R-universe search: var

bpfaff

vars:VAR Modelling

Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.

Maintained by Bernhard Pfaff. Last updated 1 years ago.

74.6 match 7 stars 8.84 score 2.8k scripts 45 dependents

d-acharya

Dowd:Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd's Book Measuring Market Risk

'Kevin Dowd's' book Measuring Market Risk is a widely read book in the area of risk measurement by students and practitioners alike. As he claims, 'MATLAB' indeed might have been the most suitable language when he originally wrote the functions, but, with growing popularity of R it is not entirely valid. As 'Dowd's' code was not intended to be error free and were mainly for reference, some functions in this package have inherited those errors. An attempt will be made in future releases to identify and correct them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. It should be noted that 'Dowd' offers both 'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more recent version of 'MMR1' toolbox and they both have mostly similar function. The toolbox mainly contains different parametric and non parametric methods for measurement of market risk as well as backtesting risk measurement methods.

Maintained by Dinesh Acharya. Last updated 9 years ago.

126.2 match 3.19 score 154 scripts

skranz

RTutor:Interactive R problem sets with automatic testing of solutions and automatic hints

Interactive R problem sets with automatic testing of solutions and automatic hints

Maintained by Sebastian Kranz. Last updated 1 years ago.

economics learn-to-code problem-set rstudio rtutor shiny teaching

52.5 match 205 stars 5.83 score 111 scripts 1 dependents

svazzole

sparsevar:Sparse VAR/VECM Models Estimation

A wrapper for sparse VAR/VECM time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Sumanta Basu and George Michailidis <doi:10.1214/15-AOS1315>.

Maintained by Simone Vazzoler. Last updated 4 years ago.

econometrics lasso mcp scad sparse statistics time-series var vecm

42.6 match 11 stars 5.69 score 30 scripts 1 dependents

rtsay1

MTS:All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.

Maintained by Ruey S. Tsay. Last updated 3 years ago.

cpp

28.6 match 6 stars 6.52 score 272 scripts 6 dependents

ygeunkim

bvhar:Bayesian Vector Heterogeneous Autoregressive Modeling

Tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

Maintained by Young Geun Kim. Last updated 1 months ago.

bayesian bayesian-econometrics bvar eigen forecasting har pybind11 python rcppeigen time-series vector-autoregression cpp openmp

28.1 match 6 stars 6.35 score 25 scripts

ovvo-financial

NNS:Nonlinear Nonparametric Statistics

Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).

Maintained by Fred Viole. Last updated 4 days ago.

clustering econometrics machine-learning nonlinear nonparametric partial-moments statistics time-series cpp

14.8 match 72 stars 10.77 score 66 scripts 3 dependents

nk027

BVAR:Hierarchical Bayesian Vector Autoregression

Estimation of hierarchical Bayesian vector autoregressive models following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Functions to compute and identify impulse responses, calculate forecasts, forecast error variance decompositions and scenarios are available. Several methods to print, plot and summarise results facilitate analysis.

Maintained by Nikolas Kuschnig. Last updated 5 months ago.

bayesian bvar forecasts impulse-responses vector-autoregressions

21.7 match 51 stars 7.30 score 68 scripts 1 dependents

cran

PairedData:Paired Data Analysis

Many datasets and a set of graphics (based on ggplot2), statistics, effect sizes and hypothesis tests are provided for analysing paired data with S4 class.

Maintained by Stephane Champely. Last updated 7 years ago.

40.8 match 2 stars 3.67 score 4 dependents

cran

compositions:Compositional Data Analysis

Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.

Maintained by K. Gerald van den Boogaart. Last updated 1 years ago.

openblas

21.3 match 1 stars 6.35 score 36 dependents

jh8080

VAR.etp:VAR Modelling: Estimation, Testing, and Prediction

A collection of the functions for estimation, hypothesis testing, prediction for stationary vector autoregressive models.

Maintained by Jae H. Kim. Last updated 2 years ago.

88.5 match 1.52 score 33 scripts

ineswilms

bigtime:Sparse Estimation of Large Time Series Models

Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.

Maintained by Ines Wilms. Last updated 2 years ago.

openblas cpp

26.3 match 30 stars 4.94 score 29 scripts

matthieustigler

tsDyn:Nonlinear Time Series Models with Regime Switching

Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).

Maintained by Matthieu Stigler. Last updated 5 months ago.

12.1 match 34 stars 10.53 score 684 scripts 3 dependents

skranz

gtree:gtree basic functionality to model and solve games

gtree basic functionality to model and solve games

Maintained by Sebastian Kranz. Last updated 4 years ago.

economic-experiments economics gambit game-theory nash-equilibrium

32.6 match 18 stars 3.79 score 23 scripts 1 dependents

mjwoods

RNetCDF:Interface to 'NetCDF' Datasets

An interface to the 'NetCDF' file formats designed by Unidata for efficient storage of array-oriented scientific data and descriptions. Most capabilities of 'NetCDF' version 4 are supported. Optional conversions of time units are enabled by 'UDUNITS' version 2, also from Unidata.

Maintained by Milton Woods. Last updated 23 days ago.

udunits netcdf

12.0 match 24 stars 10.26 score 540 scripts 23 dependents

braverock

PerformanceAnalytics:Econometric Tools for Performance and Risk Analysis

Collection of econometric functions for performance and risk analysis. In addition to standard risk and performance metrics, this package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible.

Maintained by Brian G. Peterson. Last updated 4 months ago.

6.7 match 222 stars 15.93 score 4.8k scripts 20 dependents

rpolars

polars:Lightning-Fast 'DataFrame' Library

Lightning-fast 'DataFrame' library written in 'Rust'. Convert R data to 'Polars' data and vice versa. Perform fast, lazy, larger-than-memory and optimized data queries. 'Polars' is interoperable with the package 'arrow', as both are based on the 'Apache Arrow' Columnar Format.

Maintained by Soren Welling. Last updated 13 days ago.

arrow polars rust

8.9 match 501 stars 12.01 score 1.0k scripts 2 dependents

tidyverse

ggplot2:Create Elegant Data Visualisations Using the Grammar of Graphics

A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

Maintained by Thomas Lin Pedersen. Last updated 7 days ago.

data-visualisation visualisation

4.2 match 6.6k stars 25.10 score 645k scripts 7.6k dependents

franzmohr

bvartools:Bayesian Inference of Vector Autoregressive and Error Correction Models

Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).

Maintained by Franz X. Mohr. Last updated 1 years ago.

bayesian bayesian-inference bayesian-var bvar bvecm gibbs-sampling mcmc vector-autoregression vector-error-correction-model openblas cpp

15.2 match 31 stars 6.80 score 34 scripts 1 dependents

tidyverts

fable:Forecasting Models for Tidy Time Series

Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.

Maintained by Mitchell OHara-Wild. Last updated 4 months ago.

forecasting cpp

7.6 match 569 stars 13.54 score 2.1k scripts 6 dependents

rdatatable

data.table:Extension of `data.frame`

Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.

Maintained by Tyson Barrett. Last updated 5 days ago.

4.1 match 3.7k stars 23.51 score 230k scripts 4.6k dependents

bioc

ISAnalytics:Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies

In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites (IS), essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo. This package is aimed at (1) supporting automation of IS workflow, (2) performing base and advance analysis for IS tracking (clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc.), (3) providing basic biology insights of transduced stem cells in vivo.

Maintained by Francesco Gazzo. Last updated 4 months ago.

biomedicalinformatics sequencing singlecell

16.4 match 3 stars 5.83 score 15 scripts

flr

FLCore:Core Package of FLR, Fisheries Modelling in R

Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.

Maintained by Iago Mosqueira. Last updated 12 days ago.

fisheries flr fisheries-modelling

10.8 match 16 stars 8.78 score 956 scripts 23 dependents

donaldrwilliams

BGGM:Bayesian Gaussian Graphical Models

Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Maintained by Philippe Rast. Last updated 3 months ago.

bayes-factors bayesian-hypothesis-testing gaussian-graphical-models openblas cpp openmp

9.7 match 55 stars 9.61 score 102 scripts 1 dependents

skembel

picante:Integrating Phylogenies and Ecology

Functions for phylocom integration, community analyses, null-models, traits and evolution. Implements numerous ecophylogenetic approaches including measures of community phylogenetic and trait diversity, phylogenetic signal, estimation of trait values for unobserved taxa, null models for community and phylogeny randomizations, and utility functions for data input/output and phylogeny plotting. A full description of package functionality and methods are provided by Kembel et al. (2010) <doi:10.1093/bioinformatics/btq166>.

Maintained by Steven W. Kembel. Last updated 2 years ago.

8.1 match 34 stars 11.42 score 1.1k scripts 16 dependents

dcousin3

superb:Summary Plots with Adjusted Error Bars

Computes standard error and confidence interval of various descriptive statistics under various designs and sampling schemes. The main function, superb(), return a plot. It can also be used to obtain a dataframe with the statistics and their precision intervals so that other plotting environments (e.g., Excel) can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005) <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012) <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>, Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding <doi:10.20982/tqmp.15.1.p001> for specific references.

Maintained by Denis Cousineau. Last updated 2 months ago.

error-bars plotting statistics summary-plots summary-statistics visualization

9.0 match 19 stars 9.53 score 155 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 2 days 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

8.5 match 148 stars 9.92 score 117 scripts

harrelfe

Hmisc:Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Maintained by Frank E Harrell Jr. Last updated 7 days ago.

fortran

4.5 match 209 stars 17.64 score 17k scripts 750 dependents

ecor

RMAWGEN:Multi-Site Auto-Regressive Weather GENerator

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

Maintained by Emanuele Cordano. Last updated 1 months ago.

