R-universe search: stationary

rich-iannone

stationaRy:Detailed Meteorological Data from Stations All Over the World

Acquire hourly meteorological data from stations located all over the world. There is a wealth of data available, with historic weather data accessible from nearly 30,000 stations. The available data is automatically downloaded from a data repository and processed into a 'tibble' for the exact range of years requested. A relative humidity approximation is provided using the 'August-Roche-Magnus' formula, which was adapted from Alduchov and Eskridge (1996) <doi:10.1175%2F1520-0450%281996%29035%3C0601%3AIMFAOS%3E2.0.CO%3B2>.

Maintained by Richard Iannone. Last updated 5 years ago.

dataset global met-data

50.0 match 250 stars 6.44 score 74 scripts

spatstat

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

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

Maintained by Adrian Baddeley. Last updated 11 days ago.

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

17.0 match 5 stars 9.09 score 6 scripts 46 dependents

davidbolin

rSPDE:Rational Approximations of Fractional Stochastic Partial Differential Equations

Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.1080/10618600.2023.2231051> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.

Maintained by David Bolin. Last updated 11 days ago.

18.6 match 11 stars 7.65 score 188 scripts 3 dependents

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

10.5 match 15 stars 12.70 score 7.7k scripts 297 dependents

alowis

RtsEva:Performs the Transformed-Stationary Extreme Values Analysis

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

Maintained by Alois Tilloy. Last updated 6 months ago.

extreme-value-statistics non-stationary-environment

17.0 match 4 stars 5.41 score 4 scripts

bmcclintock

momentuHMM:Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, discrete- or continuous-time HMMs, continuous- or discrete-space movement models, approximate Langevin diffusion models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.

Maintained by Brett McClintock. Last updated 2 months ago.

openblas cpp

10.3 match 43 stars 8.47 score 162 scripts

pachadotdev

LSTS:Locally Stationary Time Series

A set of functions that allow stationary analysis and locally stationary time series analysis.

Maintained by Mauricio Vargas. Last updated 1 years ago.

14.6 match 3 stars 5.54 score 51 scripts 5 dependents

janoleko

LaMa:Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.

Maintained by Jan-Ole Koslik. Last updated 5 days ago.

openblas cpp openmp

10.2 match 9 stars 7.86 score 42 scripts

spatstat

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

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

Maintained by Adrian Baddeley. Last updated 14 days ago.

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

7.0 match 5 stars 10.85 score 84 scripts 175 dependents

theomichelot

moveHMM:Animal Movement Modelling using Hidden Markov Models

Provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process, etc. <doi:10.1111/2041-210X.12578>.

Maintained by Theo Michelot. Last updated 1 years ago.

openblas cpp

8.8 match 38 stars 8.63 score 112 scripts

spsanderson

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

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

Maintained by Steven Sanderson. Last updated 6 months ago.

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

8.7 match 19 stars 7.58 score 56 scripts 1 dependents

mlysy

SuperGauss:Superfast Likelihood Inference for Stationary Gaussian Time Series

Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations. This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300. Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo. The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.

Maintained by Martin Lysy. Last updated 2 months ago.

fftw3 cpp

11.5 match 2 stars 5.60 score 33 scripts 2 dependents

spedygiorgio

markovchain:Easy Handling Discrete Time Markov Chains

Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>. Some functions for continuous times Markov chains depend on the suggested ctmcd package.

Maintained by Giorgio Alfredo Spedicato. Last updated 5 months ago.

ctmc dtmc markov-chain markov-model r-programming rcpp openblas cpp

4.9 match 104 stars 12.78 score 712 scripts 4 dependents

robjhyndman

forecast:Forecasting Functions for Time Series and Linear Models

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

Maintained by Rob Hyndman. Last updated 7 months ago.

forecast forecasting openblas cpp

3.5 match 1.1k stars 17.46 score 16k scripts 240 dependents

agbarnett

season:Seasonal Analysis of Health Data

Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo for checking the case-crossover code.

Maintained by Adrian Barnett. Last updated 3 years ago.

non-linear seasons time-series

10.5 match 2 stars 5.85 score 70 scripts

jclavel

mvMORPH:Multivariate Comparative Tools for Fitting Evolutionary Models to Morphometric Data

Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood. See Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.

Maintained by Julien Clavel. Last updated 2 months ago.

openblas

6.5 match 17 stars 9.46 score 189 scripts 3 dependents

spatstat

spatstat.linnet:Linear Networks Functionality of the 'spatstat' Family

Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models.

Maintained by Adrian Baddeley. Last updated 2 months ago.

density-estimation heat-equation kernel-density-estimation network-analysis point-processes spatial-data-analysis statistical-analysis statistical-inference statistical-models

6.2 match 6 stars 9.58 score 35 scripts 42 dependents

smac-group

avar:Allan Variance

Implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.

Maintained by Stéphane Guerrier. Last updated 3 years ago.

allan-variance inertial-sensors statistics time-series cpp

10.2 match 5 stars 4.88 score 9 scripts

albgarre

biogrowth:Modelling of Population Growth

Modelling of population growth under static and dynamic environmental conditions. Includes functions for model fitting and making prediction under isothermal and dynamic conditions. The methods (algorithms & models) are based on predictive microbiology (See Perez-Rodriguez and Valero (2012, ISBN:978-1-4614-5519-6)).

Maintained by Alberto Garre. Last updated 8 days ago.

6.0 match 6 stars 6.98 score 44 scripts

fchamroukhi

samurais:Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

Provides a variety of original and flexible user-friendly statistical latent variable models and unsupervised learning algorithms to segment and represent time-series data (univariate or multivariate), and more generally, longitudinal data, which include regime changes. 'samurais' is built upon the following packages, each of them is an autonomous time-series segmentation approach: Regression with Hidden Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise regression ('PWR'). For the advantages/differences of each of them, the user is referred to our mentioned paper references. These models are originally introduced and written in 'Matlab' by Faicel Chamroukhi <https://github.com/fchamroukhi?&tab=repositories&q=time-series&type=public&language=matlab>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence change-point-detection data-science dynamic-programming em-algorithm hidden-markov-models hidden-process-regression human-activity-recognition latent-variable-models model-selection multivariate-timeseries newton-raphson piecewise-regression statistical-inference statistical-learning time-series-analysis time-series-clustering openblas cpp

6.8 match 11 stars 6.14 score 28 scripts

cran

quantilogram:Cross-Quantilogram

Estimation and inference methods for the cross-quantilogram. The cross-quantilogram is a measure of nonlinear dependence between two variables, based on either unconditional or conditional quantile functions. It can be considered an extension of the correlogram, which is a correlation function over multiple lag periods that mainly focuses on linear dependency. One can use the cross-quantilogram to detect the presence of directional predictability from one time series to another. This package provides a statistical inference method based on the stationary bootstrap. For detailed theoretical and empirical explanations, see Linton and Whang (2007) for univariate time series analysis and Han, Linton, Oka and Whang (2016) for multivariate time series analysis. The full references for these key publications are as follows: (1) Linton, O., and Whang, Y. J. (2007). The quantilogram: with an application to evaluating directional predictability. Journal of Econometrics, 141(1), 250-282 <doi:10.1016/j.jeconom.2007.01.004>; (2) Han, H., Linton, O., Oka, T., and Whang, Y. J. (2016). The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251-270 <doi:10.1016/j.jeconom.2016.03.001>.

Maintained by Tatsushi Oka. Last updated 7 months ago.

19.2 match 3 stars 2.18 score

eldarrak

FLightR:Reconstruct Animal Paths from Solar Geolocation Loggers Data

Spatio-temporal locations of an animal are computed from annotated data with a hidden Markov model via particle filter algorithm. The package is relatively robust to varying degrees of shading. The hidden Markov model is described in Movement Ecology - Rakhimberdiev et al. (2015) <doi:10.1186/s40462-015-0062-5>, general package description is in the Methods in Ecology and Evolution - Rakhimberdiev et al. (2017) <doi:10.1111/2041-210X.12765> and package accuracy assessed in the Journal of Avian Biology - Rakhimberdiev et al. (2016) <doi:10.1111/jav.00891>.

Maintained by Eldar Rakhimberdiev. Last updated 6 months ago.

movement-ecology solar-geolocation-loggers solar-geolocator

5.7 match 23 stars 7.28 score 111 scripts

laplacesdemonr

LaplacesDemon:Complete Environment for Bayesian Inference

Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).

Maintained by Henrik Singmann. Last updated 1 years ago.

3.0 match 93 stars 13.45 score 1.8k scripts 60 dependents

danheck

MCMCprecision:Precision of Discrete Parameters in Transdimensional MCMC

Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.

Maintained by Daniel W. Heck. Last updated 9 months ago.

openblas cpp

7.0 match 5.49 score 52 scripts 4 dependents

ingmarvisser

depmixS4:Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4

Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>).

Maintained by Ingmar Visser. Last updated 4 years ago.

4.8 match 12 stars 6.85 score 308 scripts 4 dependents

mashroommole

MG1StationaryProbability:Computes Stationary Distribution for M/G/1 Queuing System

The idea of a computational algorithm described in the article by Andronov M. et al. (2022) <https://link.springer.com/chapter/10.1007/978-3-030-92507-9_13>. The purpose of this package is to automate computations for a Markov-Modulated M/G/1 queuing system with alternating Poisson flow of arrivals. It offers a set of functions to calculate various mean indices of the system, including mean flow intensity, mean service busy and idle times, and the system's stationary probability.

Maintained by Olga Zoldaka. Last updated 2 years ago.

16.0 match 2.00 score 1 scripts

azalk

RChest:Locating Distributional Changes in Highly Dependent Time Series

Provides algorithms to locate multiple distributional change-points in piecewise stationary time series. The algorithms are provably consistent, even in the presence of long-range dependencies. Knowledge of the number of change-points is not required. The code is written in Go and interfaced with R.

Maintained by Lukas Zierahn. Last updated 3 years ago.

changepoints consistent ergodic long-range-dependence stationary time-series

10.5 match 2 stars 3.00 score 1 scripts

euanmcgonigle

TrendLSW:Wavelet Methods for Analysing Locally Stationary Time Series

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

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

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

8.5 match 1 stars 3.30 score 3 scripts

asael697

nortsTest:Assessing Normality of Stationary Process

Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages.

