars 5.58 score 16 scripts 1 dependents

fate-ewi

bayesdfa:Bayesian Dynamic Factor Analysis (DFA) with 'Stan'

Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. 'bayesdfa' extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes.

Maintained by Eric J. Ward. Last updated 8 hours ago.

cpp

31.2 match 28 stars 8.27 score 101 scripts

zhaokg

Rbeast:Bayesian Change-Point Detection and Time Series Decomposition

Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.

Maintained by Kaiguang Zhao. Last updated 6 months ago.

anomoly-detection bayesian-time-series breakpoint-detection changepoint-detection interrupted-time-series seasonality-analysis structural-breakpoint technical-analysis time-series time-series-decomposition trend trend-analysis

27.8 match 302 stars 7.63 score 89 scripts

pmassicotte

gtrendsR:Perform and Display Google Trends Queries

An interface for retrieving and displaying the information returned online by Google Trends is provided. Trends (number of hits) over the time as well as geographic representation of the results can be displayed.

Maintained by Philippe Massicotte. Last updated 7 months ago.

google google-trends

19.1 match 356 stars 10.35 score 716 scripts 1 dependents

microsoft

wpa:Tools for Analysing and Visualising Viva Insights Data

Opinionated functions that enable easier and faster analysis of Viva Insights data. There are three main types of functions in 'wpa': (i) Standard functions create a 'ggplot' visual or a summary table based on a specific Viva Insights metric; (2) Report Generation functions generate HTML reports on a specific analysis area, e.g. Collaboration; (3) Other miscellaneous functions cover more specific applications (e.g. Subject Line text mining) of Viva Insights data. This package adheres to 'tidyverse' principles and works well with the pipe syntax. 'wpa' is built with the beginner-to-intermediate R users in mind, and is optimised for simplicity.

Maintained by Martin Chan. Last updated 4 months ago.

workplace-analytics

25.5 match 30 stars 6.69 score 39 scripts 1 dependents

business-science

timetk:A Tool Kit for Working with Time Series

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.

Maintained by Matt Dancho. Last updated 1 years ago.

coercion coercion-functions data-mining dplyr forecast forecasting forecasting-models machine-learning series-decomposition series-signature tibble tidy tidyquant tidyverse time time-series timeseries

11.5 match 625 stars 14.15 score 4.0k scripts 16 dependents

alexkowa

EnvStats:Package for Environmental Statistics, Including US EPA Guidance

Graphical and statistical analyses of environmental data, with focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. Major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. Numerous built-in data sets from regulatory guidance documents and environmental statistics literature. Includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, <doi:10.1007/978-1-4614-8456-1>).

Maintained by Alexander Kowarik. Last updated 17 days ago.

12.3 match 26 stars 12.80 score 2.4k scripts 46 dependents

bcgov

fasstr:Analyze, Summarize, and Visualize Daily Streamflow Data

The Flow Analysis Summary Statistics Tool for R, 'fasstr', provides various functions to tidy and screen daily stream discharge data, calculate and visualize various summary statistics and metrics, and compute annual trending and volume frequency analyses. It features useful function arguments for filtering of and handling dates, customizing data and metrics, and the ability to pull daily data directly from the Water Survey of Canada hydrometric database (<https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>).

Maintained by Jon Goetz. Last updated 6 months ago.

bcgov for frequency-analysis hydat hydrology streamflow summary-statistics trends water

19.5 match 57 stars 7.48 score 89 scripts

microsoft

vivainsights:Analyze and Visualize Data from 'Microsoft Viva Insights'

Provides a versatile range of functions, including exploratory data analysis, time-series analysis, organizational network analysis, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to 'Microsoft Viva Insights'.

Maintained by Martin Chan. Last updated 24 days ago.

21.9 match 11 stars 6.12 score 68 scripts

vlyubchich

funtimes:Functions for Time Series Analysis

Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.

Maintained by Vyacheslav Lyubchich. Last updated 2 years ago.

19.9 match 7 stars 6.69 score 93 scripts

pasraia

RRphylo:Phylogenetic Ridge Regression Methods for Comparative Studies

Functions for phylogenetic analysis (Castiglione et al., 2018 <doi:10.1111/2041-210X.12954>). The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, the computation of ontogenetic shape vectors and test for morphological convergence.

Maintained by Silvia Castiglione. Last updated 7 months ago.

17.1 match 10 stars 7.48 score 83 scripts

doi-usgs

EGRET:Exploration and Graphics for RivEr Trends

Statistics and graphics for streamflow history, water quality trends, and the statistical modeling algorithm: Weighted Regressions on Time, Discharge, and Season (WRTDS).

Maintained by Laura DeCicco. Last updated 4 months ago.

usgs water-quality water-quality-data

11.4 match 90 stars 10.72 score 362 scripts 1 dependents

sciviews

pastecs:Package for Analysis of Space-Time Ecological Series

Regularisation, decomposition and analysis of space-time series. The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 functionalities to R.

Maintained by Philippe Grosjean. Last updated 1 years ago.

ecology time-series

11.7 match 4 stars 10.34 score 2.1k scripts 13 dependents

bioc

edgeR:Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

Maintained by Yunshun Chen. Last updated 6 days ago.

alternativesplicing batcheffect bayesian biomedicalinformatics cellbiology chipseq clustering coverage differentialexpression differentialmethylation differentialsplicing dnamethylation epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways proteomics qualitycontrol regression rnaseq sage sequencing singlecell systemsbiology timecourse transcription transcriptomics openblas

8.6 match 13.40 score 17k scripts 255 dependents

jeffreyevans

spatialEco:Spatial Analysis and Modelling Utilities

Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics.

Maintained by Jeffrey S. Evans. Last updated 13 days ago.

biodiversity conservation ecology r-spatial raster spatial vector

12.0 match 110 stars 9.55 score 736 scripts 2 dependents

nicholasjclark

mvgam:Multivariate (Dynamic) Generalized Additive Models

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

Maintained by Nicholas J Clark. Last updated 7 hours ago.

bayesian-statistics dynamic-factor-models ecological-modelling forecasting gaussian-process generalised-additive-models generalized-additive-models joint-species-distribution-modelling multilevel-models multivariate-timeseries stan time-series-analysis timeseries vector-autoregression vectorautoregression cpp

11.0 match 139 stars 9.85 score 117 scripts

cbergmeir

Rlgt:Bayesian Exponential Smoothing Models with Trend Modifications

An implementation of a number of Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the 'rstan' package.

Maintained by Christoph Bergmeir. Last updated 8 months ago.

cpp

14.6 match 20 stars 7.05 score 31 scripts

jsta

wql:Exploring Water Quality Monitoring Data

Functions to assist in the processing and exploration of data from environmental monitoring programs. The package name stands for "water quality" and reflects the original focus on time series data for physical and chemical properties of water, as well as the biota. Intended for programs that sample approximately monthly, quarterly or annually at discrete stations, a feature of many legacy data sets. Most of the functions should be useful for analysis of similar-frequency time series regardless of the subject matter.

Maintained by Jemma Stachelek. Last updated 2 months ago.

water-quality

12.9 match 12 stars 7.34 score 204 scripts 3 dependents

joshuaulrich

TTR:Technical Trading Rules

A collection of over 50 technical indicators for creating technical trading rules. The package also provides fast implementations of common rolling-window functions, and several volatility calculations.

Maintained by Joshua Ulrich. Last updated 1 years ago.

algorithmic-trading finance technical-analysis

5.8 match 338 stars 15.11 score 2.8k scripts 359 dependents

collinerickson

GauPro:Gaussian Process Fitting

Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) <doi:10.1016/j.ejor.2017.10.002>.

Maintained by Collin Erickson. Last updated 16 hours ago.

openblas cpp openmp

10.0 match 16 stars 8.44 score 104 scripts 1 dependents

steve-the-bayesian

bsts:Bayesian Structural Time Series

Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many other sources.

Maintained by Steven L. Scott. Last updated 1 years ago.

cpp

12.9 match 33 stars 6.54 score 338 scripts 3 dependents

zheng206

ComBatFamQC:Comprehensive Batch Effect Diagnostics and Harmonization

Provides a comprehensive framework for batch effect diagnostics, harmonization, and post-harmonization downstream analysis. Features include interactive visualization tools, robust statistical tests, and a range of harmonization techniques. Additionally, 'ComBatFamQC' enables the creation of life-span age trend plots with estimated age-adjusted centiles and facilitates the generation of covariate-corrected residuals for analytical purposes. Methods for harmonization are based on approaches described in Johnson et al., (2007) <doi:10.1093/biostatistics/kxj037>, Beer et al., (2020) <doi:10.1016/j.neuroimage.2020.117129>, Pomponio et al., (2020) <doi:10.1016/j.neuroimage.2019.116450>, and Chen et al., (2021) <doi:10.1002/hbm.25688>.

Maintained by Zheng Ren. Last updated 2 hours ago.

diagnostic-tool harmonization rshinyapp

15.2 match 2 stars 5.41 score 16 scripts

rominsal

pspatreg:Spatial and Spatio-Temporal Semiparametric Regression Models with Spatial Lags

Estimation and inference of spatial and spatio-temporal semiparametric models including spatial or spatio-temporal non-parametric trends, parametric and non-parametric covariates and, possibly, a spatial lag for the dependent variable and temporal correlation in the noise. The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>; Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.

Maintained by Roman Minguez. Last updated 3 years ago.

12.6 match 12 stars 6.44 score 77 scripts

jknape

poptrend:Estimate Smooth and Linear Trends from Population Count Survey Data

Functions to estimate and plot smooth or linear population trends, or population indices, from animal or plant count survey data.

Maintained by Jonas Knape. Last updated 1 years ago.

19.9 match 10 stars 4.00 score 4 scripts

ha-pu

globaltrends:Download and Measure Global Trends Through Google Search Volumes

Google offers public access to global search volumes from its search engine through the Google Trends portal. The package downloads these search volumes provided by Google Trends and uses them to measure and analyze the distribution of search scores across countries or within countries. The package allows researchers and analysts to use these search scores to investigate global trends based on patterns within these scores. This offers insights such as degree of internationalization of firms and organizations or dissemination of political, social, or technological trends across the globe or within single countries. An outline of the package's methodological foundations and potential applications is available as a working paper: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3969013>.

Maintained by Harald Puhr. Last updated 2 years ago.

google-trends internationalization

15.3 match 18 stars 5.00 score 11 scripts

bioc

scran:Methods for Single-Cell RNA-Seq Data Analysis

Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell clustering bioconductor-package human-cell-atlas single-cell-rna-seq openblas cpp

5.7 match 41 stars 13.14 score 7.6k scripts 36 dependents

bioc

peco:A Supervised Approach for Predicting cell Cycle Progression using scRNA-seq data

Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data.

Maintained by Chiaowen Joyce Hsiao. Last updated 5 months ago.

sequencing rnaseq geneexpression transcriptomics singlecell software statisticalmethod classification visualization cell-cycle single-cell-rna-seq

12.1 match 12 stars 6.09 score 34 scripts

openair-project

openair:Tools for the Analysis of Air Pollution Data

Tools to analyse, interpret and understand air pollution data. Data are typically regular time series and air quality measurement, meteorological data and dispersion model output can be analysed. The package is described in Carslaw and Ropkins (2012, <doi:10.1016/j.envsoft.2011.09.008>) and subsequent papers.

Maintained by David Carslaw. Last updated 26 days ago.

air-quality air-quality-data meteorology openair cpp

5.5 match 311 stars 12.91 score 1.2k scripts 12 dependents

cran

spatial:Functions for Kriging and Point Pattern Analysis

Functions for kriging and point pattern analysis.

Maintained by Brian Ripley. Last updated 3 months ago.

10.8 match 6.53 score 134 dependents

cdalzell

Lahman:Sean 'Lahman' Baseball Database

Provides the tables from the 'Sean Lahman Baseball Database' as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2023, as recorded in the 2024 version of the database. Documentation examples show how many baseball questions can be investigated.

Maintained by Chris Dalzell. Last updated 4 months ago.