14.0 match 3 stars 5.62 score 115 scripts 4 dependents

tidymodels

infer:Tidy Statistical Inference

The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework.

Maintained by Simon Couch. Last updated 6 months ago.

5.0 match 736 stars 15.75 score 3.5k scripts 18 dependents

tidyverse

dplyr:A Grammar of Data Manipulation

A fast, consistent tool for working with data frame like objects, both in memory and out of memory.

Maintained by Hadley Wickham. Last updated 28 days ago.

data-manipulation grammar cpp

3.0 match 4.8k stars 24.68 score 659k scripts 7.8k dependents

cran

VAR.spec:Allows Specifying a Bivariate VAR (Vector Autoregression) with Desired Spectral Characteristics

The spectral characteristics of a bivariate series (Marginal Spectra, Coherency- and Phase-Spectrum) determine whether there is a strong presence of short-, medium-, or long-term fluctuations (components of certain frequencies in the spectral representation of the series) in each one of them. These are induced by strong peaks of the marginal spectra of each series at the corresponding frequencies. The spectral characteristics also determine how strongly these short-, medium-, or long-term fluctuations of the two series are correlated between the two series. Information on this is provided by the Coherency spectrum at the corresponding frequencies. Finally, certain fluctuations of the two series may be lagged to each other. Information on this is provided by the Phase spectrum at the corresponding frequencies. The idea in this package is to define a VAR (Vector autoregression) model with desired spectral characteristics by specifying a number of polynomials, required to define the VAR. See Ioannidis(2007) <doi:10.1016/j.jspi.2005.12.013>. These are specified via their roots, instead of via their coefficients. This is an idea borrowed from the Time Series Library of R. Dahlhaus, where it is used for defining ARMA models for univariate time series. This way, one may e.g. specify a VAR inducing a strong presence of long-term fluctuations in series 1 and in series 2, which are weakly correlated, but lagged by a number of time units to each other, while short-term fluctuations in series 1 and in series 2, are strongly present only in one of the two series, while they are strongly correlated to each other between the two series. Simulation from such models allows studying the behavior of data-analysis tools, such as estimation of the spectra, under different circumstances, as e.g. peaks in the spectra, generating bias, induced by leakage.

Maintained by Evangelos Ioannidis. Last updated 10 months ago.

68.7 match 1.00 score

hadley

reshape:Flexibly Reshape Data

Flexibly restructure and aggregate data using just two functions: melt and cast.

Maintained by Hadley Wickham. Last updated 3 years ago.

6.8 match 9.86 score 21k scripts 232 dependents

moviedo5

fda.usc:Functional Data Analysis and Utilities for Statistical Computing

Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.

Maintained by Manuel Oviedo de la Fuente. Last updated 5 months ago.

functional-data-analysis fortran

6.8 match 12 stars 9.72 score 560 scripts 22 dependents

bsvars

bsvars:Bayesian Estimation of Structural Vector Autoregressive Models

Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>. The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset.

Maintained by Tomasz Woźniak. Last updated 1 days ago.

bayesian-inference econometrics vector-autoregression openblas cpp openmp

8.5 match 47 stars 7.68 score 32 scripts 1 dependents

martinbladt

matrixdist:Statistics for Matrix Distributions

Tools for phase-type distributions including the following variants: continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. Methods for functional evaluation, simulation and estimation using the expectation-maximization (EM) algorithm are provided for all models. The methods of this package are based on the following references. Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm, Olsson, M. (1996). Estimation of phase-type distributions from censored data, Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>, Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>, Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>, Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>, Bladt, M. (2022) <doi:10.1017/asb.2021.40>, Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>, Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>, Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.

Maintained by Martin Bladt. Last updated 1 months ago.

openblas cpp openmp

15.9 match 1 stars 4.00 score 2 scripts

vigou3

actuar:Actuarial Functions and Heavy Tailed Distributions

Functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. Support for many additional probability distributions to model insurance loss size and frequency: 23 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. Support for phase-type distributions commonly used to compute ruin probabilities. Main reference: <doi:10.18637/jss.v025.i07>. Implementation of the Feller-Pareto family of distributions: <doi:10.18637/jss.v103.i06>.

Maintained by Vincent Goulet. Last updated 3 months ago.

openblas

6.6 match 12 stars 9.44 score 732 scripts 35 dependents

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 2 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

4.6 match 872 stars 13.34 score 5.2k scripts

r-gregmisc

gdata:Various R Programming Tools for Data Manipulation

Various R programming tools for data manipulation, including medical unit conversions, combining objects, character vector operations, factor manipulation, obtaining information about R objects, generating fixed-width format files, extracting components of date & time objects, operations on columns of data frames, matrix operations, operations on vectors, operations on data frames, value of last evaluated expression, and a resample() wrapper for sample() that ensures consistent behavior for both scalar and vector arguments.

Maintained by Arni Magnusson. Last updated 3 months ago.

4.5 match 9 stars 13.62 score 4.5k scripts 124 dependents

luisgruber

bayesianVARs:MCMC Estimation of Bayesian Vectorautoregressions

Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber & Kastner (2023) <doi:10.48550/arXiv.2206.04902>. Efficient equation-per-equation estimation following Kastner & Huber (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021) <doi:10.1016/j.jeconom.2021.11.010>.

Maintained by Luis Gruber. Last updated 5 months ago.

bayesian time-series vectorautoregression openblas cpp

11.2 match 9 stars 5.43 score 9 scripts

daijiang

phyr:Model Based Phylogenetic Analysis

A collection of functions to do model-based phylogenetic analysis. It includes functions to calculate community phylogenetic diversity, to estimate correlations among functional traits while accounting for phylogenetic relationships, and to fit phylogenetic generalized linear mixed models. The Bayesian phylogenetic generalized linear mixed models are fitted with the 'INLA' package (<https://www.r-inla.org>).

Maintained by Daijiang Li. Last updated 1 years ago.

bayesian glmm inla phylogeny species-distribution-modeling openblas cpp

7.0 match 31 stars 8.71 score 107 scripts 2 dependents

decisionpatterns

formula.tools:Programmatic Utilities for Manipulating Formulas, Expressions, Calls, Assignments and Other R Objects

These utilities facilitate the programmatic manipulations of formulas, expressions, calls, assignments and other R language objects. These objects all share the same structure: a left-hand side, operator and right-hand side. This packages provides methods for accessing and modifying this structures as well as extracting and replacing names and symbols from these objects.

Maintained by Christopher Brown. Last updated 7 years ago.

6.8 match 17 stars 8.89 score 236 scripts 79 dependents

tylerjpike

sovereign:State-Dependent Empirical Analysis

A set of tools for state-dependent empirical analysis through both VAR- and local projection-based state-dependent forecasts, impulse response functions, historical decompositions, and forecast error variance decompositions.

Maintained by Tyler J. Pike. Last updated 2 years ago.

econometrics forecasting impulse-response local-projection macroeconomics state-dependent time-series vector-autoregression

12.3 match 12 stars 4.78 score 8 scripts

open-eo

openeo:Client Interface for 'openEO' Servers

Access data and processing functionalities of 'openEO' compliant back-ends in R.

Maintained by Florian Lahn. Last updated 2 months ago.

openeo openeo-user

6.8 match 65 stars 8.65 score 128 scripts

jcfaria

fdth:Frequency Distribution Tables, Histograms and Polygons

Perform frequency distribution tables, associated histograms and polygons from vector, data.frame and matrix objects for numerical and categorical variables.

Maintained by José C. Faria. Last updated 1 years ago.

9.8 match 2 stars 5.93 score 107 scripts

knausb

vcfR:Manipulate and Visualize VCF Data

Facilitates easy manipulation of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices of data. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file (*.vcf.gz). It also may be converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and familiar R software.

Maintained by Brian J. Knaus. Last updated 1 months ago.

genomics population-genetics population-genomics rcpp vcf-data visualization zlib cpp

4.3 match 256 stars 13.66 score 3.1k scripts 19 dependents

gleon

LakeMetabolizer:Tools for the Analysis of Ecosystem Metabolism

A collection of tools for the calculation of freewater metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models).

Maintained by Jake Zwart. Last updated 2 years ago.

9.8 match 18 stars 5.94 score 122 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 12 days ago.

3.0 match 379 stars 19.21 score 13k scripts 188 dependents

wbnicholson

BigVAR:Dimension Reduction Methods for Multivariate Time Series

Estimates VAR and VARX models with Structured Penalties.

Maintained by Will Nicholson. Last updated 6 months ago.

openblas cpp

7.9 match 58 stars 7.24 score 100 scripts 1 dependents

chr1swallace

coloc:Colocalisation Tests of Two Genetic Traits

Performs the colocalisation tests described in Giambartolomei et al (2013) <doi:10.1371/journal.pgen.1004383>, Wallace (2020) <doi:10.1371/journal.pgen.1008720>, Wallace (2021) <doi:10.1371/journal.pgen.1009440>, Pullin and Wallace (2025) <doi:10.1101/2024.08.21.608957>.

Maintained by Chris Wallace. Last updated 4 days ago.

4.5 match 164 stars 12.68 score 916 scripts 3 dependents

cran

circular:Circular Statistics

Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.

Maintained by Eduardo García-Portugués. Last updated 7 months ago.

fortran

9.8 match 7 stars 5.71 score 40 dependents

sinhrks

ggfortify:Data Visualization Tools for Statistical Analysis Results

Unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using 'ggplot2'.