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

7.6 match 3 stars 3.69 score 33 scripts

tobiaskley

forecastSNSTS:Forecasting for Stationary and Non-Stationary Time Series

Methods to compute linear h-step ahead prediction coefficients based on localised and iterated Yule-Walker estimates and empirical mean squared and absolute prediction errors for the resulting predictors. Also, functions to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time series, and to verify an assumption from Kley et al. (2017), Preprint <http://personal.lse.ac.uk/kley/forecastSNSTS.pdf>.

Maintained by Tobias Kley. Last updated 7 years ago.

cpp

8.0 match 5 stars 3.40 score 9 scripts

siacus

sde:Simulation and Inference for Stochastic Differential Equations

Companion package to the book Simulation and Inference for Stochastic Differential Equations With R Examples, ISBN 978-0-387-75838-1, Springer, NY. *

Maintained by Stefano Maria Iacus. Last updated 2 years ago.

3.5 match 7.08 score 178 scripts 15 dependents

tnagler

svines:Stationary Vine Copula Models

Provides functionality to fit and simulate from stationary vine copula models for time series, see Nagler et al. (2022) <doi:10.1016/j.jeconom.2021.11.015>.

Maintained by Thomas Nagler. Last updated 3 months ago.

cpp

7.4 match 4 stars 3.30 score 6 scripts

cran

timsac:Time Series Analysis and Control Package

Functions for statistical analysis, prediction and control of time series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.

Maintained by Masami Saga. Last updated 2 years ago.

fortran

16.0 match 1 stars 1.48 score 1 dependents

spatstat

spatstat.explore:Exploratory Data Analysis for the 'spatstat' Family

Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

Maintained by Adrian Baddeley. Last updated 13 days ago.

cluster-detection confidence-intervals hypothesis-testing k-function roc-curves scan-statistics significance-testing simulation-envelopes spatial-analysis spatial-data-analysis spatial-sharpening spatial-smoothing spatial-statistics

2.3 match 1 stars 10.18 score 67 scripts 150 dependents

kisungyou

maotai:Tools for Matrix Algebra, Optimization and Inference

Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).

Maintained by Kisung You. Last updated 17 days ago.

openblas cpp openmp

4.1 match 8 stars 5.51 score 15 scripts 9 dependents

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.

1.8 match 300 stars 12.38 score 1.4k scripts 7 dependents

bastistician

hhh4contacts:Age-Structured Spatio-Temporal Models for Infectious Disease Counts

Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an age-structured spatio-temporal model for infectious disease counts. The approach is illustrated in a case study on norovirus gastroenteritis in Berlin, 2011-2015, by age group, city district and week, using additional contact data from the POLYMOD survey. This package contains the data and code to reproduce the results from the paper, see 'demo("hhh4contacts")'.

Maintained by Sebastian Meyer. Last updated 6 months ago.

6.6 match 3.28 score 19 scripts

jeffreyracine

np:Nonparametric Kernel Smoothing Methods for Mixed Data Types

Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.

Maintained by Jeffrey S. Racine. Last updated 2 months ago.

1.7 match 49 stars 12.64 score 672 scripts 44 dependents

sebkrantz

dfms:Dynamic Factor Models

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. The estimation options follow advances in the econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

Maintained by Sebastian Krantz. Last updated 15 days ago.

dynamic-factor-models time-series openblas cpp

3.7 match 32 stars 5.76 score 12 scripts

kurthornik

tseries:Time Series Analysis and Computational Finance

Time series analysis and computational finance.

Maintained by Kurt Hornik. Last updated 6 months ago.

fortran openblas

1.9 match 4 stars 11.29 score 10k scripts 289 dependents

markdrisser

convoSPAT:Convolution-Based Nonstationary Spatial Modeling

Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data. The nonstationary covariance function allows the user to specify the underlying correlation structure and which spatial dependence parameters should be allowed to vary over space: the anisotropy, nugget variance, and process variance. The parameters are estimated via maximum likelihood, using a local likelihood approach. Also provided are functions to fit stationary spatial models for comparison, calculate the Kriging predictor and standard errors, and create various plots to visualize nonstationarity.

Maintained by Mark D. Risser. Last updated 7 years ago.

7.8 match 2 stars 2.70 score 25 scripts

bioc

atSNP:Affinity test for identifying regulatory SNPs

atSNP performs affinity tests of motif matches with the SNP or the reference genomes and SNP-led changes in motif matches.

Maintained by Sunyoung Shin. Last updated 5 months ago.

software chipseq genomeannotation motifannotation visualization cpp

3.4 match 1 stars 5.73 score 36 scripts

helske

KFAS:Kalman Filter and Smoother for Exponential Family State Space Models

State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.

Maintained by Jouni Helske. Last updated 7 months ago.

dynamic-linear-model exponential-family fortran gaussian-models state-space time-series openblas

1.8 match 64 stars 10.97 score 242 scripts 16 dependents

ajmcneil

tscopula:Time Series Copula Models

Functions for the analysis of time series using copula models. The package is based on methodology described in the following references. McNeil, A.J. (2021) <doi:10.3390/risks9010014>, Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>, Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.

Maintained by Alexander McNeil. Last updated 1 months ago.

3.5 match 2 stars 5.53 score 12 scripts

smac-group

wv:Wavelet Variance

Provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <arXiv:1607.05858>.

Maintained by Stéphane Guerrier. Last updated 2 years ago.

signal-processing time-series wavelet-variance openblas cpp

3.3 match 17 stars 5.73 score 15 scripts 2 dependents

ctmm-initiative

ctmm:Continuous-Time Movement Modeling

Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic-process movement models to animal tracking data. The package is described in Calabrese et al (2016) <doi:10.1111/2041-210X.12559>, with models and methods based on those introduced and detailed in Fleming & Calabrese et al (2014) <doi:10.1086/675504>, Fleming et al (2014) <doi:10.1111/2041-210X.12176>, Fleming et al (2015) <doi:10.1103/PhysRevE.91.032107>, Fleming et al (2015) <doi:10.1890/14-2010.1>, Fleming et al (2016) <doi:10.1890/15-1607>, Péron & Fleming et al (2016) <doi:10.1186/s40462-016-0084-7>, Fleming & Calabrese (2017) <doi:10.1111/2041-210X.12673>, Péron et al (2017) <doi:10.1002/ecm.1260>, Fleming et al (2017) <doi:10.1016/j.ecoinf.2017.04.008>, Fleming et al (2018) <doi:10.1002/eap.1704>, Winner & Noonan et al (2018) <doi:10.1111/2041-210X.13027>, Fleming et al (2019) <doi:10.1111/2041-210X.13270>, Noonan & Fleming et al (2019) <doi:10.1186/s40462-019-0177-1>, Fleming et al (2020) <doi:10.1101/2020.06.12.130195>, Noonan et al (2021) <doi:10.1111/2041-210X.13597>, Fleming et al (2022) <doi:10.1111/2041-210X.13815>, Silva et al (2022) <doi:10.1111/2041-210X.13786>, Alston & Fleming et al (2023) <doi:10.1111/2041-210X.14025>.

Maintained by Christen H. Fleming. Last updated 12 days ago.

1.8 match 51 stars 10.54 score 534 scripts 4 dependents

ericgilleland

ismev:An Introduction to Statistical Modeling of Extreme Values

Functions to support the computations carried out in `An Introduction to Statistical Modeling of Extreme Values' by Stuart Coles. The functions may be divided into the following groups; maxima/minima, order statistics, peaks over thresholds and point processes.

Maintained by Eric Gilleland. Last updated 7 years ago.

3.5 match 1 stars 5.31 score 326 scripts 14 dependents

tianxia-jia

mcgf:Markov Chain Gaussian Fields Simulation and Parameter Estimation

Simulating and estimating (regime-switching) Markov chain Gaussian fields with covariance functions of the Gneiting class (Gneiting 2002) <doi:10.1198/016214502760047113>. It supports parameter estimation by weighted least squares and maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations.

Maintained by Tianxia Jia. Last updated 9 months ago.

3.8 match 1 stars 4.64 score 11 scripts

cran

AFR:Toolkit for Regression Analysis of Kazakhstan Banking Sector Data

Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data. The package includes functions from existing R packages adapted for banking sector of Kazakhstan. The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians.

Maintained by Sultan Zhaparov. Last updated 7 months ago.

5.4 match 3.18 score

rbgramacy

tgp:Bayesian Treed Gaussian Process Models

Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output. Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement. The latter supports derivative-free optimization of noisy black-box functions. For details and tutorials, see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) <doi:10.18637/jss.v033.i06>.

Maintained by Robert B. Gramacy. Last updated 7 months ago.

openblas cpp

2.2 match 9 stars 7.36 score 203 scripts 12 dependents

saviviro

uGMAR:Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models

Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR), Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, Mika Meitz, Daniel Preve, Pentti Saikkonen (2023) <doi:10.1080/03610926.2021.1916531>, Savi Virolainen (2022) <doi:10.1515/snde-2020-0060>.

Maintained by Savi Virolainen. Last updated 3 months ago.

3.3 match 1 stars 4.88 score 51 scripts

mikejareds

hermiter:Efficient Sequential and Batch Estimation of Univariate and Bivariate Probability Density Functions and Cumulative Distribution Functions along with Quantiles (Univariate) and Nonparametric Correlation (Bivariate)

Facilitates estimation of full univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation (bivariate) using Hermite series based estimators. These estimators are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 <doi:10.1214/17-EJS1245>, Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) <doi:10.1007/s00184-020-00785-z> and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) <doi:10.1016/j.jmva.2021.104783>.

Maintained by Michael Stephanou. Last updated 7 months ago.

cumulative-distribution-function kendall-correlation-coefficient online-algorithms probability-density-function quantile spearman-correlation-coefficient statistics streaming-algorithms streaming-data cpp

3.1 match 15 stars 5.11 score 17 scripts

egarpor

sdetorus:Statistical Tools for Toroidal Diffusions

Implementation of statistical methods for the estimation of toroidal diffusions. Several diffusive models are provided, most of them belonging to the Langevin family of diffusions on the torus. Specifically, the wrapped normal and von Mises processes are included, which can be seen as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of methods for approximate maximum likelihood estimation, organized in four blocks, is given: (i) based on the exact transition probability density, obtained as the numerical solution to the Fokker-Plank equation; (ii) based on wrapped pseudo-likelihoods; (iii) based on specific analytic approximations by wrapped processes; (iv) based on maximum likelihood of the stationary densities. The package allows the replicability of the results in García-Portugués et al. (2019) <doi:10.1007/s11222-017-9790-2>.