5.8 match 79 stars 11.98 score 1.7k scripts 2 dependents

frbcesab

popbayes:Bayesian Model to Estimate Population Trends from Counts Series

Infers the trends of one or several animal populations over time from series of counts. It does so by accounting for count precision (provided or inferred based on expert knowledge, e.g. guesstimates), smoothing the population rate of increase over time, and accounting for the maximum demographic potential of species. Inference is carried out in a Bayesian framework. This work is part of the FRB-CESAB working group AfroBioDrivers <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.

Maintained by Nicolas Casajus. Last updated 1 years ago.

animal bayesian counts population precision temporal-trend jags cpp

16.0 match 1 stars 4.30 score

cran

PMCMRplus:Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended

For one-way layout experiments the one-way ANOVA can be performed as an omnibus test. All-pairs multiple comparisons tests (Tukey-Kramer test, Scheffe test, LSD-test) and many-to-one tests (Dunnett test) for normally distributed residuals and equal within variance are available. Furthermore, all-pairs tests (Games-Howell test, Tamhane's T2 test, Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one (Tamhane-Dunnett Test) for normally distributed residuals and heterogeneous variances are provided. Van der Waerden's normal scores test for omnibus, all-pairs and many-to-one tests is provided for non-normally distributed residuals and homogeneous variances. The Kruskal-Wallis, BWS and Anderson-Darling omnibus test and all-pairs tests (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow- Fligner test) as well as many-to-one (Nemenyi test, Dunn test, U-test) are given for the analysis of variance by ranks. Non-parametric trend tests (Jonckheere test, Cuzick test, Johnson-Mehrotra test, Spearman test) are included. In addition, a Friedman-test for one-way ANOVA with repeated measures on ranks (CRBD) and Skillings-Mack test for unbalanced CRBD is provided with consequent all-pairs tests (Nemenyi test, Siegel test, Miller test, Conover test, Exact test) and many-to-one tests (Nemenyi test, Demsar test, Exact test). A trend can be tested with Pages's test. Durbin's test for a two-way balanced incomplete block design (BIBD) is given in this package as well as Gore's test for CRBD with multiple observations per cell is given. Outlier tests, Mandel's k- and h statistic as well as functions for Type I error and Power analysis as well as generic summary, print and plot methods are provided.

Maintained by Thorsten Pohlert. Last updated 6 months ago.

fortran

9.5 match 6 stars 7.24 score 12 dependents

patakamuri

modifiedmk:Modified Versions of Mann Kendall and Spearman's Rho Trend Tests

Power of non-parametric Mann-Kendall test and Spearman’s Rho test is highly influenced by serially correlated data. To address this issue, trend tests may be applied on the modified versions of the time series data by Block Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias Corrected Prewhitening and Variance Correction Approach by calculating effective sample size. Mann, H. B. (1945).<doi:10.1017/CBO9781107415324.004>. Kendall, M. (1975). Multivariate analysis. Charles Griffin&Company Ltd,. sen, P. K. (1968).<doi:10.2307/2285891>. Önöz, B., & Bayazit, M. (2012) <doi:10.1002/hyp.8438>. Hamed, K. H. (2009).<doi:10.1016/j.jhydrol.2009.01.040>. Yue, S., & Wang, C. Y. (2002) <doi:10.1029/2001WR000861>. Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002) <doi:10.1002/hyp.1095>. Hamed, K. H., & Ramachandra Rao, A. (1998) <doi:10.1016/S0022-1694(97)00125-X>. Yue, S., & Wang, C. Y. (2004) <doi:10.1023/B:WARM.0000043140.61082.60>.

Maintained by Sandeep Kumar Patakamuri. Last updated 4 years ago.

12.8 match 4 stars 5.36 score 38 scripts 1 dependents

kainhofer

MortalityTables:A Framework for Various Types of Mortality / Life Tables

Classes to implement, analyze and plot cohort life tables for actuarial calculations. Birth-year dependent cohort mortality tables using a yearly trend to extrapolate from a base year are implemented, as well as period life table, cohort life tables using an age shift, and merged life tables. Additionally, several data sets from various countries are included to provide widely-used tables out of the box.

Maintained by Reinhold Kainhofer. Last updated 1 years ago.

11.9 match 1 stars 5.70 score 84 scripts 2 dependents

bioc

siggenes:Multiple Testing using SAM and Efron's Empirical Bayes Approaches

Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM).

Maintained by Holger Schwender. Last updated 5 months ago.

multiplecomparison microarray geneexpression snp exonarray differentialexpression

8.3 match 7.86 score 74 scripts 33 dependents

tbep-tech

wqtrends:Assess Water Quality Trends with Generalized Additive Models

Assess Water Quality Trends for Long-Term Monitoring Data in Estuaries using Generalized Additive Models following Wood (2017) <doi:10.1201/9781315370279> and Error Propagation with Mixed-Effects Meta-Analysis following Sera et al. (2019) <doi:10.1002/sim.8362>. Methods are available for model fitting, assessment of fit, annual and seasonal trend tests, and visualization of results.

Maintained by Marcus Beck. Last updated 6 days ago.

reporting san-francisco-bay time-series-analysis water-quality

11.9 match 10 stars 5.38 score 24 scripts

fawda123

WRTDStidal:Weighted Regression for Water Quality Evaluation in Tidal Waters

An adaptation for estuaries (tidal waters) of weighted regression on time, discharge, and season to evaluate trends in water quality time series. Please see Beck and Hagy (2015) <doi:10.1007/s10666-015-9452-8> for details.

Maintained by Marcus W. Beck. Last updated 1 years ago.

weighted-regression

11.7 match 4 stars 5.38 score 119 scripts

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

3.9 match 19k stars 15.53 score 976 scripts 13 dependents

rstudio

tfprobability:Interface to 'TensorFlow Probability'

Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.

Maintained by Tomasz Kalinowski. Last updated 3 years ago.

6.9 match 54 stars 8.63 score 221 scripts 3 dependents

robjhyndman

forecast:Forecasting Functions for Time Series and Linear Models

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Maintained by Rob Hyndman. Last updated 7 months ago.

forecast forecasting openblas cpp

3.2 match 1.1k stars 18.63 score 16k scripts 239 dependents

angusian

Kendall:Kendall Rank Correlation and Mann-Kendall Trend Test

Computes the Kendall rank correlation and Mann-Kendall trend test. See documentation for use of block bootstrap when there is autocorrelation.

Maintained by A.I. McLeod. Last updated 3 years ago.

fortran

8.8 match 6.74 score 864 scripts 25 dependents

bfast2

bfast:Breaks for Additive Season and Trend

Decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. 'BFAST' can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. 'BFAST' monitoring functionality is described in Verbesselt et al. (2010) <doi:10.1016/j.rse.2009.08.014>. 'BFAST monitor' provides functionality to detect disturbance in near real-time based on 'BFAST'- type models, and is described in Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>. 'BFAST Lite' approach is a flexible approach that handles missing data without interpolation, and will be described in an upcoming paper. Furthermore, different models can now be used to fit the time series data and detect structural changes (breaks).

Maintained by Dainius Masiliūnas. Last updated 5 months ago.

cpp

7.4 match 42 stars 7.98 score 212 scripts 1 dependents

liamrevell

phytools:Phylogenetic Tools for Comparative Biology (and Other Things)

A wide range of methods for phylogenetic analysis - concentrated in phylogenetic comparative biology, but also including numerous techniques for visualizing, analyzing, manipulating, reading or writing, and even inferring phylogenetic trees. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, and simulation of phylogenies and trait data. A broad range of plotting methods for phylogenies and comparative data include (but are not restricted to) methods for mapping trait evolution on trees, for projecting trees into phenotype space or a onto a geographic map, and for visualizing correlated speciation between trees. Lastly, numerous functions are designed for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. For instance, there are functions for computing consensus phylogenies from a set, for simulating phylogenetic trees and data under a range of models, for randomly or non-randomly attaching species or clades to a tree, as well as for a wide range of other manipulations and analyses that phylogenetic biologists might find useful in their research.

Maintained by Liam J. Revell. Last updated 28 days ago.

4.1 match 218 stars 13.85 score 4.8k scripts 76 dependents

corviday

zyp:Zhang + Yue-Pilon Trends Package

An efficient implementation of the slope method described by Sen (1968) <doi:10.1080/01621459.1968.10480934> plus implementation of prewhitening approaches to determining trends in climate data described by Zhang, Vincent, Hogg, and Niitsoo (2000) <doi:10.1080/07055900.2000.9649654> and Yue, Pilon, Phinney, and Cavadias (2002) <doi:10.1002/hyp.1095>.

Maintained by Lee Zeman. Last updated 2 years ago.

15.3 match 3.68 score 134 scripts 4 dependents

silvaneojunior

kDGLM:Bayesian Analysis of Dynamic Generalized Linear Models

Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024)<doi:10.48550/arXiv.2201.05387> and dos Santos Jr. et al. (2024)<doi:10.48550/arXiv.2403.13069>.

Maintained by Silvaneo Vieira dos Santos Junior. Last updated 4 days ago.

9.6 match 2 stars 5.70 score 9 scripts

hafen

stlplus:Enhanced Seasonal Decomposition of Time Series by Loess

Decompose a time series into seasonal, trend, and remainder components using an implementation of Seasonal Decomposition of Time Series by Loess (STL) that provides several enhancements over the STL method in the stats package. These enhancements include handling missing values, providing higher order (quadratic) loess smoothing with automated parameter choices, frequency component smoothing beyond the seasonal and trend components, and some basic plot methods for diagnostics.

Maintained by Ryan Hafen. Last updated 8 years ago.

cpp

7.6 match 66 stars 7.02 score 63 scripts 5 dependents

robwschlegel

heatwaveR:Detect Heatwaves and Cold-Spells

The different methods for defining, detecting, and categorising the extreme events known as heatwaves or cold-spells, as first proposed in Hobday et al. (2016) <doi: 10.1016/j.pocean.2015.12.014> and Hobday et al. (2018) <https://www.jstor.org/stable/26542662>. The functions in this package work on both air and water temperature data. These detection algorithms may be used on non-temperature data as well.

Maintained by Robert W. Schlegel. Last updated 2 months ago.

cpp

5.7 match 46 stars 9.36 score 343 scripts

doi-usgs

EGRETci:Exploration and Graphics for RivEr Trends Confidence Intervals

Collection of functions to evaluate uncertainty of results from water quality analysis using the Weighted Regressions on Time Discharge and Season (WRTDS) method. This package is an add-on to the EGRET package that performs the WRTDS analysis. The WRTDS modeling method was initially introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>, and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>. The paper describing the uncertainty and confidence interval calculations is Hirsch et al. (2015) <doi:10.1016/j.envsoft.2015.07.017>.

Maintained by Laura DeCicco. Last updated 2 years ago.

bootstrap egret usgs water-quality-trends

9.6 match 8 stars 5.41 score 32 scripts

anikoszabo

CorrBin:Nonparametrics with Clustered Binary and Multinomial Data

Implements non-parametric analyses for clustered binary and multinomial data. The elements of the cluster are assumed exchangeable, and identical joint distribution (also known as marginal compatibility, or reproducibility) is assumed for clusters of different sizes. A trend test based on stochastic ordering is implemented. Szabo A, George EO. (2010) <doi:10.1093/biomet/asp077>; George EO, Cheon K, Yuan Y, Szabo A (2016) <doi:10.1093/biomet/asw009>.

Maintained by Aniko Szabo. Last updated 7 months ago.

14.5 match 3.45 score 28 scripts

weirichs

eatRep:Educational Assessment Tools for Replication Methods

Replication methods to compute some basic statistic operations (means, standard deviations, frequency tables, percentiles, mean comparisons using weighted effect coding, generalized linear models, and linear multilevel models) in complex survey designs comprising multiple imputed or nested imputed variables and/or a clustered sampling structure which both deserve special procedures at least in estimating standard errors. See the package documentation for a more detailed description along with references.

Maintained by Sebastian Weirich. Last updated 18 days ago.

9.6 match 1 stars 5.16 score 13 scripts

pavlakrotka

NCC:Simulation and Analysis of Platform Trials with Non-Concurrent Controls

Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.