Maintained by Yuan Tang. Last updated 9 months ago.

3.8 match 528 stars 14.60 score 9.1k scripts 24 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 4 days ago.

fortran cpp

3.3 match 86 stars 16.73 score 7.7k scripts 101 dependents

rstudio

gt:Easily Create Presentation-Ready Display Tables

Build display tables from tabular data with an easy-to-use set of functions. With its progressive approach, we can construct display tables with a cohesive set of table parts. Table values can be formatted using any of the included formatting functions. Footnotes and cell styles can be precisely added through a location targeting system. The way in which 'gt' handles things for you means that you don't often have to worry about the fine details.

Maintained by Richard Iannone. Last updated 27 days ago.

docx easy-to-use html latex rtf summary-tables

3.0 match 2.1k stars 18.36 score 20k scripts 112 dependents

maximeherve

RVAideMemoire:Testing and Plotting Procedures for Biostatistics

Contains miscellaneous functions useful in biostatistics, mostly univariate and multivariate testing procedures with a special emphasis on permutation tests. Many functions intend to simplify user's life by shortening existing procedures or by implementing plotting functions that can be used with as many methods from different packages as possible.

Maintained by Maxime HERVE. Last updated 1 years ago.

10.4 match 8 stars 5.28 score 632 scripts

mlverse

torch:Tensors and Neural Networks with 'GPU' Acceleration

Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.

Maintained by Daniel Falbel. Last updated 6 days ago.

autograd deep-learning torch cpp

3.3 match 521 stars 16.50 score 1.4k scripts 39 dependents

cran

mgcv:Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.

Maintained by Simon Wood. Last updated 1 years ago.

openblas openmp

4.3 match 32 stars 12.71 score 17k scripts 7.8k dependents

namgillee

VARshrink:Shrinkage Estimation Methods for Vector Autoregressive Models

Vector autoregressive (VAR) model is a fundamental and effective approach for multivariate time series analysis. Shrinkage estimation methods can be applied to high-dimensional VAR models with dimensionality greater than the number of observations, contrary to the standard ordinary least squares method. This package is an integrative package delivering nonparametric, parametric, and semiparametric methods in a unified and consistent manner, such as the multivariate ridge regression in Golub, Heath, and Wahba (1979) <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and Bayesian estimation methods using noninformative and informative priors in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and Ni and Sun (2005) <doi:10.1198/073500104000000622>.

Maintained by Namgil Lee. Last updated 5 years ago.

17.0 match 3 stars 3.18 score 6 scripts

stan-dev

posterior:Tools for Working with Posterior Distributions

Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) <doi:10.1214/20-BA1221>.

Maintained by Paul-Christian Bürkner. Last updated 10 hours ago.

bayes bayesian mcmc

3.3 match 168 stars 16.22 score 3.3k scripts 347 dependents

bioc

IRanges:Foundation of integer range manipulation in Bioconductor

Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames.

Maintained by Hervé Pagès. Last updated 2 months ago.

infrastructure datarepresentation bioconductor-package core-package

3.3 match 22 stars 16.09 score 2.1k scripts 1.8k dependents

bioc

S4Vectors:Foundation of vector-like and list-like containers in Bioconductor

The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, Factor, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).

Maintained by Hervé Pagès. Last updated 2 months ago.

infrastructure datarepresentation bioconductor-package core-package

3.3 match 18 stars 16.05 score 1.0k scripts 1.9k dependents

eldafani

intsvy:International Assessment Data Manager

Provides tools for importing, merging, and analysing data from international assessment studies (TIMSS, PIRLS, PISA, ICILS, and PIAAC).

Maintained by Daniel Caro. Last updated 1 years ago.

10.0 match 22 stars 5.29 score 88 scripts

jmbh

mgm:Estimating Time-Varying k-Order Mixed Graphical Models

Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.

Maintained by Jonas Haslbeck. Last updated 21 days ago.

6.4 match 29 stars 8.16 score 125 scripts 6 dependents

ddsjoberg

gtsummary:Presentation-Ready Data Summary and Analytic Result Tables

Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.

Maintained by Daniel D. Sjoberg. Last updated 7 days ago.

easy-to-use gt html5 regression-models reproducibility reproducible-research statistics summary-statistics summary-tables table1 tableone

3.0 match 1.1k stars 17.02 score 8.2k scripts 15 dependents

haeran-cho

fnets:Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

Implements methods for network estimation and forecasting of high-dimensional time series exhibiting strong serial and cross-sectional correlations under a factor-adjusted vector autoregressive model. See Barigozzi, Cho and Owens (2024) <doi:10.1080/07350015.2023.2257270> for further descriptions of FNETS methodology and Owens, Cho and Barigozzi (2024) <arXiv:2301.11675> accompanying the R package.

Maintained by Haeran Cho. Last updated 4 months ago.

factor-models forecasting high-dimensional network-estimation time-series vector-autoregression cpp

9.7 match 7 stars 5.20 score 28 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 5 days ago.

average-information mixed-models rcpparmadillo openblas cpp openmp

4.0 match 44 stars 12.63 score 300 scripts 10 dependents

distancedevelopment

dsm:Density Surface Modelling of Distance Sampling Data

Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.

Maintained by Laura Marshall. Last updated 2 years ago.

8.3 match 8 stars 6.09 score 146 scripts

jamiemkass

ENMeval:Automated Tuning and Evaluations of Ecological Niche Models

Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.

Maintained by Jamie M. Kass. Last updated 3 days ago.

4.5 match 49 stars 11.16 score 332 scripts 2 dependents

xrobin

pROC:Display and Analyze ROC Curves

Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

Maintained by Xavier Robin. Last updated 5 months ago.

bootstrapping covariance hypothesis-testing machine-learning plot plotting roc roc-curve variance cpp

3.3 match 125 stars 15.18 score 16k scripts 445 dependents

stan-dev

bayesplot:Plotting for Bayesian Models

Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'.

Maintained by Jonah Gabry. Last updated 2 months ago.

bayesian ggplot2 mcmc pandoc stan statistical-graphics visualization

3.0 match 436 stars 16.69 score 6.5k scripts 98 dependents

beerda

lfl:Linguistic Fuzzy Logic

Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association rules, composition of fuzzy relations, performing perception-based logical deduction (PbLD), and forecasting time-series using fuzzy rule-based ensemble (FRBE). The package also contains basic fuzzy-related algebraic functions capable of handling missing values in different styles (Bochvar, Sobocinski, Kleene etc.), computation of Sugeno integrals and fuzzy transform.

Maintained by Michal Burda. Last updated 5 months ago.

association-rules forecast-model fuzzy-logic inference-rules cpp openmp

9.2 match 8 stars 5.35 score 28 scripts

cran

bayesm:Bayesian Inference for Marketing/Micro-Econometrics

Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley first edition 2005 and second forthcoming) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014).

Maintained by Peter Rossi. Last updated 2 years ago.

openblas cpp

7.1 match 20 stars 6.74 score 44 dependents

statnet

statnet.common:Common R Scripts and Utilities Used by the Statnet Project Software

Non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.

Maintained by Pavel N. Krivitsky. Last updated 1 months ago.

4.3 match 8 stars 11.32 score 197 scripts 148 dependents

pecanproject

PEcAn.DB:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

4.0 match 216 stars 11.90 score 127 scripts 27 dependents

tanaylab

misha:Toolkit for Analysis of Genomic Data

A toolkit for analysis of genomic data. The 'misha' package implements an efficient data structure for storing genomic data, and provides a set of functions for data extraction, manipulation and analysis. Some of the 2D genome algorithms were described in Yaffe and Tanay (2011) <doi:10.1038/ng.947>.

Maintained by Aviezer Lifshitz. Last updated 2 days ago.

genomic-data-analysis cpp

8.0 match 4 stars 5.90 score

bioc

BiocGenerics:S4 generic functions used in Bioconductor

The package defines many S4 generic functions used in Bioconductor.

Maintained by Hervé Pagès. Last updated 2 months ago.

infrastructure bioconductor-package core-package

3.3 match 12 stars 14.22 score 612 scripts 2.2k dependents

mariushofert

qrmtools:Tools for Quantitative Risk Management

Functions and data sets for reproducing selected results from the book "Quantitative Risk Management: Concepts, Techniques and Tools". Furthermore, new developments and auxiliary functions for Quantitative Risk Management practice.

Maintained by Marius Hofert. Last updated 1 years ago.

12.1 match 1 stars 3.85 score 237 scripts

pacificclimate

ncdf4.helpers:Helper Functions for Use with the 'ncdf4' Package

Contains a collection of helper functions for dealing with 'NetCDF' files <https://www.unidata.ucar.edu/software/netcdf/> opened using 'ncdf4', particularly 'NetCDF' files that conform to the Climate and Forecast (CF) Metadata Conventions <http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html>.

Maintained by Lee Zeman. Last updated 14 days ago.

7.1 match 5 stars 6.55 score 236 scripts 1 dependents

pecanproject

PEcAn.ED2:PEcAn Package for Integration of ED2 Model

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This package provides functions to link the Ecosystem Demography Model, version 2, to PEcAn.

Maintained by Mike Dietze. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

5.3 match 216 stars 8.74 score 145 scripts

geobosh

cvar:Compute Expected Shortfall and Value at Risk for Continuous Distributions

Compute expected shortfall (ES) and Value at Risk (VaR) from a quantile function, distribution function, random number generator or probability density function. ES is also known as Conditional Value at Risk (CVaR). Virtually any continuous distribution can be specified. The functions are vectorized over the arguments. The computations are done directly from the definitions, see e.g. Acerbi and Tasche (2002) <doi:10.1111/1468-0300.00091>. Some support for GARCH models is provided, as well.