Maintained by Eduardo García-Portugués. Last updated 1 years ago.

circular-statistics inference maximum-likelihood reproducible-research sde statistics toroidal-data openblas cpp

3.9 match 6 stars 3.95 score 9 scripts 1 dependents

cran

mvLSW:Multivariate, Locally Stationary Wavelet Process Estimation

Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.

Maintained by Daniel Grose. Last updated 3 years ago.

8.7 match 1.78 score 2 dependents

ovgu-sh

desk:Didactic Econometrics Starter Kit

Written to help undergraduate as well as graduate students to get started with R for basic econometrics without the need to import specific functions and datasets from many different sources. Primarily, the package is meant to accompany the German textbook Auer, L.v., Hoffmann, S., Kranz, T. (2024, ISBN: 978-3-662-68263-0) from which the exercises cover all the topics from the textbook Auer, L.v. (2023, ISBN: 978-3-658-42699-6).

Maintained by Soenke Hoffmann. Last updated 12 months ago.

3.6 match 4.30 score 10 scripts

e-caron

slm:Stationary Linear Models

Provides statistical procedures for linear regression in the general context where the errors are assumed to be correlated. Different ways to estimate the asymptotic covariance matrix of the least squares estimators are available. Starting from this estimation of the covariance matrix, the confidence intervals and the usual tests on the parameters are modified. The functions of this package are very similar to those of 'lm': it contains methods such as summary(), plot(), confint() and predict(). The 'slm' package is described in the paper by E. Caron, J. Dedecker and B. Michel (2019), "Linear regression with stationary errors: the R package slm", arXiv preprint <arXiv:1906.06583>.

Maintained by Emmanuel Caron. Last updated 5 years ago.

11.6 match 1.28 score 19 scripts

jonesor

Rage:Life History Metrics from Matrix Population Models

Functions for calculating life history metrics using matrix population models ('MPMs'). Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.

Maintained by Owen Jones. Last updated 3 months ago.

1.7 match 12 stars 8.18 score 62 scripts 1 dependents

gpetris

dlm:Bayesian and Likelihood Analysis of Dynamic Linear Models

Provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.

Maintained by Giovanni Petris. Last updated 6 months ago.

openblas

1.8 match 9 stars 7.72 score 470 scripts 11 dependents

cran

extRemes:Extreme Value Analysis

General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) <doi: 10.18637/jss.v072.i08> and for bootstrapping, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.

Maintained by Eric Gilleland. Last updated 4 months ago.

3.6 match 2 stars 3.81 score 5 dependents

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

1.7 match 3 stars 7.84 score 108 scripts 23 dependents

smac-group

simts:Time Series Analysis Tools

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

Maintained by Stéphane Guerrier. Last updated 2 years ago.

rcpp rcpparmadillo simulation time-series timeseries timeseries-data openblas cpp

1.8 match 15 stars 7.68 score 59 scripts 4 dependents

cran

wavethresh:Wavelets Statistics and Transforms

Performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation.

Maintained by Guy Nason. Last updated 8 months ago.

2.3 match 5.90 score 41 dependents

cran

aTSA:Alternative Time Series Analysis

Contains some tools for testing, analyzing time series data and fitting popular time series models such as ARIMA, Moving Average and Holt Winters, etc. Most functions also provide nice and clear outputs like SAS does, such as identify, estimate and forecast, which are the same statements in PROC ARIMA in SAS.

Maintained by Debin Qiu. Last updated 1 years ago.

4.0 match 1 stars 3.29 score 5 dependents

rvlenth

rsm:Response-Surface Analysis

Provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis. A good reference on these methods is Chapter 10 of Wu, C-F J and Hamada, M (2009) "Experiments: Planning, Analysis, and Parameter Design Optimization" ISBN 978-0-471-69946-0. An early version of the package is documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.

Maintained by Russell Lenth. Last updated 8 days ago.

1.3 match 18 stars 10.32 score 192 scripts 8 dependents

pbastide

PhylogeneticEM:Automatic Shift Detection using a Phylogenetic EM

Implementation of the automatic shift detection method for Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on phylogenies. Some tools to handle equivalent shifts configurations are also available. See Bastide et al. (2017) <doi:10.1111/rssb.12206> and Bastide et al. (2018) <doi:10.1093/sysbio/syy005>.

Maintained by Paul Bastide. Last updated 2 months ago.

openblas cpp

1.9 match 17 stars 6.81 score 47 scripts

cran

SpatialBSS:Blind Source Separation for Multivariate Spatial Data

Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) <doi:10.1093/biomet/asz079>.

Maintained by Klaus Nordhausen. Last updated 6 days ago.

openblas cpp openmp

5.1 match 2.48 score 1 dependents

eliaskrainski

INLAspacetime:Spatial and Spatio-Temporal Models using 'INLA'

Prepare objects to implement models over spatial and spacetime domains with the 'INLA' package (<https://www.r-inla.org>). These objects contain data to for the 'cgeneric' interface in 'INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal models proposed in Lindgren et. al. (2023) <https://www.idescat.cat/sort/sort481/48.1.1.Lindgren-etal.pdf>. Details are provided in the available vignettes and from the URL bellow.

Maintained by Elias Teixeira Krainski. Last updated 19 days ago.

openblas

1.8 match 4 stars 7.05 score 56 scripts

doi-usgs

EGRET:Exploration and Graphics for RivEr Trends

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

Maintained by Laura DeCicco. Last updated 4 months ago.

usgs water-quality water-quality-data

1.1 match 90 stars 10.67 score 362 scripts 1 dependents

geobosh

sarima:Simulation and Prediction with Seasonal ARIMA Models

Functions, classes and methods for time series modelling with ARIMA and related models. The aim of the package is to provide consistent interface for the user. For example, a single function autocorrelations() computes various kinds of theoretical and sample autocorrelations. This is work in progress, see the documentation and vignettes for the current functionality. Function sarima() fits extended multiplicative seasonal ARIMA models with trends, exogenous variables and arbitrary roots on the unit circle, which can be fixed or estimated (for the algebraic basis for this see <arXiv:2208.05055>, a paper on the methodology is being prepared).

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

arima kalman-filter reg-sarima sarima sarimax seasonal time-series xarima openblas cpp

2.0 match 3 stars 6.09 score 112 scripts 1 dependents

loelschlaeger

oeli:Utilities for Developing Data Science Software

Some general helper functions that I (and maybe others) find useful when developing data science software.

Maintained by Lennart Oelschläger. Last updated 4 months ago.

openblas cpp

2.3 match 2 stars 5.38 score 1 scripts 4 dependents

adrian-bowman

sm:Smoothing Methods for Nonparametric Regression and Density Estimation

This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press.

Maintained by Adrian Bowman. Last updated 1 years ago.

fortran

1.7 match 1 stars 6.99 score 732 scripts 36 dependents

sportsdataverse

hoopR:Access Men's Basketball Play by Play Data

A utility to quickly obtain clean and tidy men's basketball play by play data. Provides functions to access live play by play and box score data from ESPN<https://www.espn.com> with shot locations when available. It is also a full NBA Stats API<https://www.nba.com/stats/> wrapper. It is also a scraping and aggregating interface for Ken Pomeroy's men's college basketball statistics website<https://kenpom.com>. It provides users with an active subscription the capability to scrape the website tables and analyze the data for themselves.

Maintained by Saiem Gilani. Last updated 1 years ago.

basketball college-basketball espn kenpom nba nba-analytics nba-api nba-data nba-statistics nba-stats nba-stats-api ncaa ncaa-basketball ncaa-bracket ncaa-players ncaa-ratings ncaam sportsdataverse

1.7 match 91 stars 6.93 score 261 scripts

rjdverse

rjd3toolkit:Utility Functions around 'JDemetra+ 3.0'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It provides functions allowing to model time series (create outlier regressors, user-defined calendar regressors, UCARIMA models...), to test the presence of trading days or seasonal effects and also to set specifications in pre-adjustment and benchmarking when using rjd3x13 or rjd3tramoseats.

Maintained by Tanguy Barthelemy. Last updated 5 months ago.

java jdemetra seasonal-adjustment time-series timeseries openjdk

2.0 match 6 stars 5.74 score 48 scripts 16 dependents

objornstad

ncf:Spatial Covariance Functions

Spatial (cross-)covariance and related geostatistical tools: the nonparametric (cross-)covariance function , the spline correlogram, the nonparametric phase coherence function, local indicators of spatial association (LISA), (Mantel) correlogram, (Partial) Mantel test.

Maintained by Ottar N. Bjornstad. Last updated 3 years ago.

1.7 match 5 stars 6.44 score 328 scripts 1 dependents

jacobnabe

DEPONS2R:Read, Plot and Analyse Output from the DEPONS Model

Methods for analyzing population dynamics and movement tracks simulated using the DEPONS model <https://www.depons.eu> (v.3.0), for manipulating input raster files, shipping routes and for analyzing sound propagated from ships.

Maintained by Jacob Nabe-Nielsen. Last updated 17 hours ago.

agent-based-modeling environmental-modelling marine-biology

3.7 match 2.95 score 4 scripts

squidlobster

castor:Efficient Phylogenetics on Large Trees

Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.

Maintained by Stilianos Louca. Last updated 5 months ago.

cpp

1.8 match 2 stars 5.75 score 450 scripts 9 dependents

eckleyi

LS2W:Locally Stationary Two-Dimensional Wavelet Process Estimation Scheme

Estimates two-dimensional local wavelet spectra.

Maintained by Idris Eckley. Last updated 2 years ago.

5.5 match 1.88 score 25 scripts 1 dependents

bpfaff

QRM:Provides R-Language Code to Examine Quantitative Risk Management Concepts

Provides functions/methods to accompany the book Quantitative Risk Management: Concepts, Techniques and Tools by Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.

Maintained by Bernhard Pfaff. Last updated 5 years ago.

cpp

2.3 match 4.53 score 181 scripts 5 dependents

cran

tseriesTARMA:Analysis of Nonlinear Time Series Through Threshold Autoregressive Moving Average Models (TARMA) Models

Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average (TARMA) models. It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) <doi:10.1080/07350015.2024.2412011>; tests for threshold effects, see Giannerini et al. JoE (2024) <doi:10.1016/j.jeconom.2023.01.004>, Goracci et al. Statistica Sinica (2023) <doi:10.5705/ss.202021.0120>, Angelini et al. (2024) <doi:10.48550/arXiv.2308.00444>; unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) <doi:10.5705/ss.202022.0125>.