Maintained by Pavla Krotka. Last updated 7 days ago.

clinical-trials platform-trials simulation statistical-inference jags cpp

7.4 match 5 stars 6.64 score 29 scripts

bioc

UMI4Cats:UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data

UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay.

Maintained by Mireia Ramos-Rodriguez. Last updated 5 months ago.

qualitycontrol preprocessing alignment normalization visualization sequencing coverage chromatin chromatin-interaction genomics umi4c

8.7 match 5 stars 5.57 score 7 scripts

mages

googleVis:R Interface to Google Charts

R interface to Google's chart tools, allowing users to create interactive charts based on data frames. Charts are displayed locally via the R HTTP help server. A modern browser with an Internet connection is required. The data remains local and is not uploaded to Google.

Maintained by Markus Gesmann. Last updated 10 months ago.

3.7 match 361 stars 12.98 score 2.4k scripts 11 dependents

poissonconsulting

bboutools:Boreal Caribou Survival, Recruitment and Population Growth

Estimates annual survival, recruitment and population growth for boreal caribou populations using Bayesian and Maximum Likelihood models with fixed and random effects.

Maintained by Seb Dalgarno. Last updated 2 months ago.

9.3 match 1 stars 5.15 score 13 scripts 2 dependents

r-forge

Sleuth3:Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rd Ed)"

Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)", Cengage Learning.

Maintained by Berwin A Turlach. Last updated 1 years ago.

7.4 match 6.38 score 522 scripts

robjhyndman

tsfeatures:Time Series Feature Extraction

Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.

Maintained by Rob Hyndman. Last updated 8 months ago.

feature-extraction time-series

4.1 match 254 stars 11.47 score 268 scripts 22 dependents

biometris

statgenHTP:High Throughput Phenotyping (HTP) Data Analysis

Phenotypic analysis of data coming from high throughput phenotyping (HTP) platforms, including different types of outlier detection, spatial analysis, and parameter estimation. The package is being developed within the EPPN2020 project (<https://eppn2020.plant-phenotyping.eu/>). Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).

Maintained by Bart-Jan van Rossum. Last updated 3 months ago.

genetics high-troughput-phenotyping

8.5 match 4 stars 5.43 score 17 scripts

bioc

limma:Linear Models for Microarray and Omics Data

Data analysis, linear models and differential expression for omics data.

Maintained by Gordon Smyth. Last updated 6 days ago.

exonarray geneexpression transcription alternativesplicing differentialexpression differentialsplicing genesetenrichment dataimport bayesian clustering regression timecourse microarray micrornaarray mrnamicroarray onechannel proprietaryplatforms twochannel sequencing rnaseq batcheffect multiplecomparison normalization preprocessing qualitycontrol biomedicalinformatics cellbiology cheminformatics epigenetics functionalgenomics genetics immunooncology metabolomics proteomics systemsbiology transcriptomics

3.4 match 13.81 score 16k scripts 585 dependents

dschulz13

smoots:Nonparametric Estimation of the Trend and Its Derivatives in TS

The nonparametric trend and its derivatives in equidistant time series (TS) with short-memory stationary errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel smoother is also built-in as a comparison. With version 1.1.0, a linearity test for the trend function, forecasting methods and backtesting approaches are implemented as well. The smoothing methods of the package are described in Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.

Maintained by Dominik Schulz. Last updated 2 years ago.

openblas cpp openmp

17.4 match 2.65 score 6 scripts 3 dependents

business-science

sweep:Tidy Tools for Forecasting

Tidies up the forecasting modeling and prediction work flow, extends the 'broom' package with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions for various forecasting models, and enables converting 'forecast' objects to "tidy" data frames with 'sw_sweep'.

Maintained by Matt Dancho. Last updated 1 years ago.

broom forecast forecasting-models prediction tidy tidyverse time time-series timeseries

4.9 match 155 stars 9.26 score 399 scripts 1 dependents

jazznbass

scan:Single-Case Data Analyses for Single and Multiple Baseline Designs

A collection of procedures for analysing, visualising, and managing single-case data. These include piecewise linear regression models, multilevel models, overlap indices ('PND', 'PEM', 'PAND', 'PET', 'tau-u', 'baseline corrected tau', 'CDC'), and randomization tests. Data preparation functions support outlier detection, handling missing values, scaling, and custom transformations. An export function helps to generate html, word, and latex tables in a publication friendly style. More details can be found in the online book 'Analyzing single-case data with R and scan', Juergen Wilbert (2025) <https://jazznbass.github.io/scan-Book/>.

Maintained by Juergen Wilbert. Last updated 16 days ago.

7.1 match 4 stars 6.42 score 62 scripts 1 dependents

cmsaf

cmsafops:Tools for CM SAF NetCDF Data

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The 'cmsafops' R-package provides a collection of R-operators for the analysis and manipulation of CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, longitude and latitude dimension should be applicable, but there is no guarantee for an error-free application. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (<http://www.cmsaf.eu/R_toolbox>).

Maintained by Steffen Kothe. Last updated 6 months ago.

9.0 match 2 stars 5.03 score 4 scripts 2 dependents

joachim-gassen

ExPanDaR:Explore Your Data Interactively

Provides a shiny-based front end (the 'ExPanD' app) and a set of functions for exploratory data analysis. Run as a web-based app, 'ExPanD' enables users to assess the robustness of empirical evidence without providing them access to the underlying data. You can export a notebook containing the analysis of 'ExPanD' and/or use the functions of the package to support your exploratory data analysis workflow. Refer to the vignettes of the package for more information on how to use 'ExPanD' and/or the functions of this package.

Maintained by Joachim Gassen. Last updated 4 years ago.

accounting eda exploratory-data-analysis finance open-science replication shiny shiny-apps

5.8 match 156 stars 7.80 score 203 scripts

rvlenth

emmeans:Estimated Marginal Means, aka Least-Squares Means

Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.

Maintained by Russell V. Lenth. Last updated 4 days ago.

2.3 match 377 stars 19.19 score 13k scripts 187 dependents

dsstoffer

astsa:Applied Statistical Time Series Analysis

Contains data sets and scripts for analyzing time series in both the frequency and time domains including state space modeling as well as supporting the texts Time Series Analysis and Its Applications: With R Examples (5th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2025, <https://link.springer.com/book/9783031705830>, and Time Series: A Data Analysis Approach Using R. Chapman-Hall, 2019, <DOI:10.1201/9780429273285>.

Maintained by David Stoffer. Last updated 2 months ago.

5.6 match 7 stars 7.88 score 2.2k scripts 8 dependents

vlyubchich

lawstat:Tools for Biostatistics, Public Policy, and Law

Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.

Maintained by Yulia R. Gel. Last updated 2 years ago.

5.9 match 7.17 score 484 scripts 6 dependents

r-forge

Sleuth2:Data Sets from Ramsey and Schafer's "Statistical Sleuth (2nd Ed)"

Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed)", Duxbury.

Maintained by Berwin A Turlach. Last updated 1 years ago.

7.4 match 5.70 score 191 scripts

flr

FLa4a:A Simple and Robust Statistical Catch at Age Model

A simple and robust statistical Catch at Age model that is specifically designed for stocks with intermediate levels of data quantity and quality.

Maintained by Ernesto Jardim. Last updated 6 days ago.

6.3 match 12 stars 6.66 score 177 scripts 2 dependents

alowis

RtsEva:Performs the Transformed-Stationary Extreme Values Analysis

Adaptation of the 'Matlab' 'tsEVA' toolbox developed by Lorenzo Mentaschi available here: <https://github.com/menta78/tsEva>. It contains an implementation of the Transformed-Stationary (TS) methodology for non-stationary extreme value Analysis (EVA) as described in Mentaschi et al. (2016) <doi:10.5194/hess-20-3527-2016>. In synthesis this approach consists in: (i) transforming a non-stationary time series into a stationary one to which the stationary extreme value theory can be applied; and (ii) reverse-transforming the result into a non-stationary extreme value distribution. 'RtsEva' offers several options for trend estimation (mean, extremes, seasonal) and contains multiple plotting functions displaying different aspects of the non-stationarity of extremes.

Maintained by Alois Tilloy. Last updated 6 months ago.

extreme-value-statistics non-stationary-environment

7.6 match 4 stars 5.41 score 4 scripts

jonathan-g

kayadata:Kaya Identity Data for Nations and Regions

Provides data for Kaya identity variables (population, gross domestic product, primary energy consumption, and energy-related CO2 emissions) for the world and for individual nations, and utility functions for looking up data, plotting trends of Kaya variables, and plotting the fuel mix for a given country or region. The Kaya identity (Yoichi Kaya and Keiichi Yokobori, "Environment, Energy, and Economy: Strategies for Sustainability" (United Nations University Press, 1998) and <https://en.wikipedia.org/wiki/Kaya_identity>) expresses a nation's or region's greenhouse gas emissions in terms of its population, per-capita Gross Domestic Product, the energy intensity of its economy, and the carbon-intensity of its energy supply.

Maintained by Jonathan Gilligan. Last updated 8 months ago.

8.2 match 4.98 score 32 scripts

opengeos

whitebox:'WhiteboxTools' R Frontend

An R frontend for the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) <doi:10.1016/j.cageo.2016.07.003>.

Maintained by Andrew Brown. Last updated 5 months ago.

geomorphometry geoprocessing geospatial gis hydrology remote-sensing rstudio

4.1 match 173 stars 9.65 score 203 scripts 2 dependents

paul-haimerl

BTtest:Estimate the Number of Factors in Large Nonstationary Datasets

Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties. The package implements the Barigozzi and Trapani (2022) <doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors. Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) <doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the number of factors.

Maintained by Paul Haimerl. Last updated 13 days ago.

common-trends nonstationary-panels openblas cpp openmp

10.1 match 3 stars 3.95 score 4 scripts

christophsax

seasonal:R Interface to X-13-ARIMA-SEATS

Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the 'seasonalview' package. Uses the X-13-binaries from the 'x13binary' package.

Maintained by Christoph Sax. Last updated 17 days ago.

seasonal-adjustment time-series

3.3 match 120 stars 12.03 score 1.1k scripts 8 dependents

beckerbenj

eatGADS:Data Management of Large Hierarchical Data

Import 'SPSS' data, handle and change 'SPSS' meta data, store and access large hierarchical data in 'SQLite' data bases.

Maintained by Benjamin Becker. Last updated 24 days ago.

5.1 match 1 stars 7.36 score 34 scripts 1 dependents

tiesbos

EquiTrends:Equivalence Testing for Pre-Trends in Difference-in-Differences Designs

Testing for parallel trends is crucial in the Difference-in-Differences framework. To this end, this package performs equivalence testing in the context of Difference-in-Differences estimation. It allows users to test if pre-treatment trends in the treated group are “equivalent” to those in the control group. Here, “equivalence” means that rejection of the null hypothesis implies that a function of the pre-treatment placebo effects (maximum absolute, average or root mean squared value) does not exceed a pre-specified threshold below which trend differences are considered negligible. The package is based on the theory developed in Dette & Schumann (2024) <doi:10.1080/07350015.2024.2308121>.

Maintained by Ties Bos. Last updated 6 months ago.

openblas cpp

10.5 match 1 stars 3.54 score 4 scripts

handcock

RDS:Respondent-Driven Sampling

Provides functionality for carrying out estimation with data collected using Respondent-Driven Sampling. This includes Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential Sampling estimator. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data. See Gile and Handcock (2010) <doi:10.1111/j.1467-9531.2010.01223.x>, Gile and Handcock (2015) <doi:10.1111/rssa.12091> and Gile, Beaudry, Handcock and Ott (2018) <doi:10.1146/annurev-statistics-031017-100704>.

Maintained by Mark S. Handcock. Last updated 6 months ago.

9.6 match 1 stars 3.87 score 82 scripts 3 dependents

csids

csalert:Alerts from Public Health Surveillance Data

Helps create alerts and determine trends by using various methods to analyze public health surveillance data. The primary analysis method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>.