Maintained by Georgi N. Boshnakov. Last updated 2 years ago.

expected-shortfall locations-scale-transformations quantile quantile-functions risk value-at-risk

5.7 match 6 stars 8.05 score 27 scripts 52 dependents

tomasfryda

h2o:R Interface for the 'H2O' Scalable Machine Learning Platform

R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

Maintained by Tomas Fryda. Last updated 1 years ago.

5.6 match 3 stars 8.20 score 7.8k scripts 11 dependents

fbartos

BayesTools:Tools for Bayesian Analyses

Provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating 'JAGS' and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.

Maintained by František Bartoš. Last updated 2 months ago.

bayesian model-averaging

7.4 match 7 stars 6.06 score 17 scripts 3 dependents

declaredesign

DeclareDesign:Declare and Diagnose Research Designs

Researchers can characterize and learn about the properties of research designs before implementation using `DeclareDesign`. Ex ante declaration and diagnosis of designs can help researchers clarify the strengths and limitations of their designs and to improve their properties, and can help readers evaluate a research strategy prior to implementation and without access to results. It can also make it easier for designs to be shared, replicated, and critiqued.

Maintained by Graeme Blair. Last updated 2 months ago.

design research simulations

5.3 match 101 stars 8.42 score 398 scripts 1 dependents

vincentarelbundock

modelsummary:Summary Tables and Plots for Statistical Models and Data: Beautiful, Customizable, and Publication-Ready

Create beautiful and customizable tables to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and correlation matrices. This package supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr' dynamic documents. Details can be found in Arel-Bundock (2022) <doi:10.18637/jss.v103.i01>.

Maintained by Vincent Arel-Bundock. Last updated 1 months ago.

3.3 match 927 stars 13.39 score 6.2k scripts 2 dependents

projectmosaic

mosaic:Project MOSAIC Statistics and Mathematics Teaching Utilities

Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.

Maintained by Randall Pruim. Last updated 1 years ago.

3.3 match 93 stars 13.32 score 7.2k scripts 7 dependents

tanaylab

naryn:Native Access Medical Record Retriever for High Yield Analytics

A toolkit for medical records data analysis. The 'naryn' package implements an efficient data structure for storing medical records, and provides a set of functions for data extraction, manipulation and analysis.

Maintained by Aviezer Lifshitz. Last updated 15 days ago.

data-analysis medical-records cpp

8.0 match 3 stars 5.38 score 4 scripts

kogalur

randomForestSRC:Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)

Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes. Extreme random forests and randomized splitting. Suite of imputation methods for missing data. Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance. Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.

Maintained by Udaya B. Kogalur. Last updated 4 days ago.

openmp

4.3 match 124 stars 10.10 score 1.2k scripts 11 dependents

cran

corpcor:Efficient Estimation of Covariance and (Partial) Correlation

Implements a James-Stein-type shrinkage estimator for the covariance matrix, with separate shrinkage for variances and correlations. The details of the method are explained in Schafer and Strimmer (2005) <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) <DOI:10.2202/1544-6115.1252>. The approach is both computationally as well as statistically very efficient, it is applicable to "small n, large p" data, and always returns a positive definite and well-conditioned covariance matrix. In addition to inferring the covariance matrix the package also provides shrinkage estimators for partial correlations and partial variances. The inverse of the covariance and correlation matrix can be efficiently computed, as well as any arbitrary power of the shrinkage correlation matrix. Furthermore, functions are available for fast singular value decomposition, for computing the pseudoinverse, and for checking the rank and positive definiteness of a matrix.

Maintained by Korbinian Strimmer. Last updated 4 years ago.

6.5 match 6.57 score 310 dependents

rapporter

rapportools:Miscellaneous (Stats) Helper Functions with Sane Defaults for Reporting

Helper functions that act as wrappers to more advanced statistical methods with the advantage of having sane defaults for quick reporting.

Maintained by Gergely Daróczi. Last updated 1 months ago.

5.6 match 8 stars 7.65 score 186 scripts 11 dependents

rfastofficial

Rfast:A Collection of Efficient and Extremely Fast R Functions

A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>. c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. <doi:10.48550/arXiv.1806.10947>. d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. <doi:10.3390/computation12090185>. e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. <doi:10.48550/arXiv.2501.02849>.

Maintained by Manos Papadakis. Last updated 1 months ago.

openblas cpp openmp

3.3 match 147 stars 12.67 score 1.2k scripts 174 dependents

gergness

srvyr:'dplyr'-Like Syntax for Summary Statistics of Survey Data

Use piping, verbs like 'group_by' and 'summarize', and other 'dplyr' inspired syntactic style when calculating summary statistics on survey data using functions from the 'survey' package.

Maintained by Greg Freedman Ellis. Last updated 2 months ago.

survey

3.0 match 215 stars 13.88 score 1.8k scripts 15 dependents

bioc

SparseArray:High-performance sparse data representation and manipulation in R

The SparseArray package provides array-like containers for efficient in-memory representation of multidimensional sparse data in R (arrays and matrices). The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data: the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN.

Maintained by Hervé Pagès. Last updated 13 days ago.

infrastructure datarepresentation bioconductor-package core-package openmp

3.3 match 9 stars 12.47 score 79 scripts 1.2k dependents

melff

memisc:Management of Survey Data and Presentation of Analysis Results

An infrastructure for the management of survey data including value labels, definable missing values, recoding of variables, production of code books, and import of (subsets of) 'SPSS' and 'Stata' files is provided. Further, the package allows to produce tables and data frames of arbitrary descriptive statistics and (almost) publication-ready tables of regression model estimates, which can be exported to 'LaTeX' and HTML.

Maintained by Martin Elff. Last updated 27 days ago.

survey-data

3.3 match 46 stars 12.34 score 1.2k scripts 13 dependents

repboxr

repboxReg:Repbox module for analysing regressions

Repbox module for analysing regressions

Maintained by Sebastian Kranz. Last updated 2 months ago.

10.9 match 3.71 score 6 scripts 2 dependents

kkholst

lava:Latent Variable Models

A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.

Maintained by Klaus K. Holst. Last updated 3 months ago.

latent-variable-models simulation statistics structural-equation-models

3.0 match 33 stars 12.87 score 610 scripts 478 dependents

doccstat

fastcpd:Fast Change Point Detection via Sequential Gradient Descent

Implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function.

Maintained by Xingchi Li. Last updated 1 days ago.

change-point-detection cpp custom-function gradient-descent lasso linear-regression logistic-regression offline pelt penalized-regression poisson-regression quasi-newton statistics time-series warm-start fortran openblas cpp openmp

5.5 match 21 stars 6.98 score 7 scripts

bioboot

bio3d:Biological Structure Analysis

Utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data. Please refer to the URLs below for more information.

Maintained by Barry Grant. Last updated 5 months ago.

zlib cpp

4.5 match 5 stars 8.47 score 1.4k scripts 10 dependents

pecanproject

PEcAn.uncertainty:PEcAn Functions Used for Propagating and Partitioning Uncertainties in Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by David LeBauer. Last updated 4 hours ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

4.3 match 216 stars 8.94 score 15 scripts 5 dependents

jcrodriguez1989

rco:The R Code Optimizer

Automatically apply different strategies to optimize R code. 'rco' functions take R code as input, and returns R code as output.

Maintained by Juan Cruz Rodriguez. Last updated 5 months ago.

compiler fast gcc hpc optimization optimizer

5.5 match 82 stars 6.73 score

airpino

HistDAWass:Histogram-Valued Data Analysis

In the framework of Symbolic Data Analysis, a relatively new approach to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e., data described by univariate histograms. The methods and the basic statistics for histogram-valued data are mainly based on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric between quantile functions. The package contains unsupervised classification techniques, least square regression and tools for histogram-valued data and for histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.

Maintained by Antonio Irpino. Last updated 1 years ago.

openblas cpp openmp

7.8 match 5 stars 4.75 score 75 scripts

tidyverts

fabletools:Core Tools for Packages in the 'fable' Framework

Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.

Maintained by Mitchell OHara-Wild. Last updated 2 months ago.

3.0 match 91 stars 12.18 score 396 scripts 18 dependents

bioc

glmGamPoi:Fit a Gamma-Poisson Generalized Linear Model

Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.

Maintained by Constantin Ahlmann-Eltze. Last updated 15 days ago.

regression rnaseq software singlecell gamma-poisson glm negative-binomial-regression on-disk openblas cpp

3.0 match 111 stars 12.16 score 1.0k scripts 4 dependents

bioc

S4Arrays:Foundation of array-like containers in Bioconductor

The S4Arrays package defines the Array virtual class to be extended by other S4 classes that wish to implement a container with an array-like semantic. It also provides: (1) low-level functionality meant to help the developer of such container to implement basic operations like display, subsetting, or coercion of their array-like objects to an ordinary matrix or array, and (2) a framework that facilitates block processing of array-like objects (typically on-disk objects).