Maintained by Simone Giannerini. Last updated 6 months ago.

fortran openblas

3.3 match 3.06 score

cran

GPoM:Generalized Polynomial Modelling

Platform dedicated to the Global Modelling technique. Its aim is to obtain ordinary differential equations of polynomial form directly from time series. It can be applied to single or multiple time series under various conditions of noise, time series lengths, sampling, etc. This platform is developped at the Centre d'Etudes Spatiales de la Biosphere (CESBIO), UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE. The developments were funded by the French program Les Enveloppes Fluides et l'Environnement (LEFE, MANU, projets GloMo, SpatioGloMo and MoMu). The French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged (projects Crops'IChaos and Musc & SlowFast). The method is described in the article : Mangiarotti S. and Huc M. (2019) <doi:10.1063/1.5081448>.

Maintained by Mireille Huc. Last updated 2 years ago.

3.4 match 2.90 score

nicolas-udec

LSEbootLS:Bootstrap Methods for Regression Models with Locally Stationary Errors

Implements bootstrap methods for linear regression models with errors following a time-varying process, focusing on approximating the distribution of the least-squares estimator for regression models with locally stationary errors. It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis. The methodology is based on the approach described in Ferreira et al. (2020), allowing errors to be locally approximated by stationary processes.

Maintained by Nicolas Loyola. Last updated 9 months ago.

3.6 match 2.70 score 1 scripts

travisbyrum

einet:Effective Information and Causal Emergence

Methods and utilities for causal emergence. Used to explore and compute various information theory metrics for networks, such as effective information, effectiveness and causal emergence.

Maintained by Travis Byrum. Last updated 3 years ago.

2.3 match 3 stars 4.26 score 12 scripts

bioc

deltaGseg:deltaGseg

Identifying distinct subpopulations through multiscale time series analysis

Maintained by Diana Low. Last updated 5 months ago.

proteomics timecourse visualization clustering

2.9 match 3.30 score 2 scripts

magnusdv

pedmut:Mutation Models for Pedigree Likelihood Computations

A collection of functions for modelling mutations in pedigrees with marker data, as used e.g. in likelihood computations with microsatellite data. Implemented models include equal, proportional and stepwise models, as well as random models for experimental work, and custom models allowing the user to apply any valid mutation matrix. Allele lumping is done following the lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922.

Maintained by Magnus Dehli Vigeland. Last updated 15 hours ago.

1.9 match 2 stars 5.03 score 5 scripts 18 dependents

bsnatr

tswge:Time Series for Data Science

Accompanies the texts Time Series for Data Science with R by Woodward, Sadler and Robertson & Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott. It is helpful for data analysis and for time series instruction.

Maintained by Bivin Sadler. Last updated 2 years ago.

3.4 match 2.70 score 496 scripts

sportsdataverse

wehoop:Access Women's Basketball Play by Play Data

A utility for working with women's basketball data. A scraping and aggregating interface for the WNBA Stats API <https://stats.wnba.com/> and ESPN's <https://www.espn.com> women's college basketball and WNBA statistics. It provides users with the capability to access the game play-by-plays, box scores, standings and results to analyze the data for themselves.

Maintained by Saiem Gilani. Last updated 8 months ago.

college-basketball espn espn-stats ncaa ncaa-basketball professional-basketball-data sportsdataverse wnba wnba-players wnba-stats womens-basketball

1.7 match 31 stars 5.40 score 54 scripts

funwithr

LongMemoryTS:Long Memory Time Series

Long Memory Time Series is a collection of functions for estimation, simulation and testing of long memory processes, spurious long memory processes and fractionally cointegrated systems.

Maintained by Christian Leschinski. Last updated 6 years ago.

openblas cpp

2.6 match 2 stars 3.40 score 42 scripts 1 dependents

saviviro

gmvarkit:Estimate Gaussian and Student's t Mixture Vector Autoregressive Models

Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen (2022) <doi:10.48550/arXiv.2109.13648>.

Maintained by Savi Virolainen. Last updated 3 months ago.

1.6 match 3 stars 5.32 score 45 scripts

xcding1212

Sie2nts:Sieve Methods for Non-Stationary Time Series

We provide functions for estimation and inference of locally-stationary time series using the sieve methods and bootstrapping procedure. In addition, it also contains functions to generate Daubechies and Coiflet wavelet by Cascade algorithm and to process data visualization.

Maintained by Xiucai Ding. Last updated 2 years ago.

3.4 match 2.48 score 2 scripts 1 dependents

cran

mvLSWimpute:Imputation Methods for Multivariate Locally Stationary Time Series

Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) <doi:10.1007/s11222-021-09998-2>.

Maintained by Matt Nunes. Last updated 3 years ago.

8.3 match 1.00 score

cran

ssaBSS:Stationary Subspace Analysis

Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).

Maintained by Markus Matilainen. Last updated 2 years ago.

8.1 match 1.00 score

cran

locits:Test of Stationarity and Localized Autocovariance

Provides test of second-order stationarity for time series (for dyadic and arbitrary-n length data). Provides localized autocovariance, with confidence intervals, for locally stationary (nonstationary) time series. See Nason, G P (2013) "A test for second-order stationarity and approximate confidence intervals for localized autocovariance for locally stationary time series." Journal of the Royal Statistical Society, Series B, 75, 879-904. <doi:10.1111/rssb.12015>.

Maintained by Guy Nason. Last updated 2 years ago.

4.1 match 1 stars 1.95 score 3 dependents

cran

TSA:Time Series Analysis

Contains R functions and datasets detailed in the book "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.

Maintained by Kung-Sik Chan. Last updated 3 years ago.

1.7 match 2 stars 4.58 score 5 dependents

handcock

RDS:Respondent-Driven Sampling

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

Maintained by Mark S. Handcock. Last updated 7 months ago.

2.0 match 1 stars 3.87 score 82 scripts 3 dependents

pythonhealthdatascience

treat.sim:Nelson's Treatment Centre Simulation in Simmer

A discrete-event simulation of a simple urgent care treatment centre simulation from Nelson (2013). Implemented in R Simmer. The model is packaged to allow for easy experimentation, summary of results, and implementation in other software such as a Shiny interface.

Maintained by Thomas Monks. Last updated 8 months ago.

computer-simulation discrete-event-simulation health open-modelling open-science open-source r-language reproducible-research simmer

1.7 match 2 stars 4.48 score 5 scripts

cran

popReconstruct:Reconstruct Human Populations of the Recent Past

Implements the Bayesian hierarchical model described by Wheldon, Raftery, Clark and Gerland (see: <doi:10.1080/01621459.2012.737729>) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.

Maintained by "Mark C. Wheldon". Last updated 5 years ago.

3.8 match 2.00 score

r-forge

TopKLists:Inference, Aggregation and Visualization for Top-K Ranked Lists

For multiple ranked input lists (full or partial) representing the same set of N objects, the package TopKLists offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results.

Maintained by Michael G. Schimek. Last updated 9 years ago.

1.9 match 4.05 score 37 scripts 1 dependents

okamumu

mapfit:PH/MAP Parameter Estimation

Estimation methods for phase-type distribution (PH) and Markovian arrival process (MAP) from empirical data (point and grouped data) and density function. The tool is based on the following researches: Okamura et al. (2009) <doi:10.1109/TNET.2008.2008750>, Okamura and Dohi (2009) <doi:10.1109/QEST.2009.28>, Okamura et al. (2011) <doi:10.1016/j.peva.2011.04.001>, Okamura et al. (2013) <doi:10.1002/asmb.1919>, Horvath and Okamura (2013) <doi:10.1007/978-3-642-40725-3_10>, Okamura and Dohi (2016) <doi:10.15807/jorsj.59.72>.

Maintained by Hiroyuki Okamura. Last updated 2 years ago.

openblas cpp

2.3 match 2 stars 3.34 score 22 scripts

skoestlmeier

monotonicity:Test for Monotonicity in Expected Asset Returns, Sorted by Portfolios

Test for monotonicity in financial variables sorted by portfolios. It is conventional practice in empirical research to form portfolios of assets ranked by a certain sort variable. A t-test is then used to consider the mean return spread between the portfolios with the highest and lowest values of the sort variable. Yet comparing only the average returns on the top and bottom portfolios does not provide a sufficient way to test for a monotonic relation between expected returns and the sort variable. This package provides nonparametric tests for the full set of monotonic patterns by Patton, A. and Timmermann, A. (2010) <doi:10.1016/j.jfineco.2010.06.006> and compares the proposed results with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.

Maintained by Siegfried Köstlmeier. Last updated 3 years ago.

monotonicity portfolio-analysis

2.0 match 10 stars 3.70 score 10 scripts

paulnorthrop

lax:Loglikelihood Adjustment for Extreme Value Models

Performs adjusted inferences based on model objects fitted, using maximum likelihood estimation, by the extreme value analysis packages 'eva' <https://cran.r-project.org/package=eva>, 'evd' <https://cran.r-project.org/package=evd>, 'evir' <https://cran.r-project.org/package=evir>, 'extRemes' <https://cran.r-project.org/package=extRemes>, 'fExtremes' <https://cran.r-project.org/package=fExtremes>, 'ismev' <https://cran.r-project.org/package=ismev>, 'mev' <https://cran.r-project.org/package=mev>, 'POT' <https://cran.r-project.org/package=POT> and 'texmex' <https://cran.r-project.org/package=texmex>. Adjusted standard errors and an adjusted loglikelihood are provided, using the 'chandwich' package <https://cran.r-project.org/package=chandwich> and the object-oriented features of the 'sandwich' package <https://cran.r-project.org/package=sandwich>. The adjustment is based on a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions, or for performing inferences that are robust to certain types of model misspecification. Univariate extreme value models, including regression models, are supported.

Maintained by Paul J. Northrop. Last updated 1 years ago.

clustered-data clusters composite-likelihood evd extreme-value-analysis extreme-value-statistics extremes independence-loglikelihood loglikelihood-adjustment mle pot regression regression-modelling robust sandwich sandwich-estimator

1.7 match 3 stars 4.29 score 13 scripts

adunaic

lpacf:Local Partial Autocorrelation Function Estimation for Locally Stationary Wavelet Processes

Provides the method for computing the local partial autocorrelation function for locally stationary wavelet time series from Killick, Knight, Nason, Eckley (2020) <doi:10.1214/20-EJS1748>.

Maintained by Rebecca Killick. Last updated 2 years ago.