Maintained by Richard Aubrey White. Last updated 9 months ago.

csverse

9.3 match 1 stars 4.00 score

jocelynchi

L2E:Robust Structured Regression via the L2 Criterion

An implementation of a computational framework for performing robust structured regression with the L2 criterion from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and Lange (2022+) added in Version 2.0.

Maintained by Jocelyn Chi. Last updated 3 years ago.

13.7 match 2.70 score 2 scripts

usepa

spsurvey:Spatial Sampling Design and Analysis

A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. For additional details, see Dumelle et al. (2023) <doi:10.18637/jss.v105.i03>.

Maintained by Michael Dumelle. Last updated 7 months ago.

ord

4.1 match 15 stars 8.98 score 241 scripts 1 dependents

holgerschw

scrime:Analysis of High-Dimensional Categorical Data Such as SNP Data

Tools for the analysis of high-dimensional data developed/implemented at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME). Main focus is on SNP data. But most of the functions can also be applied to other types of categorical data.

Maintained by Holger Schwender. Last updated 6 years ago.

7.1 match 5.10 score 53 scripts 35 dependents

kwstat

agridat:Agricultural Datasets

Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.

Maintained by Kevin Wright. Last updated 28 days ago.

data

3.3 match 125 stars 11.02 score 1.7k scripts 2 dependents

jemus42

tRakt:Get Data from 'trakt.tv'

A wrapper for the <https://trakt.tv> API to retrieve data about shows and movies, including user ratings, credits and related metadata. Additional functions retrieve user-specific information including collections and history of watched items. A full API reference is available at <https://trakt.docs.apiary.io>.

Maintained by Lukas Burk. Last updated 4 hours ago.

trakt trakt-api trakttv

5.9 match 22 stars 6.12 score 33 scripts

bcallaway11

did:Treatment Effects with Multiple Periods and Groups

The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group. Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time. This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>. The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time. These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these. There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.

Maintained by Brantly Callaway. Last updated 4 months ago.

3.0 match 327 stars 12.01 score 696 scripts 3 dependents

patakamuri

trendchange:Innovative Trend Analysis and Time-Series Change Point Analysis

Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series. Zekai, S. (2011). <doi:10.1061/(ASCE)HE.1943-5584.0000556>. Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp.

Maintained by Sandeep Kumar Patakamuri. Last updated 3 years ago.

10.9 match 4 stars 3.30 score 7 scripts

spatstat

spatstat.model:Parametric Statistical Modelling and Inference for the 'spatstat' Family

Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Maintained by Adrian Baddeley. Last updated 8 days ago.

analysis-of-variance cluster-process confidence-intervals cox-process determinantal-point-processes gibbs-process influence leverage model-diagnostics neyman-scott parameter-estimation poisson-process spatial-analysis spatial-modelling spatial-point-processes statistical-inference

3.9 match 5 stars 9.09 score 6 scripts 46 dependents

wviechtb

metadat:Meta-Analysis Datasets

A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses.

Maintained by Wolfgang Viechtbauer. Last updated 3 days ago.

dataset datasets meta-analysis

3.4 match 30 stars 10.54 score 65 scripts 93 dependents

r-forge

fUnitRoots:Rmetrics - Modelling Trends and Unit Roots

Provides four addons for analyzing trends and unit roots in financial time series: (i) functions for the density and probability of the augmented Dickey-Fuller Test, (ii) functions for the density and probability of MacKinnon's unit root test statistics, (iii) reimplementations for the ADF and MacKinnon Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's unit root test suite.

Maintained by Georgi N. Boshnakov. Last updated 3 months ago.

fortran

5.4 match 1 stars 6.58 score 292 scripts

hubbardalex

autostsm:Automatic Structural Time Series Models

Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.

Maintained by Alex Hubbard. Last updated 9 months ago.

10.0 match 3.55 score 29 scripts

business-science

tidyquant:Tidy Quantitative Financial Analysis

Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. The main advantage is being able to use quantitative functions with the 'tidyverse' functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See the 'tidyquant' website for more information, documentation and examples.

Maintained by Matt Dancho. Last updated 1 months ago.

dplyr financial-analysis financial-data financial-statements multiple-stocks performance-analysis performanceanalytics quantmod stock stock-exchanges stock-indexes stock-lists stock-performance stock-prices stock-symbol tidyverse time-series timeseries xts

2.6 match 872 stars 13.34 score 5.2k scripts

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

3.3 match 549 stars 10.57 score 1.1k scripts 7 dependents

petolau

TSrepr:Time Series Representations

Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining. Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson), and forecasting accuracy measures are implemented.

Maintained by Peter Laurinec. Last updated 5 years ago.

data-analysis data-mining data-mining-algorithms data-science representation time-series time-series-analysis time-series-classification time-series-clustering time-series-data-mining time-series-representations cpp

4.8 match 97 stars 7.23 score 117 scripts

happma

pseudorank:Pseudo-Ranks

Efficient calculation of pseudo-ranks and (pseudo)-rank based test statistics. In case of equal sample sizes, pseudo-ranks and mid-ranks are equal. When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem. See Happ et al. (2020, <doi:10.18637/jss.v095.c01>) for details.

Maintained by Martin Happ. Last updated 27 days ago.

cpp nonparametric nonparametric-statistics pseudo-rank pseudo-ranks rank rank-tests trend-test cpp

9.3 match 3 stars 3.71 score 17 scripts

tetratech

baytrends:Long Term Water Quality Trend Analysis

Enable users to evaluate long-term trends using a Generalized Additive Modeling (GAM) approach. The model development includes selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporation of hydrologic variability via either a river flow or salinity, the use of an intervention to deal with method or laboratory changes suspected to impact data values, and representation of left- and interval-censored data. The approach has been applied to water quality data in the Chesapeake Bay, a major estuary on the east coast of the United States to provide insights to a range of management- and research-focused questions. Methodology described in Murphy (2019) <doi:10.1016/j.envsoft.2019.03.027>.

Maintained by Erik W Leppo. Last updated 5 months ago.

5.1 match 12 stars 6.67 score 97 scripts

bxc147

Epi:Statistical Analysis in Epidemiology

Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data. In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.

Maintained by Bendix Carstensen. Last updated 2 months ago.

3.4 match 4 stars 9.65 score 708 scripts 11 dependents

b-cubed-eu

b3gbi:General Biodiversity Indicators for Biodiversity Data Cubes

Calculate general biodiversity indicators from GBIF data cubes. Includes many common indicators such as species richness and evenness, which can be calculated over time (trends) or space (maps).

Maintained by Shawn Dove. Last updated 13 days ago.

biodiversity-indicators data-cubes

5.2 match 3 stars 6.26 score 34 scripts 1 dependents

ggobi

GGally:Extension to 'ggplot2'

The R package 'ggplot2' is a plotting system based on the grammar of graphics. 'GGally' extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.

Maintained by Barret Schloerke. Last updated 10 months ago.

2.0 match 597 stars 16.15 score 17k scripts 154 dependents

philipberg

baldur:Bayesian Hierarchical Modeling for Label-Free Proteomics

Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (<doi:10.1101/2023.05.11.540411>).

Maintained by Philip Berg. Last updated 7 months ago.

cpp

8.0 match 1 stars 4.00 score 8 scripts

paulojus

geoR:Analysis of Geostatistical Data

Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.

Maintained by Paulo Justiniano Ribeiro Jr. Last updated 1 years ago.

4.3 match 10 stars 7.57 score 1.8k scripts 12 dependents

andrisignorell

DescTools:Tools for Descriptive Statistics

A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.

Maintained by Andri Signorell. Last updated 16 hours ago.

fortran cpp

1.9 match 87 stars 16.70 score 7.7k scripts 99 dependents

alobondo

MCTrend:Monte Carlo Trend Analysis

Application of a test to rule out that trends detected in hydrological time series are explained exclusively by the randomness of the climate. Based on: Ricchetti, (2018) <https://repositorio.uchile.cl/handle/2250/168487>.

Maintained by Alonso Arriagada. Last updated 12 months ago.

8.4 match 3.70 score

skranz

ParallelTrendsPlot:Experimental Package: Plots to diagnose parallel trends in DID regression with additional control variables.

Experimental Package: Plots to diagnose parallel trends in DID regression with additional control variables.

Maintained by Sebastian Kranz. Last updated 3 years ago.

12.4 match 6 stars 2.48 score 3 scripts

biometris

LMMsolver:Linear Mixed Model Solver

An efficient and flexible system to solve sparse mixed model equations. Important applications are the use of splines to model spatial or temporal trends as described in Boer (2023). (<doi:10.1177/1471082X231178591>).

Maintained by Bart-Jan van Rossum. Last updated 2 months ago.

mixed-models splines cpp

3.8 match 11 stars 8.14 score 66 scripts 3 dependents

bpfaff

FRAPO:Financial Risk Modelling and Portfolio Optimisation with R

Accompanying package of the book 'Financial Risk Modelling and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.

Maintained by Bernhard Pfaff. Last updated 8 years ago.

6.5 match 11 stars 4.71 score 94 scripts

kassambara

rstatix:Pipe-Friendly Framework for Basic Statistical Tests

Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix. Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 'Cramer V' for the association between categorical variables. The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.

Maintained by Alboukadel Kassambara. Last updated 2 years ago.

2.0 match 456 stars 15.16 score 11k scripts 420 dependents

ryantibs

genlasso:Path Algorithm for Generalized Lasso Problems

Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) <doi:10.1080/10618600.2015.1008638>.

Maintained by Taylor B. Arnold. Last updated 2 years ago.

3.9 match 32 stars 7.66 score 160 scripts 6 dependents

cran

sgeostat:An Object-Oriented Framework for Geostatistical Modeling in S+

An Object-oriented Framework for Geostatistical Modeling in S+ containing functions for variogram estimation, variogram fitting and kriging as well as some plot functions. Written entirely in S, therefore works only for small data sets in acceptable computing time.

Maintained by Albrecht Gebhardt. Last updated 9 years ago.

fortran

8.6 match 1 stars 3.42 score 23 dependents

swampthingpaul

NADA2:Data Analysis for Censored Environmental Data

Contains methods described by Dennis Helsel in his book "Statistics for Censored Environmental Data using Minitab and R" (2011) and courses and videos at <https://practicalstats.com>. This package adds new functions to the `NADA` Package.

Maintained by Paul Julian. Last updated 6 months ago.

4.8 match 15 stars 6.16 score 16 scripts

larmarange

prevR:Estimating Regional Trends of a Prevalence from a DHS and Similar Surveys

Spatial estimation of a prevalence surface or a relative risks surface, using data from a Demographic and Health Survey (DHS) or an analog survey, see Larmarange et al. (2011) <doi:10.4000/cybergeo.24606>.

Maintained by Joseph Larmarange. Last updated 5 months ago.

4.7 match 5 stars 6.26 score 46 scripts

ambarbosa

ecotrends:Temporal Trends in Ecological Niche Models

Computes temporal trends in environmental suitability obtained from ecological niche models, based on a set of species presence point coordinates and predictor variables.

Maintained by A. Marcia Barbosa. Last updated 12 days ago.

5.8 match 12 stars 5.01 score

cran

perm:Exact or Asymptotic Permutation Tests

Perform Exact or Asymptotic permutation tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].

Maintained by Michael P. Fay. Last updated 2 years ago.

5.9 match 4.83 score 118 scripts 9 dependents

sistm

TcGSA:Time-Course Gene Set Analysis

Implementation of Time-course Gene Set Analysis (TcGSA), a method for analyzing longitudinal gene-expression data at the gene set level. Method is detailed in: Hejblum, Skinner & Thiebaut (2015) <doi: 10.1371/journal.pcbi.1004310>.

Maintained by Boris P Hejblum. Last updated 3 years ago.

5.3 match 6 stars 5.38 score

trnnick

tsutils:Time Series Exploration, Modelling and Forecasting

Includes: (i) tests and visualisations that can help the modeller explore time series components and perform decomposition; (ii) modelling shortcuts, such as functions to construct lagmatrices and seasonal dummy variables of various forms; (iii) an implementation of the Theta method; (iv) tools to facilitate the design of the forecasting process, such as ABC-XYZ analyses; and (v) "quality of life" functions, such as treating time series for trailing and leading values.