Maintained by Hervé Pagès. Last updated 2 months ago.

infrastructure datarepresentation bioconductor-package core-package

3.3 match 5 stars 10.99 score 8 scripts 1.2k dependents

rubensmoura87

MultiATSM:Multicountry Term Structure of Interest Rates Models

Estimation routines for several classes of affine term structure of interest rates models. All the models are based on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014, JF) <doi:10.1111/jofi.12131>. Multicountry extensions such as the ones of Jotikasthira, Le, and Lundblad (2015, JFE) <doi:10.1016/j.jfineco.2014.09.004>, Candelon and Moura (2023, EM) <doi:10.1016/j.econmod.2023.106453>, and Candelon and Moura (Forthcoming, JFEC) <doi:10.1093/jjfinec/nbae008> are also available.

Maintained by Rubens Moura. Last updated 8 days ago.

9.0 match 4.00 score 8 scripts

cran

ftsa:Functional Time Series Analysis

Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.

Maintained by Han Lin Shang. Last updated 1 months ago.

7.8 match 6 stars 4.61 score 10 dependents

bcallaway11

BMisc:Miscellaneous Functions for Panel Data, Quantiles, and Printing Results

These are miscellaneous functions for working with panel data, quantiles, and printing results. For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance. For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions. Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas.

Maintained by Brantly Callaway. Last updated 2 months ago.

cpp

4.5 match 7 stars 7.92 score 110 scripts 8 dependents

jthomasmock

gtExtras:Extending 'gt' for Beautiful HTML Tables

Provides additional functions for creating beautiful tables with 'gt'. The functions are generally wrappers around boilerplate or adding opinionated niche capabilities and helpers functions.

Maintained by Thomas Mock. Last updated 12 months ago.

data-science data-visualization datascience ggplot2 gt plots sparkline sparkline-graphs sparklines tables

3.0 match 201 stars 11.66 score 2.4k scripts 5 dependents

larmarange

broom.helpers:Helpers for Model Coefficients Tibbles

Provides suite of functions to work with regression model 'broom::tidy()' tibbles. The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more.

Maintained by Joseph Larmarange. Last updated 25 days ago.

3.0 match 22 stars 11.45 score 165 scripts 2 dependents

insightsengineering

cards:Analysis Results Data

Construct CDISC (Clinical Data Interchange Standards Consortium) compliant Analysis Results Data objects. These objects are used and re-used to construct summary tables, visualizations, and written reports. The package also exports utilities for working with these objects and creating new Analysis Results Data objects.

Maintained by Daniel D. Sjoberg. Last updated 1 months ago.

analysis cdisc dataset

3.0 match 40 stars 11.40 score 100 scripts 19 dependents

gsucarrat

gets:General-to-Specific (GETS) Modelling and Indicator Saturation Methods

Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03> for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) <doi:10.32614/RJ-2021-024>.

Maintained by Genaro Sucarrat. Last updated 8 months ago.

5.0 match 9 stars 6.79 score 73 scripts 3 dependents

rpatin

segclust2d:Bivariate Segmentation/Clustering Methods and Tools

Provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'. The segmentation method is a bivariate extension of Lavielle's method available in 'adehabitatLT' (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method (formerly available in 'cghseg' package) to the bivariate case. The method is fully described in Patin et al (2018) <doi:10.1101/444794>.

Maintained by Remi Patin. Last updated 11 months ago.

cpp

5.6 match 7 stars 5.98 score 30 scripts 1 dependents

bpfaff

urca:Unit Root and Cointegration Tests for Time Series Data

Unit root and cointegration tests encountered in applied econometric analysis are implemented.

Maintained by Bernhard Pfaff. Last updated 10 months ago.

fortran

3.8 match 6 stars 8.95 score 1.4k scripts 270 dependents

treynkens

ReIns:Functions from "Reinsurance: Actuarial and Statistical Aspects"

Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <https://www.wiley.com/en-us/Reinsurance%3A+Actuarial+and+Statistical+Aspects-p-9780470772683>.

Maintained by Tom Reynkens. Last updated 4 months ago.

extremes reinsurance risk-analysis cpp

5.3 match 21 stars 6.29 score 186 scripts

bjw34032

waveslim:Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing

Basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002). All figures in chapters 4-7 of GSW (2001) are reproducible using this package and R code available at the book website(s) below.

Maintained by Brandon Whitcher. Last updated 10 months ago.

wavelets

4.3 match 3 stars 7.84 score 108 scripts 23 dependents

husson

FactoMineR:Multivariate Exploratory Data Analysis and Data Mining

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Maintained by Francois Husson. Last updated 4 months ago.

2.3 match 47 stars 14.71 score 5.6k scripts 112 dependents

gdemin

expss:Tables, Labels and Some Useful Functions from Spreadsheets and 'SPSS' Statistics

Package computes and displays tables with support for 'SPSS'-style labels, multiple and nested banners, weights, multiple-response variables and significance testing. There are facilities for nice output of tables in 'knitr', 'Shiny', '*.xlsx' files, R and 'Jupyter' notebooks. Methods for labelled variables add value labels support to base R functions and to some functions from other packages. Additionally, the package brings popular data transformation functions from 'SPSS' Statistics and 'Excel': 'RECODE', 'COUNT', 'COUNTIF', 'VLOOKUP' and etc. These functions are very useful for data processing in marketing research surveys. Package intended to help people to move data processing from 'Excel' and 'SPSS' to R.

Maintained by Gregory Demin. Last updated 12 months ago.

excel labels labels-support msexcel pivot-tables recode spss spss-statistics tables variable-labels vlookup

3.0 match 84 stars 11.00 score 1.8k scripts 4 dependents

anthonychristidis

RPEIF:Computation and Plots of Influence Functions for Risk and Performance Measures

Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Chen and Martin (2018) <https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019) <https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.

Maintained by Anthony Christidis. Last updated 3 months ago.

6.5 match 1 stars 4.97 score 14 scripts 2 dependents

alexanderlange53

svars:Data-Driven Identification of SVAR Models

Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).

Maintained by Alexander Lange. Last updated 2 years ago.

openblas cpp openmp

4.5 match 46 stars 7.22 score 130 scripts

rstudio

pointblank:Data Validation and Organization of Metadata for Local and Remote Tables

Validate data in data frames, 'tibble' objects, 'Spark' 'DataFrames', and database tables. Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. User-defined thresholds for failure rates allow for the determination of appropriate reporting actions. Many other workflows are available including an information management workflow, where the aim is to record, collect, and generate useful information on data tables.

Maintained by Richard Iannone. Last updated 6 days ago.

data-assertions data-checker data-dictionaries data-frames data-inference data-management data-profiler data-quality data-validation data-verification database-tables easy-to-understand reporting-tool schema-validation testing-tools yaml-configuration

3.0 match 942 stars 10.73 score 284 scripts

cran

geesmv:Modified Variance Estimators for Generalized Estimating Equations

Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.

Maintained by Zheng Li. Last updated 9 years ago.

18.0 match 1.78 score

jmcurran

Bolstad:Functions for Elementary Bayesian Inference

A set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.

Maintained by James Curran. Last updated 5 months ago.

7.8 match 4.09 score 93 scripts

revelle

psych:Procedures for Psychological, Psychometric, and Personality Research

A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.

Maintained by William Revelle. Last updated 10 days ago.

2.3 match 52 stars 14.12 score 29k scripts 317 dependents

cran

fGarch:Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

Analyze and model heteroskedastic behavior in financial time series.

Maintained by Georgi N. Boshnakov. Last updated 1 years ago.

fortran

5.0 match 7 stars 6.33 score 51 dependents

bioc

matter:Out-of-core statistical computing and signal processing

Toolbox for larger-than-memory scientific computing and visualization, providing efficient out-of-core data structures using files or shared memory, for dense and sparse vectors, matrices, and arrays, with applications to nonuniformly sampled signals and images.

Maintained by Kylie A. Bemis. Last updated 4 months ago.

infrastructure datarepresentation dataimport dimensionreduction preprocessing cpp

3.3 match 57 stars 9.52 score 64 scripts 2 dependents

r-forge

car:Companion to Applied Regression

Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, 2019.

Maintained by John Fox. Last updated 5 months ago.

2.0 match 15.38 score 43k scripts 919 dependents

srivastavbudugutta

DTwrappers2:Extensions of 'DTwrappers'

Offers functionality which provides methods for data analyses and cleaning that can be flexibly applied across multiple variables and in groups. These include cleaning accidental text, contingent calculations, counting missing data, and building summarizations of the data.

Maintained by Srivastav Budugutta. Last updated 10 months ago.

8.5 match 3.60 score 5 scripts

khliland

pls:Partial Least Squares and Principal Component Regression

Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).

Maintained by Kristian Hovde Liland. Last updated 2 months ago.

2.3 match 37 stars 13.60 score 3.2k scripts 85 dependents

rkillick

changepoint:Methods for Changepoint Detection

Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.

Maintained by Rebecca Killick. Last updated 4 months ago.

changepoint segmentation

2.8 match 133 stars 11.05 score 736 scripts 40 dependents

flr

FLSAM:An Implementation of the State-Space Assessment Model for FLR

This package provides an FLR wrapper to the SAM state-space assessment model.

Maintained by N.T. Hintzen. Last updated 4 months ago.

6.8 match 4 stars 4.51 score 406 scripts

adeverse

ade4:Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.

Maintained by Aurélie Siberchicot. Last updated 12 days ago.

openblas cpp

2.0 match 40 stars 15.10 score 2.2k scripts 257 dependents

ropensci

jqr:Client for 'jq', a 'JSON' Processor

Client for 'jq', a 'JSON' processor (<https://jqlang.github.io/jq/>), written in C. 'jq' allows the following with 'JSON' data: index into, parse, do calculations, cut up and filter, change key names and values, perform conditionals and comparisons, and more.