5.0 match 1.48 score 2 scripts 1 dependents

kasselhingee

lacunaritycovariance:Gliding Box Lacunarity and Other Metrics for 2D Random Closed Sets

Functions for estimating the gliding box lacunarity (GBL), covariance, and pair-correlation of a random closed set (RACS) in 2D from a binary coverage map (e.g. presence-absence land cover maps). Contains a number of newly-developed covariance-based estimators of GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and balanced estimators, proposed by Picka (2000) <http://www.jstor.org/stable/1428408>, for covariance, centred covariance, and pair-correlation. Also contains methods for estimating contagion-like properties of RACS and simulating 2D Boolean models. Binary coverage maps are usually represented as raster images with pixel values of TRUE, FALSE or NA, with NA representing unobserved pixels. A demo for extracting such a binary map from a geospatial data format is provided. Binary maps may also be represented using polygonal sets as the foreground, however for most computations such maps are converted into raster images. The package is based on research conducted during the author's PhD studies.

Maintained by Kassel Liam Hingee. Last updated 1 years ago.

1.8 match 2 stars 4.00 score 3 scripts

haraldschellander

mevr:Fitting the Metastatistical Extreme Value Distribution MEVD

Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, <doi:10.1016/j.advwatres.2015.03.001>) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, <doi:10.1016/j.advwatres.2019.04.002>) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, <doi:10.1016/j.wace.2023.100601>). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references.

Maintained by Harald Schellander. Last updated 6 days ago.

extreme-value-statistics

1.8 match 2 stars 4.11 score 1 scripts

danielturek

BayesNSGP:Bayesian Analysis of Non-Stationary Gaussian Process Models

Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <arXiv:1702.00434v2>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the 'nimble' package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.

Maintained by Daniel Turek. Last updated 3 years ago.

5.2 match 2 stars 1.38 score 12 scripts

fdefalco

Achilles:Achilles Data Source Characterization

Automated Characterization of Health Information at Large-Scale Longitudinal Evidence Systems. Creates a descriptive statistics summary for an Observational Medical Outcomes Partnership Common Data Model standardized data source. This package includes functions for executing summary queries on the specified data source and exporting reporting content for use across a variety of Observational Health Data Sciences and Informatics community applications.

Maintained by Frank DeFalco. Last updated 2 years ago.

openjdk

1.8 match 4.06 score 115 scripts

gallegoj

tfarima:Transfer Function and ARIMA Models

Building customized transfer function and ARIMA models with multiple operators and parameter restrictions. Functions for model identification, model estimation (exact or conditional maximum likelihood), model diagnostic checking, automatic outlier detection, calendar effects, forecasting and seasonal adjustment. See Bell and Hillmer (1983) <doi:10.1080/01621459.1983.10478005>, Box, Jenkins, Reinsel and Ljung <ISBN:978-1-118-67502-1>, Box, Pierce and Newbold (1987) <doi:10.1080/01621459.1987.10478430>, Box and Tiao (1975) <doi:10.1080/01621459.1975.10480264>, Chen and Liu (1993) <doi:10.1080/01621459.1993.10594321>.

Maintained by Jose L. Gallego. Last updated 1 years ago.

openblas cpp openmp

1.8 match 2 stars 4.04 score 11 scripts

dnychka

LatticeKrig:Multi-Resolution Kriging Based on Markov Random Fields

Methods for the interpolation of large spatial datasets. This package uses a basis function approach that provides a surface fitting method that can approximate standard spatial data models. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) Moreover, the covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations and this package makes of the R spam package for sparse linear algebra. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific R functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary covariances and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation. See the 'LatticeKrigRPackage' GitHub repository for a vignette of this package. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research.

Maintained by Douglas Nychka. Last updated 6 months ago.

fortran

2.4 match 2.89 score 130 scripts 1 dependents

cran

mlrv:Long-Run Variance Estimation in Time Series Regression

Plug-in and difference-based long-run covariance matrix estimation for time series regression. Two applications of hypothesis testing are also provided. The first one is for testing for structural stability in coefficient functions. The second one is aimed at detecting long memory in time series regression. Lujia Bai and Weichi Wu (2024)<doi:10.3150/23-BEJ1680> Zhou Zhou and Wei Biao Wu(2010)<doi:10.1111/j.1467-9868.2010.00743.x> Jianqing Fan and Wenyang Zhang<doi:10.1214/aos/1017939139> Lujia Bai and Weichi Wu(2024)<doi:10.1093/biomet/asae013> Dimitris N. Politis, Joseph P. Romano, Michael Wolf(1999)<doi:10.1007/978-1-4612-1554-7> Weichi Wu and Zhou Zhou(2018)<doi:10.1214/17-AOS1582>.

Maintained by Lujia Bai. Last updated 8 months ago.

openblas cpp openmp

3.4 match 2.00 score

adunaic

forecastLSW:Forecasting Routines for Locally Stationary Wavelet Processes

Implementation to perform forecasting of locally stationary wavelet processes by examining the local second order structure of the time series.

Maintained by Rebecca Killick. Last updated 2 years ago.

6.7 match 1.00 score 3 scripts

cran

TSTutorial:Fitting and Predict Time Series Interactive Laboratory

Interactive laboratory of Time Series based in Box-Jenkins methodology.

Maintained by Alberto Lopez Moreno. Last updated 2 years ago.

3.3 match 2.00 score

shihao-yang

magi:MAnifold-Constrained Gaussian Process Inference

Provides fast and accurate inference for the parameter estimation problem in Ordinary Differential Equations, including the case when there are unobserved system components. Implements the MAGI method (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) <doi:10.1073/pnas.2020397118>. A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) <doi:10.18637/jss.v109.i04>.

Maintained by Shihao Yang. Last updated 9 months ago.

openblas cpp

1.8 match 3.67 score 47 scripts

gabrielodom

mvMonitoring:Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring

Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to data generated from a continuous-time multivariate industrial or natural process. Employ PCA-based dimension reduction to extract linear combinations of relevant features, reducing computational burdens. For a description of ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al. The multi-state application of ADPCA is from a manuscript under current revision entitled "Multi-State Multivariate Statistical Process Control" by Odom, Newhart, Cath, and Hering, and is expected to appear in Q1 of 2018.

Maintained by Gabriel Odom. Last updated 1 years ago.

1.3 match 4 stars 5.24 score 29 scripts

dschulz13

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

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

Maintained by Dominik Schulz. Last updated 2 years ago.

openblas cpp openmp

2.3 match 2.65 score 6 scripts 3 dependents

salvatirehbein

raytracing:Rossby Wave Ray Tracing

Rossby wave ray paths are traced from a determined source, specified wavenumber, and direction of propagation. "raytracing" also works with a set of experiments changing these parameters, making possible the identification of Rossby wave sources automatically. The theory used here is based on classical studies, such as Hoskins and Karoly (1981) <doi:10.1175/1520-0469(1981)038%3C1179:TSLROA%3E2.0.CO;2>, Karoly (1983) <doi:10.1016/0377-0265(83)90013-1>, Hoskins and Ambrizzi (1993) <doi:10.1175/1520-0469(1993)050%3C1661:RWPOAR%3E2.0.CO;2>, and Yang and Hoskins (1996) <doi:10.1175/1520-0469(1996)053%3C2365:PORWON%3E2.0.CO;2>.

Maintained by Amanda Rehbein. Last updated 3 years ago.

1.7 match 5 stars 3.40 score 5 scripts

mcthedwards

bsplinePsd:Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors

Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series. The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). <doi.org/10.1007/s11222-017-9796-9>.

Maintained by Matthew C. Edwards. Last updated 6 years ago.

cpp

2.1 match 1 stars 2.70 score 3 scripts

angusian

ltsa:Linear Time Series Analysis

Methods of developing linear time series modelling. Methods are given for loglikelihood computation, forecasting and simulation.

Maintained by A.I. McLeod. Last updated 7 months ago.

1.7 match 3.39 score 47 scripts 11 dependents

cran

Markovchart:Markov Chain-Based Cost-Optimal Control Charts

Functions for cost-optimal control charts with a focus on health care applications. Compared to assumptions in traditional control chart theory, here, we allow random shift sizes, random repair and random sampling times. The package focuses on X-bar charts with a sample size of 1 (representing the monitoring of a single patient at a time). The methods are described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni (2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019) <http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.

Maintained by Balazs Dobi. Last updated 3 years ago.

2.7 match 2.00 score

nicolasv-dev

drimmR:Estimation, Simulation and Reliability of Drifting Markov Models

Performs the drifting Markov models (DMM) which are non-homogeneous Markov models designed for modeling the heterogeneities of sequences in a more flexible way than homogeneous Markov chains or even hidden Markov models. In this context, we developed an R package dedicated to the estimation, simulation and the exact computation of associated reliability of drifting Markov models. The implemented methods are described in Vergne, N. (2008), <doi:10.2202/1544-6115.1326> and Barbu, V.S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8> .

Maintained by Nicolas Vergne. Last updated 4 years ago.

5.4 match 1.00 score

cran

NSAE:Nonstationary Small Area Estimation

Executes nonstationary Fay-Herriot model and nonstationary generalized linear mixed model for small area estimation.The empirical best linear unbiased predictor (EBLUP) under stationary and nonstationary Fay-Herriot models and empirical best predictor (EBP) under nonstationary generalized linear mixed model along with the mean squared error estimation are included. EBLUP for prediction of non-sample area is also included under both stationary and nonstationary Fay-Herriot models. This extension to the Fay-Herriot model that accounts for the presence of spatial nonstationarity was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2015) <doi:10.1093/jssam/smu026> and nonstationary generalized linear mixed model was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2017) <doi:10.1016/j.spasta.2017.01.004>. This package is dedicated to the memory of Dr. Hukum Chandra who passed away while the package creation was in progress.

Maintained by Saurav Guha. Last updated 3 years ago.

4.1 match 2 stars 1.30 score

cran

HiddenMarkov:Hidden Markov Models

Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini.

Maintained by David Harte. Last updated 2 months ago.

fortran

1.8 match 2.97 score 3 dependents

cran

LSWPlib:Simulation and Spectral Estimation of Locally Stationary Wavelet Packet Processes

Library of functions for the statistical analysis and simulation of Locally Stationary Wavelet Packet (LSWP) processes. The methods implemented by this library are described in Cardinali and Nason (2017) <doi:10.1111/jtsa.12230>.

Maintained by Alessandro Cardinali. Last updated 3 years ago.

5.1 match 1.00 score

jiri-dvorak

NTSS:Nonparametric Tests in Spatial Statistics

Nonparametric test of independence between a pair of spatial objects (random fields, point processes) based on random shifts with torus or variance correction. See Mrkvička et al. (2021) <doi:10.1016/j.spasta.2020.100430>, Dvořák et al. (2022) <doi:10.1111/insr.12503>, Dvořák and Mrkvička (2022) <arxiv:2210.05424>.