Maintained by Nikolaos Kourentzes. Last updated 1 years ago.

3.6 match 12 stars 7.78 score 472 scripts 18 dependents

gabrielblain

SPIChanges:Improves the Interpretation of the Standardized Precipitation Index Under Changing Climate Conditions

Improves the interpretation of the Standardized Precipitation Index under changing climate conditions. The package uses the nonstationary approach proposed in Blain et al. (2022) <doi:10.1002/joc.7550> to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a drought event occurring.

Maintained by Gabriel Constantino Blain. Last updated 1 months ago.

4.8 match 4 stars 5.88 score

mlr-org

mlr3:Machine Learning in R - Next Generation

Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.

Maintained by Marc Becker. Last updated 5 days ago.

classification data-science machine-learning mlr3 regression

1.9 match 972 stars 14.86 score 2.3k scripts 35 dependents

jomulder

BFpack:Flexible Bayes Factor Testing of Scientific Expectations

Implementation of default Bayes factors for testing statistical hypotheses under various statistical models. The package is intended for applied quantitative researchers in the social and behavioral sciences, medical research, and related fields. The Bayes factor tests can be executed for statistical models such as univariate and multivariate normal linear models, correlation analysis, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., group means, regression coefficients), variances (e.g., group variances), and measures of association (e.g,. polychoric/polyserial/biserial/tetrachoric/product moments correlations), among others. The statistical underpinnings are described in O'Hagan (1995) <DOI:10.1111/j.2517-6161.1995.tb02017.x>, De Santis and Spezzaferri (2001) <DOI:10.1016/S0378-3758(00)00240-8>, Mulder and Xin (2022) <DOI:10.1080/00273171.2021.1904809>, Mulder and Gelissen (2019) <DOI:10.1080/02664763.2021.1992360>, Mulder (2016) <DOI:10.1016/j.jmp.2014.09.004>, Mulder and Fox (2019) <DOI:10.1214/18-BA1115>, Mulder and Fox (2013) <DOI:10.1007/s11222-011-9295-3>, Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) <DOI:10.1037/met0000116>, Hoijtink, Mulder, van Lissa, and Gu (2018) <DOI:10.1037/met0000201>, Gu, Mulder, and Hoijtink (2018) <DOI:10.1111/bmsp.12110>, Hoijtink, Gu, and Mulder (2018) <DOI:10.1111/bmsp.12145>, and Hoijtink, Gu, Mulder, and Rosseel (2018) <DOI:10.1037/met0000187>. When using the packages, please refer to the package Mulder et al. (2021) <DOI:10.18637/jss.v100.i18> and the relevant methodological papers.

Maintained by Joris Mulder. Last updated 1 months ago.

fortran openblas

3.4 match 15 stars 8.24 score 55 scripts 3 dependents

cienciadedatos

datos:Traduce al Español Varios Conjuntos de Datos de Práctica

Provee una versión traducida de los siguientes conjuntos de datos: 'airlines', 'airports', 'AwardsManagers', 'babynames', 'Batting', 'credit_data', 'diamonds', 'faithful', 'fueleconomy', 'Fielding', 'flights', 'gapminder', 'gss_cat', 'iris', 'Managers', 'mpg', 'mtcars', 'atmos', 'palmerpenguins', 'People, 'Pitching', 'planes', 'presidential', 'table1', 'table2', 'table3', 'table4a', 'table4b', 'table5', 'vehicles', 'weather', 'who'. English: It provides a Spanish translated version of the datasets listed above.

Maintained by Riva Quiroga. Last updated 1 years ago.

3.4 match 48 stars 8.12 score 354 scripts

bioc

DESeq2:Differential gene expression analysis based on the negative binomial distribution

Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

Maintained by Michael Love. Last updated 11 days ago.

sequencing rnaseq chipseq geneexpression transcription normalization differentialexpression bayesian regression principalcomponent clustering immunooncology openblas cpp

1.7 match 375 stars 16.11 score 17k scripts 115 dependents

palaeoverse

palaeoverse:Prepare and Explore Data for Palaeobiological Analyses

Provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of 'palaeoverse' is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales <https://stratigraphy.org/chart>) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) <doi: 10.1111/2041-210X.14099>.

Maintained by Lewis A. Jones. Last updated 5 months ago.

biodiversity fossil palaeobiology paleobiology

3.1 match 21 stars 8.57 score 44 scripts 1 dependents

franciscomartinezdelrio

utsf:Univariate Time Series Forecasting

An engine for univariate time series forecasting using different regression models in an autoregressive way. The engine provides an uniform interface for applying the different models. Furthermore, it is extensible so that users can easily apply their own regression models to univariate time series forecasting and benefit from all the features of the engine, such as preprocessings or estimation of forecast accuracy.

Maintained by Francisco Martinez. Last updated 28 days ago.

5.1 match 2 stars 5.23 score 4 scripts

snstatcomp

rtrim:Trends and Indices for Monitoring Data

The TRIM model is widely used for estimating growth and decline of animal populations based on (possibly sparsely available) count data. The current package is a reimplementation of the original TRIM software developed at Statistics Netherlands by Jeroen Pannekoek. See <https://www.cbs.nl/en-gb/society/nature-and-environment/indices-and-trends%2d%2dtrim%2d%2d> for more information about TRIM.

Maintained by Patrick Bogaart. Last updated 12 days ago.

3.6 match 10 stars 7.33 score 60 scripts 1 dependents

ropensci

europepmc:R Interface to the Europe PubMed Central RESTful Web Service

An R Client for the Europe PubMed Central RESTful Web Service (see <https://europepmc.org/RestfulWebService> for more information). It gives access to both metadata on life science literature and open access full texts. Europe PMC indexes all PubMed content and other literature sources including Agricola, a bibliographic database of citations to the agricultural literature, or Biological Patents. In addition to bibliographic metadata, the client allows users to fetch citations and reference lists. Links between life-science literature and other EBI databases, including ENA, PDB or ChEMBL are also accessible. No registration or API key is required. See the vignettes for usage examples.

Maintained by Najko Jahn. Last updated 1 years ago.

bibliometrics europe-pmc pubmed pubmedcentral scientific-literature scientific-publications

3.3 match 27 stars 7.94 score 122 scripts 2 dependents

fawda123

SWMPr:Retrieving, Organizing, and Analyzing Estuary Monitoring Data

Tools for retrieving, organizing, and analyzing environmental data from the System Wide Monitoring Program of the National Estuarine Research Reserve System <https://cdmo.baruch.sc.edu/>. These tools address common challenges associated with continuous time series data for environmental decision making.

Maintained by Marcus W. Beck. Last updated 1 months ago.

3.8 match 13 stars 7.05 score 143 scripts 1 dependents

sakoehler7

eesim:Simulate and Evaluate Time Series for Environmental Epidemiology

Provides functions to create simulated time series of environmental exposures (e.g., temperature, air pollution) and health outcomes for use in power analysis and simulation studies in environmental epidemiology. This package also provides functions to evaluate the results of simulation studies based on these simulated time series. This work was supported by a grant from the National Institute of Environmental Health Sciences (R00ES022631) and a fellowship from the Colorado State University Programs for Research and Scholarly Excellence.

Maintained by Brooke Anderson. Last updated 8 years ago.

4.9 match 8 stars 5.23 score 42 scripts

saudiwin

ordbetareg:Ordered Beta Regression Models with 'brms'

Implements ordered beta regression models, which are for modeling continuous variables with upper and lower bounds, such as survey sliders, dose-response relationships and indexes. For more information, see Kubinec (2023) <doi:10.31235/osf.io/2sx6y>. The package is a front-end to the R package 'brms', which facilitates a range of regression specifications, including hierarchical, dynamic and multivariate modeling.

Maintained by Robert Kubinec. Last updated 1 months ago.

3.5 match 21 stars 7.35 score 38 scripts

euanmcgonigle

TrendLSW:Wavelet Methods for Analysing Locally Stationary Time Series

Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. New users will likely want to start with the TLSW function.

Maintained by Euan T. McGonigle. Last updated 11 months ago.

nonparametric-regression spectral-analysis spectrum time-series time-series-analysis wavelets

7.1 match 1 stars 3.60 score 3 scripts

usepa

spmodel:Spatial Statistical Modeling and Prediction

Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.

Maintained by Michael Dumelle. Last updated 4 days ago.

3.3 match 15 stars 7.66 score 112 scripts 3 dependents

cran

PytrendsLongitudinalR:Create Longitudinal Google Trends Data

'Google Trends' provides cross-sectional and time-series data on searches, but lacks readily available longitudinal data. Researchers, who want to create longitudinal 'Google Trends' on their own, face practical challenges, such as normalized counts that make it difficult to combine cross-sectional and time-series data and limitations in data formats and timelines that limit data granularity over extended time periods. This package addresses these issues and enables researchers to generate longitudinal 'Google Trends' data. This package is built on 'pytrends', a Python library that acts as the unofficial 'Google Trends API' to collect 'Google Trends' data. As long as the 'Google Trends API', 'pytrends' and all their dependencies are working, this package will work. During testing, we noticed that for the same input (keyword, topic, data_format, timeline), the output index can vary from time to time. Besides, if the keyword is not very popular, then the resulting dataset will contain a lot of zeros, which will greatly affect the final result. While this package has no control over the accuracy or quality of 'Google Trends' data, once the data is created, this package coverts it to longitudinal data. In addition, the user may encounter a 429 Too Many Requests error when using cross_section() and time_series() to collect 'Google Trends' data. This error indicates that the user has exceeded the rate limits set by the 'Google Trends API'. For more information about the 'Google Trends API' - 'pytrends', visit <https://pypi.org/project/pytrends/>.

Maintained by Taeyong Park. Last updated 6 months ago.

9.4 match 2.70 score 3 scripts

covaruber

sommer:Solving Mixed Model Equations in R

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.

Maintained by Giovanny Covarrubias-Pazaran. Last updated 22 days ago.

average-information mixed-models rcpparmadillo openblas cpp openmp

2.0 match 43 stars 12.70 score 300 scripts 9 dependents

steps-dev

steps:Spatially- and Temporally-Explicit Population Simulator

Software to simulate population change across space and time. Visintin et al. (2020) <doi:10.1111/2041-210X.13354>.

Maintained by Casey Visintin. Last updated 1 years ago.

meta-population-models cpp

3.8 match 18 stars 6.66 score 84 scripts

josucham

DendroSync:A Set of Tools for Calculating Spatial Synchrony Between Tree-Ring Chronologies

Provides functions for the calculation and plotting of synchrony in tree growth from tree-ring width chronologies (TRW index). It combines variance-covariance (VCOV) mixed modelling with functions that quantify the degree to which the TRW chronologies contain a common temporal signal. It also implements temporal trends in spatial synchrony using a moving window. These methods can also be used with other kind of ecological variables that have temporal autocorrelation corrected.

Maintained by Josu G. Alday. Last updated 6 years ago.

8.5 match 2.93 score 17 scripts

njbultman

sleeperapi:Wrapper Functions Around 'Sleeper' (Fantasy Sports) API

For those wishing to interact with the 'Sleeper' (Fantasy Sports) API (<https://docs.sleeper.com/>) without looking too much into its documentation (found at <https://docs.sleeper.com/>), this package offers wrapper functions around the available API calls to make it easier.

Maintained by Nick Bultman. Last updated 4 months ago.

5.4 match 6 stars 4.59 score 8 scripts

vincentarelbundock

marginaleffects:Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests

Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.

Maintained by Vincent Arel-Bundock. Last updated 16 hours ago.

cpp

1.7 match 505 stars 14.51 score 1.8k scripts 9 dependents

prabhanjan-tattar

gpk:100 Data Sets for Statistics Education

Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.

Maintained by Prabhanjan Tattar. Last updated 12 years ago.

14.7 match 1.69 score 49 scripts

lbbe-software

DRomics:Dose Response for Omics

Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a normal distribution for each dose or concentration, with a common standard error. For further details see Delignette-Muller et al (2023) <DOI:10.24072/pcjournal.325> and Larras et al (2018) <DOI:10.1021/acs.est.8b04752>.