Maintained by Jeroen Ooms. Last updated 4 months ago.

jq json

3.0 match 144 stars 10.04 score 95 scripts 28 dependents

cran

graphicalVAR:Graphical VAR for Experience Sampling Data

Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

Maintained by Sacha Epskamp. Last updated 1 years ago.

openblas cpp

12.1 match 2 stars 2.48 score 5 dependents

neferkareii

shrinkTVPVAR:Efficient Bayesian Inference for TVP-VAR-SV Models with Shrinkage

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with shrinkage priors. Details on the algorithms used are provided in Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.

Maintained by Peter Knaus. Last updated 7 months ago.

openblas cpp

23.0 match 2 stars 1.30 score 2 scripts

ngreifer

cobalt:Covariate Balance Tables and Plots

Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'WeightIt', 'MatchThem', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.

Maintained by Noah Greifer. Last updated 12 months ago.

causal-inference propensity-scores

2.3 match 75 stars 12.98 score 1.0k scripts 8 dependents

juba

questionr:Functions to Make Surveys Processing Easier

Set of functions to make the processing and analysis of surveys easier : interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions.

Maintained by Julien Barnier. Last updated 11 days ago.

2.3 match 83 stars 12.93 score 1.1k scripts 19 dependents

rapporter

pander:An R 'Pandoc' Writer

Contains some functions catching all messages, 'stdout' and other useful information while evaluating R code and other helpers to return user specified text elements (like: header, paragraph, table, image, lists etc.) in 'pandoc' markdown or several type of R objects similarly automatically transformed to markdown format. Also capable of exporting/converting (the resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This latter reporting feature is supported in brew syntax or with a custom reference class with a smarty caching 'backend'.

Maintained by Gergely Daróczi. Last updated 1 months ago.

literate-programming markdown pandoc pandoc-markdown reproducible-research rmarkdown cpp

1.7 match 298 stars 16.76 score 7.6k scripts 110 dependents

mmahmoudian

varhandle:Functions for Robust Variable Handling

Variables are the fundamental parts of each programming language but handling them efficiently might be frustrating for programmers. This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. These functions are written and crafted since 2014 with years of experience in statistical data analysis on high-dimensional data, and for each of them there was a need. Functions in this package are supposed to be efficient and easy to use, hence they will be frequently updated to make them more convenient.

Maintained by Mehrad Mahmoudian. Last updated 2 years ago.

4.5 match 6.18 score 572 scripts 6 dependents

r-forge

fExtremes:Rmetrics - Modelling Extreme Events in Finance

Provides functions for analysing and modelling extreme events in financial time Series. The topics include: (i) data pre-processing, (ii) explorative data analysis, (iii) peak over threshold modelling, (iv) block maxima modelling, (v) estimation of VaR and CVaR, and (vi) the computation of the extreme index.

Maintained by Paul J. Northrop. Last updated 3 hours ago.

3.8 match 1 stars 7.24 score 118 scripts 4 dependents

tdhock

nc:Named Capture to Data Tables

User-friendly functions for extracting a data table (row for each match, column for each group) from non-tabular text data using regular expressions, and for melting columns that match a regular expression. Patterns are defined using a readable syntax that makes it easy to build complex patterns in terms of simpler, re-usable sub-patterns. Named R arguments are translated to column names in the output; capture groups without names are used internally in order to provide a standard interface to three regular expression 'C' libraries ('PCRE', 'RE2', 'ICU'). Output can also include numeric columns via user-specified type conversion functions.

Maintained by Toby Hocking. Last updated 7 days ago.

4.0 match 17 stars 6.84 score 46 scripts

arnaldpuy

sensobol:Computation of Variance-Based Sensitivity Indices

It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol' indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. An overview of the package can be found in Puy et al. (2022) <doi:10.18637/jss.v102.i05>.

Maintained by Arnald Puy. Last updated 1 years ago.

cpp

4.9 match 15 stars 5.57 score 50 scripts

jamesramsay5

fda:Functional Data Analysis

These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from <https://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.

Maintained by James Ramsay. Last updated 4 months ago.

2.3 match 3 stars 11.88 score 2.0k scripts 142 dependents

jcfaria

bpca:Biplot of Multivariate Data Based on Principal Components Analysis

Implements biplot (2d and 3d) of multivariate data based on principal components analysis and diagnostic tools of the quality of the reduction.

Maintained by José C. Faria. Last updated 1 years ago.

4.5 match 4 stars 5.79 score 34 scripts

braverock

PortfolioAnalytics:Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios

Portfolio optimization and analysis routines and graphics.

Maintained by Brian G. Peterson. Last updated 4 months ago.

2.3 match 81 stars 11.49 score 626 scripts 2 dependents

inlabru-org

inlabru:Bayesian Latent Gaussian Modelling using INLA and Extensions

Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.

Maintained by Finn Lindgren. Last updated 16 hours ago.

2.0 match 96 stars 12.60 score 832 scripts 6 dependents

elvanceyhan

nnspat:Nearest Neighbor Methods for Spatial Patterns

Contains the functions for testing the spatial patterns (of segregation, spatial symmetry, association, disease clustering, species correspondence and reflexivity) based on nearest neighbor relations, especially using contingency tables such as nearest neighbor contingency tables (Ceyhan (2010) <doi:10.1007/s10651-008-0104-x> and Ceyhan (2017) <doi:10.1016/j.jkss.2016.10.002> and references therein), nearest neighbor symmetry contingency tables (Ceyhan (2014) <doi:10.1155/2014/698296>), species correspondence contingency tables and reflexivity contingency tables (Ceyhan (2018) <doi:10.2436/20.8080.02.72>) for two (or higher) dimensional data. Also contains functions for generating patterns of segregation, association, uniformity in a multi-class setting (Ceyhan (2014) <doi:10.1007/s00477-013-0824-9>), and various non-random labeling patterns for disease clustering in two dimensional cases (Ceyhan (2014) <doi:10.1002/sim.6053>), and for visualization of all these patterns for the two dimensional data. The tests are usually (asymptotic) normal z-tests and chi-square tests.

Maintained by Elvan Ceyhan. Last updated 3 years ago.

8.8 match 2.90 score 16 scripts

adamlilith

fasterRaster:Faster Raster and Spatial Vector Processing Using 'GRASS GIS'

Processing of large-in-memory/large-on disk rasters and spatial vectors using 'GRASS GIS' <https://grass.osgeo.org/>. Most functions in the 'terra' package are recreated. Processing of medium-sized and smaller spatial objects will nearly always be faster using 'terra' or 'sf', but for large-in-memory/large-on-disk objects, 'fasterRaster' may be faster. To use most of the functions, you must have the stand-alone version (not the 'OSGeoW4' installer version) of 'GRASS GIS' 8.0 or higher.

Maintained by Adam B. Smith. Last updated 5 days ago.

aspect distance fragmentation fragmentation-indices gis grass grass-gis raster raster-projection rasterize slope topography vectorization

3.3 match 57 stars 7.68 score 8 scripts

dreamrs

apexcharter:Create Interactive Chart with the JavaScript 'ApexCharts' Library

Provides an 'htmlwidgets' interface to 'apexcharts.js'. 'Apexcharts' is a modern JavaScript charting library to build interactive charts and visualizations with simple API. 'Apexcharts' examples and documentation are available here: <https://apexcharts.com/>.

Maintained by Victor Perrier. Last updated 1 months ago.

data-visualization htmlwidgets

3.0 match 144 stars 8.43 score 247 scripts

derecost

LOGAN:Log File Analysis in International Large-Scale Assessments

Enables users to handle the dataset cleaning for conducting specific analyses with the log files from two international educational assessments: the Programme for International Student Assessment (PISA, <http://www.oecd.org/pisa/>) and the Programme for the International Assessment of Adult Competencies (PIAAC, <http://www.oecd.org/skills/piaac/>). An illustration of the analyses can be found on the LOGAN Shiny app (<https://loganpackage.shinyapps.io/shiny/>) on your browser.

Maintained by Denise Reis Costa. Last updated 5 years ago.

9.3 match 1 stars 2.70 score 1 scripts

tidyverts

feasts:Feature Extraction and Statistics for Time Series

Provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name 'feasts' is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.

Maintained by Mitchell OHara-Wild. Last updated 5 months ago.

2.0 match 300 stars 12.38 score 1.4k scripts 7 dependents

alexiosg

rugarch:Univariate GARCH Models

ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.

Maintained by Alexios Galanos. Last updated 3 months ago.

cpp

2.0 match 26 stars 12.25 score 1.3k scripts 16 dependents

meireles

spectrolab:Class and Methods for Spectral Data

Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class spectra that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra.

Maintained by Jose Eduardo Meireles. Last updated 3 months ago.

science

3.3 match 16 stars 7.39 score 256 scripts

vanderleidebastiani

SYNCSA:Analysis of Functional and Phylogenetic Patterns in Metacommunities

Analysis of metacommunities based on functional traits and phylogeny of the community components. The functions that are offered here implement for the R environment methods that have been available in the SYNCSA application written in C++ (by Valerio Pillar, available at <http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).

Maintained by Vanderlei Julio Debastiani. Last updated 5 years ago.

4.5 match 3 stars 5.36 score 28 scripts 1 dependents

adaemmerp

lpirfs:Local Projections Impulse Response Functions

Provides functions to estimate and visualize linear as well as nonlinear impulse responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>. The methods and the package are explained in detail in Adämmer (2019) <doi:10.32614/RJ-2019-052>.