Maintained by Jiří Dvořák. Last updated 2 years ago.

1.7 match 2.70 score

darkeyes

VLTimeCausality:Variable-Lag Time Series Causality Inference Framework

A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) <doi:10.1145/3441452> when referring to this package in publications.

Maintained by Chainarong Amornbunchornvej. Last updated 10 months ago.

causal-inference granger-causality time-series time-series-analysis transfer-entropy

0.8 match 54 stars 5.77 score 11 scripts

cran

costat:Time Series Costationarity Determination

Contains functions that can determine whether a time series is second-order stationary or not (and hence evidence for locally stationarity). Given two non-stationary series (i.e. locally stationary series) this package can then discover time-varying linear combinations that are second-order stationary. Cardinali, A. and Nason, G.P. (2013) <doi:10.18637/jss.v055.i01>.

Maintained by Guy Nason. Last updated 2 years ago.

4.4 match 1.00 score

andrewzm

FRK:Fixed Rank Kriging

A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.

Maintained by Andrew Zammit-Mangion. Last updated 6 months ago.

cpp

0.5 match 71 stars 8.70 score 188 scripts 1 dependents

cran

localScore:Package for Sequence Analysis by Local Score

Functionalities for calculating the local score and calculating statistical relevance (p-value) to find a local Score in a sequence of given distribution (S. Mercier and J.-J. Daudin (2001) <https://hal.science/hal-00714174/>) ; S. Karlin and S. Altschul (1990) <https://pmc.ncbi.nlm.nih.gov/articles/PMC53667/> ; S. Mercier, D. Cellier and F. Charlot (2003) <https://hal.science/hal-00937529v1/> ; A. Lagnoux, S. Mercier and P. Valois (2017) <doi:10.1093/bioinformatics/btw699> ).

Maintained by David Robelin. Last updated 1 months ago.

cpp

1.9 match 2.30 score

cran

sparseDFM:Estimate Dynamic Factor Models with Sparse Loadings

Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) <doi:10.1198/016214502388618960>, 2Stage Giannone et al. (2008) <doi:10.1016/j.jmoneco.2008.05.010>, expectation-maximisation (EM) Banbura and Modugno (2014) <doi:10.1002/jae.2306>, and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) <arXiv:2303.11892>. Options to use classic multivariate Kalman filter and smoother (KFS) equations from Shumway and Stoffer (1982) <doi:10.1111/j.1467-9892.1982.tb00349.x> or fast univariate KFS equations from Koopman and Durbin (2000) <doi:10.1111/1467-9892.00186>, and options for independent and identically distributed (IID) white noise or auto-regressive (AR(1)) idiosyncratic errors. Algorithms coded in 'C++' and linked to R via 'RcppArmadillo'.

Maintained by Alex Gibberd. Last updated 2 years ago.

openblas cpp openmp

1.9 match 1 stars 2.30 score

wbnicholson

DTMCPack:Suite of Functions Related to Discrete-Time Discrete-State Markov Chains

A series of functions which aid in both simulating and determining the properties of finite, discrete-time, discrete state markov chains. Two functions (DTMC, MultDTMC) produce n iterations of a Markov Chain(s) based on transition probabilities and an initial distribution. The function FPTime determines the first passage time into each state. The function statdistr determines the stationary distribution of a Markov Chain.

Maintained by William Nicholson. Last updated 3 years ago.

2.5 match 1.71 score 32 scripts

barryrowlingson

spatialkernel:Non-Parametric Estimation of Spatial Segregation in a Multivariate Point Process

Edge-corrected kernel density estimation and binary kernel regression estimation for multivariate spatial point process data. For details, see Diggle, P.J., Zheng, P. and Durr, P. A. (2005) <doi:10.1111/j.1467-9876.2005.05373.x>.

Maintained by Virgilio Gómez-Rubio. Last updated 4 years ago.

fortran

1.6 match 2.54 score 23 scripts

gangcai

pGRN:Single-Cell RNA Sequencing Pseudo-Time Based Gene Regulatory Network Inference

Inference and visualize gene regulatory network based on single-cell RNA sequencing pseudo-time information.

Maintained by Gangcai Xie. Last updated 2 years ago.

2.0 match 2.00 score 3 scripts

cadam00

corbouli:Corbae-Ouliaris Frequency Domain Filtering

Corbae-Ouliaris frequency domain filtering. According to Corbae and Ouliaris (2006) <doi:10.1017/CBO9781139164863.008>, this is a solution for extracting cycles from time series, like business cycles etc. when filtering. This method is valid for both stationary and non-stationary time series.

Maintained by Christos Adam. Last updated 3 months ago.

frequency-domain-filtering time-series-analysis

0.8 match 4.70 score 7 scripts

thomasvigie

sgmodel:Solves a Generic Stochastic Growth Model with a Representative Agent

It computes the solutions to a generic stochastic growth model for a given set of user supplied parameters. It includes the solutions to the model, plots of the solution, a summary of the features of the model, a function that covers different types of consumption preferences, and a function that computes the moments of a Markov process. Merton, Robert C (1971) <doi:10.1016/0022-0531(71)90038-X>, Tauchen, George (1986) <doi:10.1016/0165-1765(86)90168-0>, Wickham, Hadley (2009, ISBN:978-0-387-98140-6 ).

Maintained by Thomas Vigie. Last updated 5 years ago.

1.3 match 3.00 score 8 scripts

rogih

tsqn:Applications of the Qn Estimator to Time Series (Univariate and Multivariate)

Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) <doi:10.1080/01621459.1993.10476408> to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) <doi:10.1111/1467-9892.00203>, <doi:10.1006/jmva.2000.1942> and Cotta (2017) <doi:10.13140/RG.2.2.14092.10883> are provided. The robust pseudo-periodogram of Molinares et. al. (2009) <doi:10.1016/j.jspi.2008.12.014> is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.

Maintained by Higor Cotta. Last updated 6 years ago.

1.6 match 2.23 score 17 scripts

cran

freqdom:Frequency Domain Based Analysis: Dynamic PCA

Implementation of dynamic principal component analysis (DPCA), simulation of VAR and VMA processes and frequency domain tools. These frequency domain methods for dimensionality reduction of multivariate time series were introduced by David Brillinger in his book Time Series (1974). We follow implementation guidelines as described in Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.

Maintained by Kidzinski L.. Last updated 12 months ago.

1.7 match 2.08 score 4 dependents

prabhanjan-tattar

ACSWR:A Companion Package for the Book "A Course in Statistics with R"

A book designed to meet the requirements of masters students. Tattar, P.N., Suresh, R., and Manjunath, B.G. "A Course in Statistics with R", J. Wiley, ISBN 978-1-119-15272-9.

Maintained by Prabhanjan Tattar. Last updated 10 years ago.

1.7 match 2.03 score 106 scripts

ehanks

ctmcmove:Modeling Animal Movement with Continuous-Time Discrete-Space Markov Chains

Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.

Maintained by Ephraim Hanks. Last updated 3 months ago.

1.9 match 1 stars 1.78 score 30 scripts

angusian

artfima:ARTFIMA Model Estimation

Fit and simulate ARTFIMA. Theoretical autocovariance function and spectral density function for stationary ARTFIMA.

Maintained by A.I. McLeod. Last updated 9 years ago.

2.5 match 1.30 score 20 scripts

cran

TSSS:Time Series Analysis with State Space Model

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

Maintained by Masami Saga. Last updated 2 years ago.

fortran

1.8 match 2 stars 1.78 score

nicebread

RSA:Response Surface Analysis

Advanced response surface analysis. The main function RSA computes and compares several nested polynomial regression models (full second- or third-order polynomial, shifted and rotated squared difference model, rising ridge surfaces, basic squared difference model, asymmetric or level-dependent congruence effect models). The package provides plotting functions for 3d wireframe surfaces, interactive 3d plots, and contour plots. Calculates many surface parameters (a1 to a5, principal axes, stationary point, eigenvalues) and provides standard, robust, or bootstrapped standard errors and confidence intervals for them.

Maintained by Felix Schönbrodt. Last updated 12 months ago.

0.5 match 17 stars 6.30 score 26 scripts 1 dependents

miicteam

miic:Learning Causal or Non-Causal Graphical Models Using Information Theory

Multivariate Information-based Inductive Causation, better known by its acronym MIIC, is a causal discovery method, based on information theory principles, which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The recent more interpretable MIIC extension (iMIIC) further distinguishes genuine causes from putative and latent causal effects, while scaling to very large datasets (hundreds of thousands of samples). Since the version 2.0, MIIC also includes a temporal mode (tMIIC) to learn temporal causal graphs from stationary time series data. MIIC has been applied to a wide range of biological and biomedical data, such as single cell gene expression data, genomic alterations in tumors, live-cell time-lapse imaging data (CausalXtract), as well as medical records of patients. MIIC brings unique insights based on causal interpretation and could be used in a broad range of other data science domains (technology, climatology, economy, ...). For more information, you can refer to: Simon et al., eLife 2024, <doi:10.1101/2024.02.06.579177>, Ribeiro-Dantas et al., iScience 2024, <doi:10.1016/j.isci.2024.109736>, Cabeli et al., NeurIPS 2021, <https://why21.causalai.net/papers/WHY21_24.pdf>, Cabeli et al., Comput. Biol. 2020, <doi:10.1371/journal.pcbi.1007866>, Li et al., NeurIPS 2019, <https://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets>, Verny et al., PLoS Comput. Biol. 2017, <doi:10.1371/journal.pcbi.1005662>, Affeldt et al., UAI 2015, <https://auai.org/uai2015/proceedings/papers/293.pdf>. Changes from the previous 1.5.3 release on CRAN are available at <https://github.com/miicTeam/miic_R_package/blob/master/NEWS.md>.

Maintained by Franck Simon. Last updated 7 months ago.

cpp openmp

0.5 match 27 stars 6.22 score 69 scripts

jtimonen

lgpr:Longitudinal Gaussian Process Regression

Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

Maintained by Juho Timonen. Last updated 7 months ago.

bayesian-inference gaussian-processes longitudinal-data stan cpp

0.5 match 25 stars 5.94 score 69 scripts

cran

bentcableAR:Bent-Cable Regression for Independent Data or Autoregressive Time Series

Included are two main interfaces, bentcable.ar() and bentcable.dev.plot(), for fitting and diagnosing bent-cable regressions for autoregressive time-series data (Chiu and Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time series or otherwise - Chiu, Lockhart and Routledge 2006, <doi:10.1198/016214505000001177>). Some components in the package can also be used as stand-alone functions. The bent cable (linear-quadratic-linear) generalizes the broken stick (linear-linear), which is also handled by this package. Version 0.2 corrected a glitch in the computation of confidence intervals for the CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved robustness of the error-message producing mechanism. Version 0.3.1 improves the NAMESPACE file of the package. It is the author's intention to distribute any future updates via GitHub.