Maintained by Aurelie Siberchicot. Last updated 13 days ago.

4.1 match 4 stars 5.96 score 7 scripts

hwborchers

pracma:Practical Numerical Math Functions

Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.

Maintained by Hans W. Borchers. Last updated 1 years ago.

2.0 match 29 stars 12.34 score 6.6k scripts 931 dependents

robjhyndman

stR:Seasonal Trend Decomposition Using Regression

Methods for decomposing seasonal data: STR (a Seasonal-Trend time series decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can also be used for forecasting.

Maintained by Rob Hyndman. Last updated 2 months ago.

3.5 match 7 stars 7.02 score 1.3k scripts 2 dependents

afmagee

CRABS:Congruent Rate Analyses in Birth-Death Scenarios

Features tools for exploring congruent phylogenetic birth-death models. It can construct the pulled speciation- and net-diversification rates from a reference model. Given alternative speciation- or extinction rates, it can construct new models that are congruent with the reference model. Functionality is included to sample new rate functions, and to visualize the distribution of one congruence class. See also Louca & Pennell (2020) <doi:10.1038/s41586-020-2176-1>.

Maintained by Bjørn Tore Kopperud. Last updated 1 years ago.

6.1 match 7 stars 4.02 score 5 scripts

bioc

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.

Maintained by Catalina Vallejos. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell differentialexpression bayesian cellbiology bioconductor-package gene-expression rcpp rcpparmadillo scrna-seq single-cell openblas cpp openmp

2.4 match 83 stars 10.26 score 368 scripts 1 dependents

cmu-delphi

epidatr:Client for Delphi's 'Epidata' API

The Delphi 'Epidata' API provides real-time access to epidemiological surveillance data for influenza, 'COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change 'Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron 'Rumack', Ryan J. 'Tibshirani', 'Roni' 'Rosenfeld' (2015). Delphi 'Epidata' API. <https://github.com/cmu-delphi/delphi-epidata>.

Maintained by David Weber. Last updated 4 months ago.

4.0 match 5 stars 6.14 score 114 scripts

rkabacoff

qacBase:Functions to Facilitate Exploratory Data Analysis

Functions for descriptive statistics, data management, and data visualization.

Maintained by Kabacoff Robert. Last updated 3 years ago.

eda statistics

4.7 match 1 stars 5.13 score 45 scripts

spatstat

spatstat.random:Random Generation Functionality for the 'spatstat' Family

Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'.

Maintained by Adrian Baddeley. Last updated 2 days ago.

point-processes random-generation simulation spatial-sampling spatial-simulation cpp

2.3 match 5 stars 10.81 score 84 scripts 175 dependents

devrekerd

TTAinterfaceTrendAnalysis:Temporal Trend Analysis Graphical Interface

This interface was created to develop a standard procedure to analyse temporal trend in the framework of the OSPAR convention. The analysis process run through 4 successive steps : 1) manipulate your data, 2) select the parameters you want to analyse, 3) build your regulated time series, 4) perform diagnosis and analysis and 5) read the results. Statistical analysis call other package function such as Kendall tests or cusum() function.

Maintained by David DEVREKER. Last updated 1 years ago.

7.1 match 3.36 score 2 scripts

jinghuazhao

gap:Genetic Analysis Package

As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18. <doi:10.18637/jss.v023.i08>], it is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap).

Maintained by Jing Hua Zhao. Last updated 17 days ago.

genetics imputation lmm fortran

2.0 match 12 stars 11.88 score 448 scripts 16 dependents

bioc

BiRewire:High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals)

Fast functions for bipartite network rewiring through N consecutive switching steps (See References) and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. Includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks and directed signed networks is also provided. Starting from version 1.9.7 a more precise bound (especially for small network) has been implemented. Starting from version 2.2.0 the analysis routine is more complete and a visual montioring of the underlying Markov Chain has been implemented. Starting from 3.6.0 the library can handle also matrices with NA (not for the directed signed graphs). Since version 3.27.1 it is possible to add a constraint for dsg generation: usually positive and negative arc between two nodes could be not accepted.

Maintained by Andrea Gobbi. Last updated 5 months ago.

network

5.1 match 4.54 score 35 scripts

briencj

asremlPlus:Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences

Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' <https://vsni.co.uk/> as 'asreml-R', who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 28 days ago.

asreml mixed-models

2.5 match 19 stars 9.34 score 200 scripts

murrayefford

secr:Spatially Explicit Capture-Recapture

Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.

Maintained by Murray Efford. Last updated 3 hours ago.

cpp

2.3 match 3 stars 10.16 score 410 scripts 5 dependents

signaturescience

rplanes:Plausibility Analysis of Epidemiological Signals

Provides functionality to prepare data and analyze plausibility of both forecasted and reported epidemiological signals. The functions implement a set of plausibility algorithms that are agnostic to geographic and time resolutions and are calculated independently then presented as a combined score.

Maintained by VP Nagraj. Last updated 8 months ago.

3.8 match 9 stars 6.03 score 7 scripts

anikoszabo

multiCA:Multinomial Cochran-Armitage Trend Test

Implements a generalization of the Cochran-Armitage trend test to multinomial data. In addition to an overall test, multiple testing adjusted p-values for trend in individual outcomes and power calculation is available.

Maintained by Aniko Szabo. Last updated 3 years ago.

9.1 match 2.48 score 6 scripts 1 dependents

luca-scr

qcc:Quality Control Charts

Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.

Maintained by Luca Scrucca. Last updated 2 years ago.

2.0 match 46 stars 11.29 score 730 scripts 6 dependents

mirovago

pRecipe:Precipitation R Recipes

An open-access tool/framework to download, validate, visualize, and analyze multi-source precipitation data. More information and an example of implementation can be found in Vargas Godoy and Markonis (2023, <doi:10.1016/j.envsoft.2023.105711>).

Maintained by Mijael Rodrigo Vargas Godoy. Last updated 3 months ago.

3.3 match 10 stars 6.79 score 69 scripts 1 dependents

chjackson

disbayes:Bayesian Multi-State Modelling of Chronic Disease Burden Data

Estimation of incidence and case fatality for a chronic disease, given partial information, using a multi-state model. Given data on age-specific mortality and either incidence or prevalence, Bayesian inference is used to estimate the posterior distributions of incidence, case fatality, and functions of these such as prevalence. The methods are described in Jackson et al. (2023) <doi:10.1093/jrsssa/qnac015>.

Maintained by Christopher Jackson. Last updated 1 years ago.

cpp

4.8 match 8 stars 4.60 score 10 scripts

imarkonis

twc:Terrestrial Water Cycle

An open-access tool/framework that constitutes the core functions to analyze terrestrial water cycle data across various spatio-temporal scales.

Maintained by Mijael Rodrigo Vargas Godoy. Last updated 3 months ago.

6.3 match 3.48 score 2 scripts 2 dependents

bbsbayes

bbsBayes2:Hierarchical Bayesian Analysis of North American BBS Data

The North American Breeding Bird Survey (BBS) is a long-running program that seeks to monitor the status and trends of the breeding birds in North America. Since its start in 1966, the BBS has accumulated over 50 years of data for over 500 species of North American Birds. Given the temporal and spatial structure of the data, hierarchical Bayesian models are used to assess the status and trends of these 500+ species of birds. 'bbsBayes2' allows you to perform hierarchical Bayesian analysis of BBS data. You can run a full model analysis for one or more species that you choose, or you can take more control and specify how the data should be stratified, prepared for 'Stan', or modelled.

Maintained by Brandon P.M. Edwards. Last updated 2 months ago.

4.6 match 8 stars 4.64 score 121 scripts

ropensci

rredlist:'IUCN' Red List Client

'IUCN' Red List (<https://api.iucnredlist.org/>) client. The 'IUCN' Red List is a global list of threatened and endangered species. Functions cover all of the Red List 'API' routes. An 'API' key is required.

Maintained by William Gearty. Last updated 1 months ago.

iucn biodiversity api web-services traits habitat species conservation api-wrapper iucn-red-list taxize

1.9 match 53 stars 11.49 score 195 scripts 24 dependents

rubenfcasal

npsp:Nonparametric Spatial Statistics

Multidimensional nonparametric spatial (spatio-temporal) geostatistics. S3 classes and methods for multidimensional: linear binning, local polynomial kernel regression (spatial trend estimation), density and variogram estimation. Nonparametric methods for simultaneous inference on both spatial trend and variogram functions (for spatial processes). Nonparametric residual kriging (spatial prediction). For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) <doi:10.1007/s00477-013-0817-8> or Castillo-Paez et al. (2019) <doi:10.1016/j.csda.2019.01.017>.

Maintained by Ruben Fernandez-Casal. Last updated 4 months ago.

geostatistics spatial-data-analysis statistics fortran openblas

3.8 match 4 stars 5.71 score 64 scripts

emilhvitfeldt

walmartAPI:Walmart Open API Wrapper

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.

Maintained by Emil Hvitfeldt. Last updated 5 years ago.

walmart-api

4.9 match 19 stars 4.39 score 13 scripts

ocbe-uio

contingencytables:Statistical Analysis of Contingency Tables

Provides functions to perform statistical inference of data organized in contingency tables. This package is a companion to the "Statistical Analysis of Contingency Tables" book by Fagerland et al. <ISBN 9781466588172>.

Maintained by Waldir Leoncio. Last updated 7 months ago.

contingency-table

5.2 match 3 stars 4.13 score 8 scripts 1 dependents

kassambara

datarium:Data Bank for Statistical Analysis and Visualization

Contains data organized by topics: categorical data, regression model, means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA.

Maintained by Alboukadel Kassambara. Last updated 1 months ago.

3.4 match 22 stars 6.29 score 358 scripts

rjdverse

rjd3sts:State Space Framework and Structural Time Series with 'JDemetra+ 3.x'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers access to several functions on state space models and structural time series.

Maintained by Jean Palate. Last updated 8 months ago.

openjdk

3.2 match 2 stars 6.64 score 25 scripts 4 dependents

ropensci

tsbox:Class-Agnostic Time Series

Time series toolkit with identical behavior for all time series classes: 'ts','xts', 'data.frame', 'data.table', 'tibble', 'zoo', 'timeSeries', 'tsibble', 'tis' or 'irts'. Also converts reliably between these classes.

Maintained by Christoph Sax. Last updated 5 months ago.

graphics time-series

2.0 match 150 stars 10.61 score 496 scripts 4 dependents

opendendro

dplR:Dendrochronology Program Library in R

Perform tree-ring analyses such as detrending, chronology building, and cross dating. Read and write standard file formats used in dendrochronology.

Maintained by Andy Bunn. Last updated 19 days ago.

fortran

1.8 match 39 stars 11.71 score 546 scripts 26 dependents

billdenney

PKNCA:Perform Pharmacokinetic Non-Compartmental Analysis

Compute standard Non-Compartmental Analysis (NCA) parameters for typical pharmacokinetic analyses and summarize them.

Maintained by Bill Denney. Last updated 17 days ago.

nca noncompartmental-analysis pharmacokinetics

1.7 match 73 stars 12.61 score 214 scripts 4 dependents

ssi-dk

aedseo:Automated and Early Detection of Seasonal Epidemic Onset and Burden Levels

A powerful tool for automating the early detection of seasonal epidemic onsets in time series data. It offers the ability to estimate growth rates across consecutive time intervals, calculate the sum of cases (SoC) within those intervals, and estimate seasonal onsets within user defined seasons. With use of a disease-specific threshold it also offers the possibility to estimate seasonal onset of epidemics. Additionally it offers the ability to estimate burden levels for seasons based on historical data. It is aimed towards epidemiologists, public health professionals, and researchers seeking to identify and respond to seasonal epidemics in a timely fashion. For reference on growth rate estimation, see Walling and Lipstich (2007) <doi:10.1098/rspb.2006.3754> and Obadia et al. (2012) <doi:10.1186/1472-6947-12-147>. Seasonal burden level calculations have been inspired by The Moving Epidemic Method (MEM), see Vega and Lozano (2012) <doi:10.1111/j.1750-2659.2012.00422.x>.