Maintained by Philipp Adämmer. Last updated 12 days ago.

openblas cpp

3.6 match 44 stars 6.68 score 108 scripts

peiliangbai92

LSVAR:Estimation of Low Rank Plus Sparse Structured Vector Auto-Regressive (VAR) Model

Implementations of estimation algorithm of low rank plus sparse structured VAR model by using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). It relates to the algorithm in Sumanta, Li, and Michailidis (2019) <doi:10.1109/TSP.2018.2887401>.

Maintained by Peiliang Bai. Last updated 4 years ago.

5.1 match 4 stars 4.60 score 5 scripts

bioc

lemur:Latent Embedding Multivariate Regression

Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed.

Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.

transcriptomics differentialexpression singlecell dimensionreduction regression openblas cpp

3.0 match 87 stars 7.69 score 81 scripts

promidat

loadeR:Load Data for Analysis System

Provides a framework to load text and excel files through a 'shiny' graphical interface. It allows renaming, transforming, ordering and removing variables. It includes basic exploratory methods such as the mean, median, mode, normality test, histogram and correlation.

Maintained by Oldemar Rodriguez. Last updated 2 years ago.

4.5 match 5.09 score 275 scripts 3 dependents

spatstat

spatstat.univar:One-Dimensional Probability Distribution Support for the 'spatstat' Family

Estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, kernel properties, quantiles and integration.

Maintained by Adrian Baddeley. Last updated 1 hours ago.

2.3 match 3 stars 10.14 score 1 scripts 245 dependents

aniebee

ClusterVAR:Fitting Latent Class Vector-Autoregressive (VAR) Models

Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters.

Maintained by Anja Ernst. Last updated 3 months ago.

clustering latent-class-model multivariate-timeseries time-series-analysis vector-autoregression vector-autoregression-models

4.7 match 2 stars 4.88 score 1 scripts

earowang

sugrrants:Supporting Graphs for Analysing Time Series

Provides 'ggplot2' graphics for analysing time series data. It aims to fit into the 'tidyverse' and grammar of graphics framework for handling temporal data.

Maintained by Earo Wang. Last updated 1 years ago.

statistical-graphics time-series

3.0 match 82 stars 7.42 score 214 scripts 1 dependents

jeffreyevans

yaImpute:Nearest Neighbor Observation Imputation and Evaluation Tools

Performs nearest neighbor-based imputation using one or more alternative approaches to processing multivariate data. These include methods based on canonical correlation: analysis, canonical correspondence analysis, and a multivariate adaptation of the random forest classification and regression techniques of Leo Breiman and Adele Cutler. Additional methods are also offered. The package includes functions for comparing the results from running alternative techniques, detecting imputation targets that are notably distant from reference observations, detecting and correcting for bias, bootstrapping and building ensemble imputations, and mapping results.

Maintained by Jeffrey S. Evans. Last updated 7 months ago.

imputation cpp

3.0 match 3 stars 7.40 score 94 scripts 12 dependents

r-forge

distrEx:Extensions of Package 'distr'

Extends package 'distr' by functionals, distances, and conditional distributions.

Maintained by Matthias Kohl. Last updated 2 months ago.

3.3 match 6.64 score 107 scripts 17 dependents

bioc

BumpyMatrix:Bumpy Matrix of Non-Scalar Objects

Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects.

Maintained by Aaron Lun. Last updated 3 months ago.

software infrastructure datarepresentation

3.3 match 1 stars 6.62 score 39 scripts 12 dependents

mmaechler

fracdiff:Fractionally Differenced ARIMA aka ARFIMA(P,d,q) Models

Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989); including inference and basic methods. Some alternative algorithms to estimate "H".

Maintained by Martin Maechler. Last updated 1 years ago.

openblas

2.3 match 17 stars 9.66 score 316 scripts 259 dependents

yujunghwang

factormodel:Factor Model Estimation Using Proxy Variables

Functions to estimate a factor model using discrete and continuous proxy variables. The function 'dproxyme' estimates a factor model of discrete proxy variables using an EM algorithm (Dempster, Laird, Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>; Hu (2008) <doi:10.1016/j.jeconom.2007.12.001>; Hu(2017) <doi:10.1016/j.jeconom.2017.06.002> ). The function 'cproxyme' estimates a linear factor model (Cunha, Heckman, and Schennach (2010) <doi:10.3982/ECTA6551>).

Maintained by Yujung Hwang. Last updated 4 years ago.

4.5 match 4 stars 4.78 score 4 scripts 1 dependents

stamats

MKpower:Power Analysis and Sample Size Calculation

Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007), <doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), <doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>), comparing two negative binomial rates (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>), ANCOVA (Shieh (2020), <doi:10.1007/s11336-019-09692-3>), reference ranges (Jennen-Steinmetz and Wellek (2005), <doi:10.1002/sim.2177>), multiple primary endpoints (Sozu et al. (2015), ISBN:978-3-319-22005-5), and AUC (Hanley and McNeil (1982), <doi:10.1148/radiology.143.1.7063747>).

Maintained by Matthias Kohl. Last updated 6 months ago.

3.8 match 7 stars 5.65 score 32 scripts

brpetrucci

paleobuddy:Simulating Diversification Dynamics

Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like 'paleotree' and 'APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, 'paleobuddy' can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in 'RPANDA': Morlon H. et al (2016) <doi:10.1111/2041-210X.12526>.

Maintained by Bruno do Rosario Petrucci. Last updated 2 months ago.

evolution macroevolution paleobiology paleontology phylogenetics

4.3 match 6 stars 4.95 score 4 scripts

reinhardfurrer

spam:SPArse Matrix

Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation("spam")' for details.

Maintained by Reinhard Furrer. Last updated 2 months ago.

fortran openblas cpp

2.3 match 1 stars 9.36 score 420 scripts 439 dependents

avi-kenny

SimEngine:A Modular Framework for Statistical Simulations in R

An open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing environments.See full documentation at <https://avi-kenny.github.io/SimEngine/>.

Maintained by Avi Kenny. Last updated 1 months ago.

3.0 match 12 stars 6.95 score 50 scripts

shannonpileggi

gtreg:Regulatory Tables for Clinical Research

Creates tables suitable for regulatory agency submission by leveraging the 'gtsummary' package as the back end. Tables can be exported to HTML, Word, PDF and more. Highly customized outputs are available by utilizing existing styling functions from 'gtsummary' as well as custom options designed for regulatory tables.

Maintained by Shannon Pileggi. Last updated 1 months ago.

3.0 match 37 stars 6.92 score 30 scripts

robinhankin

mvp:Fast Symbolic Multivariate Polynomials

Fast manipulation of symbolic multivariate polynomials using the 'Map' class of the Standard Template Library. The package uses print and coercion methods from the 'mpoly' package but offers speed improvements. It is comparable in speed to the 'spray' package for sparse arrays, but retains the symbolic benefits of 'mpoly'. To cite the package in publications, use Hankin 2022 <doi:10.48550/ARXIV.2210.15991>. Uses 'disordR' discipline.

Maintained by Robin K. S. Hankin. Last updated 21 hours ago.

cpp

3.0 match 9 stars 6.89 score 36 scripts 2 dependents

dreamrs

vchartr:Interactive Charts with the 'JavaScript' 'VChart' Library

Provides an 'htmlwidgets' interface to 'VChart.js'. 'VChart', more than just a cross-platform charting library, but also an expressive data storyteller. 'VChart' examples and documentation are available here: <https://www.visactor.io/vchart>.

Maintained by Victor Perrier. Last updated 3 months ago.

3.0 match 9 stars 6.89 score 96 scripts

covaruber

lme4breeding:Relationship-Based Mixed-Effects Models

Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'.

Maintained by Giovanny Covarrubias-Pazaran. Last updated 1 months ago.

4.0 match 5 stars 5.15 score 7 scripts

bmihaljevic

bnclassify:Learning Discrete Bayesian Network Classifiers from Data

State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.

Maintained by Mihaljevic Bojan. Last updated 1 years ago.

cpp

3.0 match 18 stars 6.85 score 66 scripts

saviviro

sstvars:Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

Penalized and non-penalized maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, and calculation of impulse response functions, generalized impulse response functions, and generalized forecast error variance decompositions. See Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2025) <doi:10.48550/arXiv.2403.14216>, Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.

Maintained by Savi Virolainen. Last updated 1 months ago.

openblas cpp openmp

3.2 match 4 stars 6.33 score 41 scripts

mrkaye97

slackr:Send Messages, Images, R Objects and Files to 'Slack' Channels/Users

'Slack' <https://slack.com/> provides a service for teams to collaborate by sharing messages, images, links, files and more. Functions are provided that make it possible to interact with the 'Slack' platform 'API'. When you need to share information or data from R, rather than resort to copy/ paste in e-mails or other services like 'Skype' <https://www.skype.com/en/>, you can use this package to send well-formatted output from multiple R objects and expressions to all teammates at the same time with little effort. You can also send images from the current graphics device, R objects, and upload files.

Maintained by Matt Kaye. Last updated 6 months ago.

slack

1.8 match 306 stars 11.66 score 179 scripts

jlaake

RMark:R Code for Mark Analysis

An interface to the software package MARK that constructs input files for MARK and extracts the output. MARK was developed by Gary White and is freely available at <http://www.phidot.org/software/mark/downloads/> but is not open source.

Maintained by Jeff Laake. Last updated 3 years ago.