Maintained by Grace Chiu. Last updated 3 years ago.

3.0 match 1 stars 1.00 score

ucl

rmcmc:Robust Markov Chain Monte Carlo Methods

Functions for simulating Markov chains using the Barker proposal to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The Barker proposal, described in Livingstone and Zanella (2022) <doi:10.1111/rssb.12482>, is a gradient-based MCMC algorithm inspired by the Barker accept-reject rule. It combines the robustness of simpler MCMC schemes, such as random-walk Metropolis, with the efficiency of gradient-based methods, such as the Metropolis adjusted Langevin algorithm. The key function provided by the package is sample_chain(), which allows sampling a Markov chain with a specified target distribution as its stationary distribution. The chain is sampled by generating proposals and accepting or rejecting them using a Metropolis-Hasting acceptance rule. During an initial warm-up stage, the parameters of the proposal distribution can be adapted, with adapters available to both: tune the scale of the proposals by coercing the average acceptance rate to a target value; tune the shape of the proposals to match covariance estimates under the target distribution. As well as the default Barker proposal, the package also provides implementations of alternative proposal distributions, such as (Gaussian) random walk and Langevin proposals. Optionally, if 'BridgeStan's R interface <https://roualdes.github.io/bridgestan/latest/languages/r.html>, available on GitHub <https://github.com/roualdes/bridgestan>, is installed, then 'BridgeStan' can be used to specify the target distribution to sample from.

Maintained by Matthew M. Graham. Last updated 30 days ago.

approximate-inference mcmc

0.5 match 5 stars 5.85 score 8 scripts

mazamascience

MazamaLocationUtils:Manage Spatial Metadata for Known Locations

Utility functions for discovering and managing metadata associated with spatially unique "known locations". Applications include all fields of environmental monitoring (e.g. air and water quality) where data are collected at stationary sites.

Maintained by Jonathan Callahan. Last updated 4 months ago.

0.5 match 5.64 score 108 scripts

amutak

cosinor2:Extended Tools for Cosinor Analysis of Rhythms

Statistical procedures for calculating population–mean cosinor, non–stationary cosinor, estimation of best–fitting period, tests of population rhythm differences and more. See Cornélissen, G. (2014). <doi:10.1186/1742-4682-11-16>.

Maintained by Augustin Mutak. Last updated 6 years ago.

0.5 match 5 stars 5.63 score 19 scripts 5 dependents

brianbader

eva:Extreme Value Analysis with Goodness-of-Fit Testing

Goodness-of-fit tests for selection of r in the r-largest order statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the Generalized Pareto distribution (GPD). Random number generation and density functions for the GEVr distribution. Profile likelihood for return level estimation using the GEVr and Generalized Pareto distributions. P-value adjustments for sequential, multiple testing error control. Non-stationary fitting of GEVr and GPD.

Maintained by Brian Bader. Last updated 5 years ago.

0.5 match 3 stars 5.67 score 52 scripts 1 dependents

cran

nsarfima:Methods for Fitting and Simulating Non-Stationary ARFIMA Models

Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.

Maintained by Benjamin Groebe. Last updated 5 years ago.

2.9 match 1 stars 1.00 score

tmcd82070

Rdistance:Density and Abundance from Distance-Sampling Surveys

Distance-sampling (<doi:10.1007/978-3-319-19219-2>) estimates density and abundance of survey targets (e.g., animals) when detection probability declines with distance. Distance-sampling is popular in ecology, especially when survey targets are observed from aerial platforms (e.g., airplane or drone), surface vessels (e.g., boat or truck), or along walking transects. Distance-sampling includes line-transect studies that measure observation distances as the closest approach of the sample route (transect) to the target (i.e., perpendicular off-transect distance), and point-transect studies that measure observation distances from stationary observers to the target (i.e., radial distance). The routines included here fit smooth (parametric) curves to histograms of observation distances and use those functions to compute effective sampling distances, density of targets in the surveyed area, and abundance of targets in a surrounding study area. Curve shapes include the half-normal, hazard rate, and negative exponential functions. Physical measurement units are required and used throughout to ensure density is reported correctly. The help files are extensive and have been vetted by multiple authors.

Maintained by Trent McDonald. Last updated 8 hours ago.

0.5 match 9 stars 5.56 score 40 scripts

noeliaof

SEI:Calculating Standardised Indices

Convert a time series of observations to a time series of standardised indices that can be used to monitor variables on a common and probabilistically interpretable scale. The indices can be aggregated and rescaled to different time scales, visualised using plot capabilities, and calculated using a range of distributions. This includes flexible non-parametric and non-stationary methods.

Maintained by Sam Allen. Last updated 8 months ago.

0.5 match 3 stars 4.86 score 12 scripts

paulnorthrop

exdex:Estimation of the Extremal Index

Performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) <doi:10.1007/s10687-015-0221-5> and Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>. Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) <doi:10.1111/1467-9868.00401>). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) <doi:10.1007/s10687-007-0034-2>, the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and a similar approach of Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3> that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided.

Maintained by Paul J. Northrop. Last updated 11 months ago.

block-maxima extremal-index extreme extreme-value-statistics extremes inference maxima semiparametric semiparametric-estimation semiparametric-maxima-estimators theta threshold value cpp

0.5 match 4.92 score 11 scripts 5 dependents

paulnorthrop

lite:Likelihood-Based Inference for Time Series Extremes

Performs likelihood-based inference for stationary time series extremes. The general approach follows Fawcett and Walshaw (2012) <doi:10.1002/env.2133>. Marginal extreme value inferences are adjusted for cluster dependence in the data using the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood for the model parameters. A log-likelihood for the extremal index is produced using the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make inferences about extreme values. Both maximum likelihood and Bayesian approaches are available.

Maintained by Paul J. Northrop. Last updated 9 months ago.

clustered extremal-index extreme-value-statistics extremes frequentist generalised-pareto inference likelihood log-likelihood threshold time-series

0.5 match 3 stars 4.56 score 12 scripts

manuelabrunner

PRSim:Stochastic Simulation of Streamflow Time Series using Phase Randomization

Provides a simulation framework to simulate streamflow time series with similar main characteristics as observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform or the phases of the wavelet transform. The function prsim() is applicable to single site simulation and uses the Fourier transform. The function prsim.wave() extends the approach to multiple sites and is based on the complex wavelet transform. The function prsim.weather() extends the approach to multiple variables for weather generation. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of the simulation approach for single sites and an application example can be found in Brunner et al. (2019) <doi:10.5194/hess-23-3175-2019>. A detailed description and evaluation of the wavelet-based multi-site approach can be found in Brunner and Gilleland (2020) <doi:10.5194/hess-24-3967-2020>. A detailed description and evaluation of the multi-variable and multi-site weather generator can be found in Brunner et al. (2021) <doi:10.5194/esd-12-621-2021>. A detailed description and evaluation of the non-stationary streamflow generator can be found in Brunner and Gilleland (2024) <doi:10.1029/2023EF004238>.

Maintained by Manuela Brunner. Last updated 12 months ago.

2.3 match 1.00 score 8 scripts

gpfda

GPFDA:Gaussian Process for Functional Data Analysis

Functionalities for modelling functional data with multidimensional inputs, multivariate functional data, and non-separable and/or non-stationary covariance structure of function-valued processes. In addition, there are functionalities for functional regression models where the mean function depends on scalar and/or functional covariates and the covariance structure depends on functional covariates. The development version of the package can be found on <https://github.com/gpfda/GPFDA-dev>.

Maintained by Evandro Konzen. Last updated 2 years ago.

openblas cpp openmp

0.5 match 1 stars 3.81 score 36 scripts 1 dependents

mlysy

LMN:Inference for Linear Models with Nuisance Parameters

Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.

Maintained by Martin Lysy. Last updated 3 years ago.

cpp

0.5 match 1 stars 3.70 score 8 scripts

ca4wa

adpss:Design and Analysis of Locally or Globally Efficient Adaptive Designs

Provides the functions for planning and conducting a clinical trial with adaptive sample size determination. Maximal statistical efficiency will be exploited even when dramatic or multiple adaptations are made. Such a trial consists of adaptive determination of sample size at an interim analysis and implementation of frequentist statistical test at the interim and final analysis with a prefixed significance level. The required assumptions for the stage-wise test statistics are independent and stationary increments and normality. Predetermination of adaptation rule is not required.

Maintained by Kosuke Kashiwabara. Last updated 2 years ago.

cpp

0.5 match 3.70 score 6 scripts

hamedhm

DREGAR:Regularized Estimation of Dynamic Linear Regression in the Presence of Autocorrelated Residuals (DREGAR)

A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.

Maintained by Hamed Haselimashhadi. Last updated 8 years ago.

1.9 match 1 stars 1.00 score 4 scripts

ankita8985

UnitStat:Performs Unit Root Test Statistics

A test to understand the stability of the underlying stochastic data. Helps the user’s understand whether the random variable under consideration is stationary or non-stationary without any manual interpretation of the results. It further ensures to check all the prerequisites and assumptions which are underlying the unit root test statistics and if the underlying data is found to be non-stationary in all the 4 lags the function diagnoses the input data and returns with an optimised solution on the same.

Maintained by Ankita Sharma. Last updated 4 years ago.

0.9 match 2.00 score 2 scripts

kisungyou

Rlinsolve:Iterative Solvers for (Sparse) Linear System of Equations

Solving a system of linear equations is one of the most fundamental computational problems for many fields of mathematical studies, such as regression problems from statistics or numerical partial differential equations. We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel, Successive Over-Relaxation and SSOR methods. Nonstationary, also known as Krylov subspace methods are also provided. Sparse matrix computation is also supported in that solving large and sparse linear systems can be manageable using 'Matrix' package along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003) <doi:10.1137/1.9780898718003>.

Maintained by Kisung You. Last updated 2 years ago.

openblas cpp openmp

0.5 match 4 stars 3.56 score 30 scripts 1 dependents

cran

IndTestPP:Tests of Independence and Analysis of Dependence Between Point Processes in Time

It provides a general framework to analyse dependence between point processes in time. It includes parametric and non-parametric tests to study independence, and functions for generating and analysing different types of dependence.