Maintained by Lasse Engbo Christiansen. Last updated 5 days ago.

3.6 match 1 stars 5.72 score 8 scripts

cran

TrendSLR:Estimating Trend, Velocity and Acceleration from Sea Level Records

Analysis of annual average ocean water level time series, providing improved estimates of trend (mean sea level) and associated real-time velocities and accelerations. Improved trend estimates are based on singular spectrum analysis methods. Various gap-filling options are included to accommodate incomplete time series records. The package also includes a range of diagnostic tools to inspect the components comprising the original time series which enables expert interpretation and selection of likely trend components. A wide range of screen and plot to file options are available in the package.

Maintained by Phil J Watson. Last updated 6 years ago.

20.7 match 1.00 score

cran

AlphaPart:Partition/Decomposition of Breeding Values by Paths of Information

A software that implements a method for partitioning genetic trends to quantify the sources of genetic gain in breeding programmes. The partitioning method is described in Garcia-Cortes et al. (2008) <doi:10.1017/S175173110800205X>. The package includes the main function AlphaPart for partitioning breeding values and auxiliary functions for manipulating data and summarizing, visualizing, and saving results.

Maintained by Gregor Gorjanc. Last updated 2 years ago.

cpp

7.6 match 2.72 score 26 scripts

kopperud

slouch:Stochastic Linear Ornstein-Uhlenbeck Comparative Hypotheses

An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.

Maintained by Bjørn Tore Kopperud. Last updated 1 years ago.

4.0 match 2 stars 5.12 score 44 scripts 1 dependents

hydauer

FlowScreen:Daily Streamflow Trend and Change Point Screening

Screens daily streamflow time series for temporal trends and change-points. This package has been primarily developed for assessing the quality of daily streamflow time series. It also contains tools for plotting and calculating many different streamflow metrics. The package can be used to produce summary screening plots showing change-points and significant temporal trends for high flow, low flow, and/or baseflow statistics, or it can be used to perform more detailed hydrological time series analyses. The package was designed for screening daily streamflow time series from Water Survey Canada and the United States Geological Survey but will also work with streamflow time series from many other agencies.

Maintained by Jennifer Dierauer. Last updated 6 years ago.

10.6 match 1 stars 1.93 score 43 scripts

kevinstadler

cultevo:Tools, Measures and Statistical Tests for Cultural Evolution

Provides tools and statistics useful for analysing data from artificial language experiments. It implements the information-theoretic measure of the compositionality of signalling systems due to Spike (2016) <http://hdl.handle.net/1842/25930>, the Mantel test for distance matrix correlation (after Dietz 1983) <doi:10.1093/sysbio/32.1.21>), functions for computing string and meaning distance matrices as well as an implementation of the Page test for monotonicity of ranks (Page 1963) <doi:10.1080/01621459.1963.10500843> with exact p-values up to k = 22.

Maintained by Kevin Stadler. Last updated 1 years ago.

3.1 match 8 stars 6.50 score 131 scripts 1 dependents

svmiller

stevedata:Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics

This is a collection of various kinds of data with broad uses for teaching. My students, and academics like me who teach the same topics I teach, should find this useful if their teaching workflow is also built around the R programming language. The applications are multiple but mostly cluster on topics of statistical methodology, international relations, and political economy.

Maintained by Steve Miller. Last updated 4 days ago.

3.4 match 8 stars 5.97 score 178 scripts

rpkgs

rtrend:Trend Estimating Tools

The traditional linear regression trend, Modified Mann-Kendall (MK) non-parameter trend and bootstrap trend are included in this package. Linear regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'. Finally, those functions are about 10 times faster than previous version in R. Reference: Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. <doi:10.1016/S0022-1694(97)00125-X>.

Maintained by Dongdong Kong. Last updated 1 years ago.

openblas cpp openmp

4.4 match 3 stars 4.56 score 24 scripts

gjmvanboxtel

gsignal:Signal Processing

R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

Maintained by Geert van Boxtel. Last updated 2 months ago.

signal-processing signals cpp

2.0 match 24 stars 10.03 score 133 scripts 34 dependents

cran

TSSS:Time Series Analysis with State Space Model

Functions for statistical analysis, modeling and simulation of time series with state space model, based on the methodology in Kitagawa (2020, ISBN: 978-0-367-18733-0).

Maintained by Masami Saga. Last updated 1 years ago.

fortran

11.2 match 2 stars 1.78 score

bioc

ProteoMM:Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform

ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009).

Maintained by Yuliya V Karpievitch. Last updated 5 months ago.

immunooncology massspectrometry proteomics normalization differentialexpression

5.9 match 3.38 score 12 scripts

dschulz13

deseats:Data-Driven Locally Weighted Regression for Trend and Seasonality in TS

Various methods for the identification of trend and seasonal components in time series (TS) are provided. Among them is a data-driven locally weighted regression approach with automatically selected bandwidth for equidistant short-memory time series. The approach is a combination / extension of the algorithms by Feng (2013) <doi:10.1080/02664763.2012.740626> and Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598> and a brief description of this new method is provided in the package documentation. Furthermore, the package allows its users to apply the base model of the Berlin procedure, version 4.1, as described in Speth (2004) <https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>. Permission to include this procedure was kindly provided by the Federal Statistical Office of Germany.

Maintained by Dominik Schulz. Last updated 8 months ago.

openblas cpp openmp

11.6 match 1.70 score 2 scripts

giorgilancs

PrevMap:Geostatistical Modelling of Spatially Referenced Prevalence Data

Provides functions for both likelihood-based and Bayesian analysis of spatially referenced prevalence data. For a tutorial on the use of the R package, see Giorgi and Diggle (2017) <doi:10.18637/jss.v078.i08>.

Maintained by Emanuele Giorgi. Last updated 2 years ago.

4.5 match 4.36 score 46 scripts

blasbenito

distantia:Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) <doi:10.1111/ecog.04895>, provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data.

Maintained by Blas M. Benito. Last updated 26 days ago.

3.4 match 23 stars 5.76 score 11 scripts

mstrimas

colorist:Coloring Wildlife Distributions in Space-Time

Color and visualize wildlife distributions in space-time using raster data. In addition to enabling display of sequential change in distributions through the use of small multiples, 'colorist' provides functions for extracting several features of interest from a sequence of distributions and for visualizing those features using HCL (hue-chroma-luminance) color palettes. Resulting maps allow for "fair" visual comparison of intensity values (e.g., occurrence, abundance, or density) across space and time and can be used to address questions about where, when, and how consistently a species, group, or individual is likely to be found.

Maintained by Matthew Strimas-Mackey. Last updated 11 months ago.

3.4 match 14 stars 5.60 score 19 scripts

blasif

cocons:Covariate-Based Covariance Functions for Nonstationary Spatial Modeling

Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.

Maintained by Federico Blasi. Last updated 2 months ago.

covariance-matrix cpp estimation gaussian-processes large-dataset nonstationarity optimization prediction cpp

3.5 match 3 stars 5.48 score 1 scripts

jepusto

scdhlm:Estimating Hierarchical Linear Models for Single-Case Designs

Provides a set of tools for estimating hierarchical linear models and effect sizes based on data from single-case designs. Functions are provided for calculating standardized mean difference effect sizes that are directly comparable to standardized mean differences estimated from between-subjects randomized experiments, as described in Hedges, Pustejovsky, and Shadish (2012) <DOI:10.1002/jrsm.1052>; Hedges, Pustejovsky, and Shadish (2013) <DOI:10.1002/jrsm.1086>; Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>; and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) <DOI:10.1016/j.jsp.2023.02.002>. Includes an interactive web interface.

Maintained by James Pustejovsky. Last updated 1 years ago.

3.4 match 4 stars 5.62 score 52 scripts

mattmar

dynamAedes:A Unified Mechanistic Model for the Population Dynamics of Invasive Aedes Mosquitoes

Generalised model for population dynamics of invasive Aedes mosquitoes. Rationale and model structure are described here: Da Re et al. (2021) <doi:10.1016/j.ecoinf.2020.101180> and Da Re et al. (2022) <doi:10.1101/2021.12.21.473628>.

Maintained by Matteo Marcantonio. Last updated 1 years ago.

ecology invasive-species modelling mosquitoes pathogens

3.4 match 7 stars 5.59 score 11 scripts

theoreticalecology

sjSDM:Scalable Joint Species Distribution Modeling

A scalable and fast method for estimating joint Species Distribution Models (jSDMs) for big community data, including eDNA data. The package estimates a full (i.e. non-latent) jSDM with different response distributions (including the traditional multivariate probit model). The package allows to perform variation partitioning (VP) / ANOVA on the fitted models to separate the contribution of environmental, spatial, and biotic associations. In addition, the total R-squared can be further partitioned per species and site to reveal the internal metacommunity structure, see Leibold et al., <doi:10.1111/oik.08618>. The internal structure can then be regressed against environmental and spatial distinctiveness, richness, and traits to analyze metacommunity assembly processes. The package includes support for accounting for spatial autocorrelation and the option to fit responses using deep neural networks instead of a standard linear predictor. As described in Pichler & Hartig (2021) <doi:10.1111/2041-210X.13687>, scalability is achieved by using a Monte Carlo approximation of the joint likelihood implemented via 'PyTorch' and 'reticulate', which can be run on CPUs or GPUs.

Maintained by Maximilian Pichler. Last updated 24 days ago.

deep-learning gpu-acceleration machine-learning species-distribution-modelling species-interactions

2.5 match 69 stars 7.64 score 70 scripts

geoffjentry

twitteR:R Based Twitter Client

Provides an interface to the Twitter web API.

Maintained by Jeff Gentry. Last updated 9 years ago.

1.9 match 254 stars 10.18 score 2.0k scripts 1 dependents

cran

datarobot:'DataRobot' Predictive Modeling API

For working with the 'DataRobot' predictive modeling platform's API <https://www.datarobot.com/>.

Maintained by AJ Alon. Last updated 1 years ago.

5.5 match 2 stars 3.48 score

macroeconomicdata

dateutils:Date Utils

Utilities for mixed frequency data. In particular, use to aggregate and normalize tabular mixed frequency data, index dates to end of period, and seasonally adjust tabular data.

Maintained by Seth Leonard. Last updated 3 years ago.

data-processing econometrics time-series openblas cpp

3.7 match 3 stars 5.17 score 49 scripts

aleksandarsekulic

meteo:RFSI & STRK Interpolation for Meteo and Environmental Variables

Random Forest Spatial Interpolation (RFSI, Sekulić et al. (2020) <doi:10.3390/rs12101687>) and spatio-temporal geostatistical (spatio-temporal regression Kriging (STRK)) interpolation for meteorological (Kilibarda et al. (2014) <doi:10.1002/2013JD020803>, Sekulić et al. (2020) <doi:10.1007/s00704-019-03077-3>) and other environmental variables. Contains global spatio-temporal models calculated using publicly available data.

Maintained by Aleksandar Sekulić. Last updated 6 months ago.

3.8 match 18 stars 5.06 score 64 scripts

chris31415926535

storywranglr:Explore Twitter Trends with the 'Storywrangler' API

An interface to explore trends in Twitter data using the 'Storywrangler' Application Programming Interface (API), which can be found here: <https://github.com/janeadams/storywrangler>.

Maintained by Christopher Belanger. Last updated 4 years ago.

7.0 match 2.70 score 3 scripts

david-hervas

clickR:Semi-Automatic Preprocessing of Messy Data with Change Tracking for Dataset Cleaning

Tools for assessing data quality, performing exploratory analysis, and semi-automatic preprocessing of messy data with change tracking for integral dataset cleaning.

Maintained by David Hervas Marin. Last updated 17 days ago.

3.4 match 2 stars 5.53 score 25 scripts 1 dependents

massbays-tech

MassWateR:Quality Control and Analysis of Massachusetts Water Quality Data

Methods for quality control and exploratory analysis of surface water quality data collected in Massachusetts, USA. Functions are developed to facilitate data formatting for the Water Quality Exchange Network <https://www.epa.gov/waterdata/water-quality-data-upload-wqx> and reporting of data quality objectives to state agencies. Quality control methods are from Massachusetts Department of Environmental Protection (2020) <https://www.mass.gov/orgs/massachusetts-department-of-environmental-protection>.