4.3 match 4.77 score 366 scripts 4 dependents

tidyfun

tf:S3 Classes and Methods for Tidy Functional Data

Defines S3 vector data types for vectors of functional data (grid-based, spline-based or functional principal components-based) with all arithmetic and summary methods, derivation, integration and smoothing, plotting, data import and export, and data wrangling, such as re-evaluating, subsetting, sub-assigning, zooming into sub-domains, or extracting functional features like minima/maxima and their locations. The implementation allows including such vectors in data frames for joint analysis of functional and scalar variables.

Maintained by Fabian Scheipl. Last updated 3 days ago.

3.3 match 7 stars 6.14 score 13 scripts 2 dependents

adeverse

adespatial:Multivariate Multiscale Spatial Analysis

Tools for the multiscale spatial analysis of multivariate data. Several methods are based on the use of a spatial weighting matrix and its eigenvector decomposition (Moran's Eigenvectors Maps, MEM). Several approaches are described in the review Dray et al (2012) <doi:10.1890/11-1183.1>.

Maintained by Aurélie Siberchicot. Last updated 13 hours ago.

fortran openblas

1.8 match 36 stars 11.25 score 398 scripts 2 dependents

jinseob2kim

jstable:Create Tables from Different Types of Regression

Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.

Maintained by Jinseob Kim. Last updated 4 days ago.

label regression table

2.0 match 28 stars 10.08 score 199 scripts 1 dependents

bioc

marray:Exploratory analysis for two-color spotted microarray data

Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking.

Maintained by Yee Hwa (Jean) Yang. Last updated 5 months ago.

microarray twochannel preprocessing

2.3 match 8.92 score 222 scripts 38 dependents

paleolimbot

tidypaleo:Tidy Tools for Paleoenvironmental Archives

Provides a set of functions with a common framework for age-depth model management, stratigraphic visualization, and common statistical transformations. The focus of the package is stratigraphic visualization, for which 'ggplot2' components are provided to reproduce the scales, geometries, facets, and theme elements commonly used in publication-quality stratigraphic diagrams. Helpers are also provided to reproduce the exploratory statistical summaries that are frequently included on stratigraphic diagrams. See Dunnington et al. (2021) <doi:10.18637/jss.v101.i07>.

Maintained by Dewey Dunnington. Last updated 2 years ago.

3.0 match 34 stars 6.59 score 38 scripts

bioc

tigre:Transcription factor Inference through Gaussian process Reconstruction of Expression

The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF.

Maintained by Antti Honkela. Last updated 5 months ago.

microarray timecourse geneexpression transcription generegulation networkinference bayesian

4.5 match 4.38 score 6 scripts

tomaskrehlik

frequencyConnectedness:Spectral Decomposition of Connectedness Measures

Accompanies a paper (Barunik, Krehlik (2018) <doi:10.1093/jjfinec/nby001>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.

Maintained by Tomas Krehlik. Last updated 2 years ago.

3.3 match 100 stars 5.88 score 50 scripts 1 dependents

helloworld9293

VARcpDetectOnline:Sequential Change Point Detection for High-Dimensional VAR Models

Implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024) <doi:10.5705/ss.202024.0182>, "Sequential Change Point Detection in High-dimensional Vector Auto-regressive Models". This package provides tools for detecting change points in the transition matrices of VAR models, effectively identifying shifts in temporal and cross-correlations within high-dimensional time series data.

Maintained by Yuhan Tian. Last updated 2 months ago.

7.0 match 3 stars 2.78 score

sebkrantz

collapse:Advanced and Fast Data Transformation

A C/C++ based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R. It further includes fast functions for common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It is well integrated with base R classes, 'dplyr'/'tibble', 'data.table', 'sf', 'units', 'plm' (panel-series and data frames), and 'xts'/'zoo'.

Maintained by Sebastian Krantz. Last updated 10 days ago.

data-aggregation data-analysis data-manipulation data-processing data-science data-transformation econometrics high-performance panel-data scientific-computing statistics time-series weighted weights cpp openmp

1.2 match 672 stars 16.68 score 708 scripts 99 dependents

dgbonett

statpsych:Statistical Methods for Psychologists

Implements confidence interval and sample size methods that are especially useful in psychological research. The methods can be applied in 1-group, 2-group, paired-samples, and multiple-group designs and to a variety of parameters including means, medians, proportions, slopes, standardized mean differences, standardized linear contrasts of means, plus several measures of correlation and association. Confidence interval and sample size functions are given for single parameters as well as differences, ratios, and linear contrasts of parameters. The sample size functions can be used to approximate the sample size needed to estimate a parameter or function of parameters with desired confidence interval precision or to perform a variety of hypothesis tests (directional two-sided, equivalence, superiority, noninferiority) with desired power. For details see: Statistical Methods for Psychologists, Volumes 1 – 4, <https://dgbonett.sites.ucsc.edu/>.

Maintained by Douglas G. Bonett. Last updated 4 months ago.

4.0 match 6 stars 4.83 score 15 scripts 1 dependents

haythorn

sr:Smooth Regression - The Gamma Test and Tools

Finds causal connections in precision data, finds lags and embeddings in time series, guides training of neural networks and other smooth models, evaluates their performance, gives a mathematically grounded answer to the over-training problem. Smooth regression is based on the Gamma test, which measures smoothness in a multivariate relationship. Causal relations are smooth, noise is not. 'sr' includes the Gamma test and search techniques that use it. References: Evans & Jones (2002) <doi:10.1098/rspa.2002.1010>, AJ Jones (2004) <doi:10.1007/s10287-003-0006-1>.

Maintained by Wayne Haythorn. Last updated 2 years ago.

5.2 match 3.70 score 9 scripts

cran

paleoTS:Analyze Paleontological Time-Series

Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts).

Maintained by Gene Hunt. Last updated 7 months ago.

4.3 match 1 stars 4.48 score 2 dependents

tconwell

html5:Creates Valid HTML5 Strings

Generates valid HTML tag strings for HTML5 elements documented by Mozilla. Attributes are passed as named lists, with names being the attribute name and values being the attribute value. Attribute values are automatically double-quoted. To declare a DOCTYPE, wrap html() with function doctype(). Mozilla's documentation for HTML5 is available here: <https://developer.mozilla.org/en-US/docs/Web/HTML/Element>. Elements marked as obsolete are not included.

Maintained by Timothy Conwell. Last updated 2 years ago.

5.2 match 1 stars 3.65 score 1 scripts 3 dependents

dkahle

mpoly:Symbolic Computation and More with Multivariate Polynomials

Symbolic computing with multivariate polynomials in R.

Maintained by David Kahle. Last updated 4 months ago.

3.0 match 12 stars 6.25 score 70 scripts 7 dependents

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

3.6 match 3 stars 5.17 score 49 scripts

leemorinucf

FCVAR:Estimation and Inference for the Fractionally Cointegrated VAR

Estimation and inference using the Fractionally Cointegrated Vector Autoregressive (VAR) model. It includes functions for model specification, including lag selection and cointegration rank selection, as well as a comprehensive set of options for hypothesis testing, including tests of hypotheses on the cointegrating relations, the adjustment coefficients and the fractional differencing parameters. An article describing the FCVAR model with examples is available on the Webpage <https://sites.google.com/view/mortennielsen/software>.

Maintained by Lealand Morin. Last updated 3 years ago.

5.3 match 7 stars 3.54 score 6 scripts

robinhankin

emulator:Bayesian Emulation of Computer Programs

Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. The package includes functionality to evaluate quadratic forms efficiently.

Maintained by Robin K. S. Hankin. Last updated 9 months ago.

2.3 match 4 stars 8.27 score 56 scripts 17 dependents

cran

doudpackage:Create Elegant Table 1 in HTML for Bio-Statistics

Creates the "table one" of bio-medical papers. Fill it with your data and the name of the variable which you'll make the group(s) out of and it will make univariate, bivariate analysis and parse it into HTML. It also allows you to visualize all your data with graphic representation.

Maintained by Edouard Baudouin. Last updated 2 years ago.

10.8 match 1.70 score

flr

AAP:Aarts and Poos Stock Assessment Model that Estimates Bycatch

FLR version of Aarts and Poos stock assessment model.

Maintained by Iago Mosqueira. Last updated 2 years ago.

6.8 match 2.70 score 5 scripts

decodegenetics

gnonadd:Various Non-Additive Models for Genetic Associations

The goal of 'gnonadd' is to simplify workflows in the analysis of non-additive effects of sequence variants. This includes variance effects (Ivarsdottir et. al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and dominance effects. The package also includes convenience functions for visualization.

Maintained by Audunn S. Snaebjarnarson. Last updated 3 months ago.

6.8 match 1 stars 2.70 score

t-kalinowski

keras:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

Maintained by Tomasz Kalinowski. Last updated 12 months ago.

1.7 match 10.93 score 10k scripts 55 dependents

cran

RSDA:R to Symbolic Data Analysis

Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.

Maintained by Oldemar Rodriguez. Last updated 1 years ago.

5.6 match 1 stars 3.26 score 3 dependents

sbgraves237

Ecfun:Functions for 'Ecdat'

Functions and vignettes to update data sets in 'Ecdat' and to create, manipulate, plot, and analyze those and similar data sets.

Maintained by Spencer Graves. Last updated 4 months ago.

2.3 match 8.02 score 85 scripts 4 dependents

bayesiandemography

rvec:Vector Representing a Random Variable

Random vectors, called rvecs. An rvec holds multiple draws, but tries to behave like a standard R vector, including working well in data frames. Rvecs are useful for working with output from a simulation or a Bayesian analysis.

Maintained by John Bryant. Last updated 6 months ago.

3.3 match 2 stars 5.46 score 24 scripts 2 dependents