Maintained by Marcus Beck. Last updated 11 days ago.

3.6 match 13 stars 5.26 score 10 scripts

kryberg-usgs

seawaveQ:SEAWAVE-Q Model

A model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables. See Ryberg and York, 2020, <doi:10.3133/ofr20201082>.

Maintained by Karen R. Ryberg. Last updated 4 years ago.

7.6 match 2.43 score 27 scripts

bluefoxr

COINr:Composite Indicator Construction and Analysis

A comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.

Maintained by William Becker. Last updated 2 months ago.

2.0 match 26 stars 9.07 score 73 scripts 1 dependents

tjfarrar

skedastic:Handling Heteroskedasticity in the Linear Regression Model

Implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) <doi:10.1007/978-0-387-21606-5>. The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) <doi:10.1080/00031305.2015.1089789>. The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>. Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) <https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm>. A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) <https://projecteuclid.org/euclid.bsmsp/1200512155>, Ramsey's (1969) BAMSET Test <doi:10.1111/j.2517-6161.1969.tb00796.x>, the tests of Bickel (1978) <doi:10.1214/aos/1176344124>, Breusch and Pagan (1979) <doi:10.2307/1911963> with and without the modification proposed by Koenker (1981) <doi:10.1016/0304-4076(81)90062-2>, Carapeto and Holt (2003) <doi:10.1080/0266476022000018475>, Cook and Weisberg (1983) <doi:10.1093/biomet/70.1.1> (including their graphical methods), Diblasi and Bowman (1997) <doi:10.1016/S0167-7152(96)00115-0>, Dufour, Khalaf, Bernard, and Genest (2004) <doi:10.1016/j.jeconom.2003.10.024>, Evans and King (1985) <doi:10.1016/0304-4076(85)90085-5> and Evans and King (1988) <doi:10.1016/0304-4076(88)90006-1>, Glejser (1969) <doi:10.1080/01621459.1969.10500976> as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) <doi:10.1080/07474939908800438>, Goldfeld and Quandt (1965) <doi:10.1080/01621459.1965.10480811>, Harrison and McCabe (1979) <doi:10.1080/01621459.1979.10482544>, Harvey (1976) <doi:10.2307/1913974>, Honda (1989) <doi:10.1111/j.2517-6161.1989.tb01749.x>, Horn (1981) <doi:10.1080/03610928108828074>, Li and Yao (2019) <doi:10.1016/j.ecosta.2018.01.001> with and without the modification of Bai, Pan, and Yin (2016) <doi:10.1007/s11749-017-0575-x>, Rackauskas and Zuokas (2007) <doi:10.1007/s10986-007-0018-6>, Simonoff and Tsai (1994) <doi:10.2307/2986026> with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) <doi:10.1016/S0378-3758(03)00210-6>, Szroeter (1978) <doi:10.2307/1913831>, Verbyla (1993) <doi:10.1111/j.2517-6161.1993.tb01918.x>, White (1980) <doi:10.2307/1912934>, Wilcox and Keselman (2006) <doi:10.1080/10629360500107923>, Yuce (2008) <https://dergipark.org.tr/en/pub/iuekois/issue/8989/112070>, and Zhou, Song, and Thompson (2015) <doi:10.1002/cjs.11252>. Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) <doi:10.1080/01621459.1965.10480851>, the conditional two-sided p-values of Kulinskaya (2008) <arXiv:0810.2124v1>, and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) <doi:10.2307/1912934>, MacKinnon and White (1985) <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004) <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto et al. (2007) <doi:10.1080/03610920601126589>, Cribari-Neto and da Silva (2011) <doi:10.1007/s10182-010-0141-2>, Aftab and Chang (2016) <doi:10.18187/pjsor.v12i2.983>, and Li et al. (2017) <doi:10.1080/00949655.2016.1198906>.

Maintained by Thomas Farrar. Last updated 1 years ago.

3.9 match 7 stars 4.60 score 73 scripts

laboratorio-de-pedometria

pedometrics:Miscellaneous Pedometric Tools

An R implementation of methods employed in the field of pedometrics, soil science discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil using statistical and computational methods. The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/>.

Maintained by Alessandro Samuel-Rosa. Last updated 3 years ago.

analysis plots soil cpp

4.5 match 6 stars 4.00 score 33 scripts

loremerdrignac

EpiReport:Epidemiological Report

Drafting an epidemiological report in 'Microsoft Word' format for a given disease, similar to the Annual Epidemiological Reports published by the European Centre for Disease Prevention and Control. Through standalone functions, it is specifically designed to generate each disease specific output presented in these reports and includes: - Table with the distribution of cases by Member State over the last five years; - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, by month, over the past five years; - Trend plot with the trend and number of cases at the European Union / European Economic Area level, by month, over the past five years; - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level. Two types of datasets can be used: - The default dataset of dengue 2015-2019 data; - Any dataset specified as described in the vignette.

Maintained by Lore Merdrignac. Last updated 4 years ago.

5.4 match 3.32 score 14 scripts

dschulz13

esemifar:Smoothing Long-Memory Time Series

The nonparametric trend and its derivatives in equidistant time series (TS) with long-memory errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. The smoothing methods of the package are described in Letmathe, S., Beran, J. and Feng, Y., (2023) <doi:10.1080/03610926.2023.2276049>.

Maintained by Dominik Schulz. Last updated 10 months ago.

openblas cpp

7.2 match 1 stars 2.48 score 1 scripts 1 dependents

danchaltiel

crosstable:Crosstables for Descriptive Analyses

Create descriptive tables for continuous and categorical variables. Apply summary statistics and counting function, with or without a grouping variable, and create beautiful reports using 'rmarkdown' or 'officer'. You can also compute effect sizes and statistical tests if needed.

Maintained by Dan Chaltiel. Last updated 2 months ago.

descriptive-statistics flextable frequency-table html-report msword officer

1.7 match 116 stars 10.37 score 340 scripts

laresbernardo

lares:Analytics & Machine Learning Sidekick

Auxiliary package for better/faster analytics, visualization, data mining, and machine learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling, Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust results, without the need of repetitive coding or advanced R programming skills.

Maintained by Bernardo Lares. Last updated 24 days ago.

analytics api automation automl data-science descriptive-statistics h2o machine-learning marketing mmm predictive-modeling puzzle rlanguage robyn visualization

1.8 match 233 stars 9.84 score 185 scripts 1 dependents

georgeweigt

itsmr:Time Series Analysis Using the Innovations Algorithm

Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. <https://link.springer.com/book/10.1007/b97391>.

Maintained by George Weigt. Last updated 3 years ago.

7.6 match 2.34 score 218 scripts

lucasvenez

precintcon:Precipitation Intensity, Concentration and Anomaly Analysis

It contains functions to analyze the precipitation intensity, concentration and anomaly.

Maintained by Lucas Venezian Povoa. Last updated 9 years ago.

4.1 match 10 stars 4.28 score 38 scripts

ebirdst:Access and Analyze eBird Status and Trends Data Products

trend:Non-Parametric Trend Tests and Change-Point Detection

trending:Model Temporal Trends

bayesdfa:Bayesian Dynamic Factor Analysis (DFA) with 'Stan'

Rbeast:Bayesian Change-Point Detection and Time Series Decomposition

gtrendsR:Perform and Display Google Trends Queries

wpa:Tools for Analysing and Visualising Viva Insights Data

timetk:A Tool Kit for Working with Time Series

EnvStats:Package for Environmental Statistics, Including US EPA Guidance

fasstr:Analyze, Summarize, and Visualize Daily Streamflow Data

vivainsights:Analyze and Visualize Data from 'Microsoft Viva Insights'

funtimes:Functions for Time Series Analysis

RRphylo:Phylogenetic Ridge Regression Methods for Comparative Studies

EGRET:Exploration and Graphics for RivEr Trends

pastecs:Package for Analysis of Space-Time Ecological Series

edgeR:Empirical Analysis of Digital Gene Expression Data in R

spatialEco:Spatial Analysis and Modelling Utilities

mvgam:Multivariate (Dynamic) Generalized Additive Models

Rlgt:Bayesian Exponential Smoothing Models with Trend Modifications

wql:Exploring Water Quality Monitoring Data

TTR:Technical Trading Rules

GauPro:Gaussian Process Fitting

bsts:Bayesian Structural Time Series

ComBatFamQC:Comprehensive Batch Effect Diagnostics and Harmonization

pspatreg:Spatial and Spatio-Temporal Semiparametric Regression Models with Spatial Lags

poptrend:Estimate Smooth and Linear Trends from Population Count Survey Data

globaltrends:Download and Measure Global Trends Through Google Search Volumes

scran:Methods for Single-Cell RNA-Seq Data Analysis

peco:A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data

openair:Tools for the Analysis of Air Pollution Data

spatial:Functions for Kriging and Point Pattern Analysis

Lahman:Sean 'Lahman' Baseball Database

popbayes:Bayesian Model to Estimate Population Trends from Counts Series

PMCMRplus:Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended

modifiedmk:Modified Versions of Mann Kendall and Spearman's Rho Trend Tests

MortalityTables:A Framework for Various Types of Mortality / Life Tables

siggenes:Multiple Testing using SAM and Efron's Empirical Bayes Approaches

wqtrends:Assess Water Quality Trends with Generalized Additive Models

WRTDStidal:Weighted Regression for Water Quality Evaluation in Tidal Waters

prophet:Automatic Forecasting Procedure

tfprobability:Interface to 'TensorFlow Probability'

forecast:Forecasting Functions for Time Series and Linear Models

Kendall:Kendall Rank Correlation and Mann-Kendall Trend Test

bfast:Breaks for Additive Season and Trend

phytools:Phylogenetic Tools for Comparative Biology (and Other Things)

zyp:Zhang + Yue-Pilon Trends Package

kDGLM:Bayesian Analysis of Dynamic Generalized Linear Models

stlplus:Enhanced Seasonal Decomposition of Time Series by Loess

heatwaveR:Detect Heatwaves and Cold-Spells

EGRETci:Exploration and Graphics for RivEr Trends Confidence Intervals

CorrBin:Nonparametrics with Clustered Binary and Multinomial Data

eatRep:Educational Assessment Tools for Replication Methods

NCC:Simulation and Analysis of Platform Trials with Non-Concurrent Controls

UMI4Cats:UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data

googleVis:R Interface to Google Charts

bboutools:Boreal Caribou Survival, Recruitment and Population Growth

Sleuth3:Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rd Ed)"

tsfeatures:Time Series Feature Extraction

statgenHTP:High Throughput Phenotyping (HTP) Data Analysis

limma:Linear Models for Microarray and Omics Data

smoots:Nonparametric Estimation of the Trend and Its Derivatives in TS

sweep:Tidy Tools for Forecasting

scan:Single-Case Data Analyses for Single and Multiple Baseline Designs

cmsafops:Tools for CM SAF NetCDF Data

ExPanDaR:Explore Your Data Interactively

emmeans:Estimated Marginal Means, aka Least-Squares Means

astsa:Applied Statistical Time Series Analysis

lawstat:Tools for Biostatistics, Public Policy, and Law

Sleuth2:Data Sets from Ramsey and Schafer's "Statistical Sleuth (2nd Ed)"

FLa4a:A Simple and Robust Statistical Catch at Age Model

RtsEva:Performs the Transformed-Stationary Extreme Values Analysis

kayadata:Kaya Identity Data for Nations and Regions

whitebox:'WhiteboxTools' R Frontend

BTtest:Estimate the Number of Factors in Large Nonstationary Datasets

seasonal:R Interface to X-13-ARIMA-SEATS

eatGADS:Data Management of Large Hierarchical Data

EquiTrends:Equivalence Testing for Pre-Trends in Difference-in-Differences Designs

RDS:Respondent-Driven Sampling

csalert:Alerts from Public Health Surveillance Data

L2E:Robust Structured Regression via the L2 Criterion

peco:A Supervised Approach for Predicting cell Cycle Progression using scRNA-seq data