Maintained by Ana C. Cebrian. Last updated 5 years ago.

1.8 match 1.00 score

cran

portes:Portmanteau Tests for Time Series Models

Contains common univariate and multivariate portmanteau test statistics for time series models. These tests are based on using asymptotic distributions such as chi-square distribution and based on using the Monte Carlo significance tests. Also, it can be used to simulate from univariate and multivariate seasonal time series models.

Maintained by Esam Mahdi. Last updated 2 years ago.

1.8 match 1.00 score

cran

LS2Wstat:A Multiscale Test of Spatial Stationarity for LS2W Processes

Wavelet-based methods for testing stationarity and quadtree segmenting of images, see Taylor et al (2014) <doi:10.1080/00401706.2013.823890>.

Maintained by Matt Nunes. Last updated 2 years ago.

1.8 match 1 stars 1.00 score

tomstindl

MRHawkes:Multivariate Renewal Hawkes Process

Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) self-exciting process, with given immigrant hazard rate functions and offspring density function. Calculate the likelihood of a MRHawkes process with given hazard rate functions and offspring density function for an (increasing) sequence of event times. Calculate the Rosenblatt residuals of the event times. Predict future event times based on observed event times up to a given time. For details see Stindl and Chen (2018) <doi:10.1016/j.csda.2018.01.021>.

Maintained by Tom Stindl. Last updated 7 years ago.

1.7 match 1.00 score 10 scripts

claudiofronterre

RiskMap:Geo-Statistical Modeling of Spatially Referenced Data

Provides functions for geo-statistical analysis of both continuous and count data using maximum likelihood methods. The models implemented in the package use stationary Gaussian processes with Matern correlation function to carry out spatial prediction in a geographical area of interest. The underpinning theory of the methods implemented in the package are found in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7).

Maintained by Emanuele Giorgi. Last updated 7 months ago.

0.5 match 3.18 score 5 scripts

rainers48

tsapp:Time Series, Analysis and Application

Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.

Maintained by Rainer Schlittgen. Last updated 3 years ago.

1.6 match 1.00 score 1 scripts

cran

constrainedKriging:Constrained, Covariance-Matching Constrained and Universal Point or Block Kriging

Provides functions for efficient computation of non-linear spatial predictions with local change of support (Hofer, C. and Papritz, A. (2011) "constrainedKriging: An R-package for customary, constrained and covariance-matching constrained point or block kriging" <doi:10.1016/j.cageo.2011.02.009>). This package supplies functions for two-dimensional spatial interpolation by constrained (Cressie, N. (1993) "Aggregation in geostatistical problems" <doi:10.1007/978-94-011-1739-5_3>), covariance-matching constrained (Aldworth, J. and Cressie, N. (2003) "Prediction of nonlinear spatial functionals" <doi:10.1016/S0378-3758(02)00321-X>) and universal (external drift) Kriging for points or blocks of any shape from data with a non-stationary mean function and an isotropic weakly stationary covariance function. The linear spatial interpolation methods, constrained and covariance-matching constrained Kriging, provide approximately unbiased prediction for non-linear target values under change of support. This package extends the range of tools for spatial predictions available in R and provides an alternative to conditional simulation for non-linear spatial prediction problems with local change of support.

Maintained by Andreas Papritz. Last updated 2 months ago.

0.8 match 2.08 score 1 dependents

tillahoffmann

gptoolsStan:Gaussian Processes on Graphs and Lattices in 'Stan'

Gaussian processes are flexible distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. This package implements two methods for scaling Gaussian process inference in 'Stan'. First, a sparse approximation of the likelihood that is generally applicable and, second, an exact method for regularly spaced data modeled by stationary kernels using fast Fourier methods. Utility functions are provided to compile and fit 'Stan' models using the 'cmdstanr' interface. References: Hoffmann and Onnela (2025) <doi:10.18637/jss.v112.i02>.

Maintained by Till Hoffmann. Last updated 15 days ago.

0.5 match 3.00 score 6 scripts

mondrus96

fabisearch:Change Point Detection in High-Dimensional Time Series Networks

Implementation of the Factorized Binary Search (FaBiSearch) methodology for the estimation of the number and the location of multiple change points in the network (or clustering) structure of multivariate high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI) data. FaBiSearch uses non-negative matrix factorization (NMF), an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. It requires minimal assumptions. Lastly, we provide interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. This function can be conveniently used with any node coordinate atlas, and nodes can be color coded according to community membership, if applicable. The output is an elegantly displayed network laid over a cortical surface, which can be rotated in the 3-dimensional space. The main routines of the package are detect.cps(), for multiple change point detection, est.net(), for estimating a network between stationary multivariate time series, net.3dplot(), for plotting the estimated functional connectivity networks, and opt.rank(), for finding the optimal rank in NMF for a given data set. The functions have been extensively tested on simulated multivariate high-dimensional time series data and fMRI data. For details on the FaBiSearch methodology, please see Ondrus et al. (2021) <arXiv:2103.06347>. For a more detailed explanation and applied examples of the fabisearch package, please see Ondrus and Cribben (2022), preprint.

Maintained by Martin Ondrus. Last updated 8 months ago.

0.5 match 1 stars 3.00 score 2 scripts

cran

evmix:Extreme Value Mixture Modelling, Threshold Estimation and Boundary Corrected Kernel Density Estimation

The usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the 'evd' package is provided, so that users can safely interchange most code.

Maintained by Carl Scarrott. Last updated 6 years ago.

0.5 match 2 stars 2.85 score 7 dependents

paciorek

climextRemes:Tools for Analyzing Climate Extremes

Functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from 'extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) <doi:10.1016/j.wace.2018.01.002>.

Maintained by Christopher Paciorek. Last updated 1 years ago.

0.5 match 2.85 score 14 scripts

h0h1

autoFRK:Automatic Fixed Rank Kriging

Automatic fixed rank kriging for (irregularly located) spatial data using a class of basis functions with multi-resolution features and ordered in terms of their resolutions. The model parameters are estimated by maximum likelihood (ML) and the number of basis functions is determined by Akaike's information criterion (AIC). For spatial data with either one realization or independent replicates, the ML estimates and AIC are efficiently computed using their closed-form expressions when no missing value occurs. Details regarding the basis function construction, parameter estimation, and AIC calculation can be found in Tzeng and Huang (2018) <doi:10.1080/00401706.2017.1345701>. For data with missing values, the ML estimates are obtained using the expectation- maximization algorithm. Apart from the number of basis functions, there are no other tuning parameters, making the method fully automatic. Users can also include a stationary structure in the spatial covariance, which utilizes 'LatticeKrig' package.

Maintained by ShengLi Tzeng. Last updated 4 years ago.

cpp openmp

0.5 match 2.71 score 103 scripts

xinxiong0238

vccp:Vine Copula Change Point Detection in Multivariate Time Series

Implements the Vine Copula Change Point (VCCP) methodology for the estimation of the number and location of multiple change points in the vine copula structure of multivariate time series. The method uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between time series including tail, symmetric and asymmetric dependence. The functions have been extensively tested on simulated multivariate time series data and fMRI data. For details on the VCCP methodology, please see Xiong & Cribben (2021).

Maintained by Xin Xiong. Last updated 4 years ago.

0.5 match 1 stars 2.70 score 1 scripts

tsprass

DCCA:Detrended Fluctuation and Detrended Cross-Correlation Analysis

A collection of functions to perform Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA). This package implements the results presented in Prass, T.S. and Pumi, G. (2019). "On the behavior of the DFA and DCCA in trend-stationary processes" <arXiv:1910.10589>.

Maintained by Taiane Schaedler Prass. Last updated 5 years ago.

fortran openblas

0.5 match 2.48 score 6 scripts 1 dependents

xcding1212

SIMle:Estimation and Inference for General Time Series Regression

We provide functions for estimation and inference of nonlinear and non-stationary time series regression using the sieve methods and bootstrapping procedure.

Maintained by Xiucai Ding. Last updated 1 years ago.

0.5 match 2.30 score 3 scripts

cran

CADFtest:A Package to Perform Covariate Augmented Dickey-Fuller Unit Root Tests

Hansen's (1995) Covariate-Augmented Dickey-Fuller (CADF) test. The only required argument is y, the Tx1 time series to be tested. If no stationary covariate X is passed to the procedure, then an ordinary ADF test is performed. The p-values of the test are computed using the procedure illustrated in Lupi (2009).

Maintained by Claudio Lupi. Last updated 8 years ago.

0.5 match 1 stars 2.00 score

shanbatr

CGP:Composite Gaussian Process Models

Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary. Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.

Maintained by Shan Ba. Last updated 7 years ago.

0.5 match 2.00 score 9 scripts

cran

EMDSVRhybrid:Empirical Mode Decomposition Based Support Vector Regression Model

Description: Application of empirical mode decomposition based support vector regression model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <http://krishi.icar.gov.in/jspui/handle/123456789/44873>; (ii) Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/43174>; (iii) Das (2023) <http://krishi.icar.gov.in/jspui/handle/123456789/77772>.

Maintained by Pankaj Das. Last updated 1 years ago.

0.5 match 2.00 score

cran

EMDANNhybrid:Empirical Mode Decomposition Based Artificial Neural Network Model

Application of empirical mode decomposition based artificial neural network model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=55&issue=1&article=013>; (ii) Das (2020) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=56&issue=2&article=002>.

Maintained by Pankaj Das. Last updated 2 years ago.

0.5 match 2.00 score

cran

GPM:Gaussian Process Modeling of Multi-Response and Possibly Noisy Datasets

Provides a general and efficient tool for fitting a response surface to a dataset via Gaussian processes. The dataset can have multiple responses and be noisy (with stationary variance). The fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.

Maintained by Ramin Bostanabad. Last updated 6 years ago.

openblas cpp openmp

0.5 match 2.00 score

cran

invgamstochvol:Obtains the Log Likelihood for an Inverse Gamma Stochastic Volatility Model

Computes the log likelihood for an inverse gamma stochastic volatility model using a closed form expression of the likelihood. The details of the computation of this closed form expression are given in Gonzalez and Majoni (2023) <http://rcea.org/RePEc/pdf/wp23-11.pdf> . The closed form expression is obtained for a stationary inverse gamma stochastic volatility model by marginalising out the volatility. This allows the user to obtain the maximum likelihood estimator for this non linear non Gaussian state space model. In addition, the user can obtain the estimates of the smoothed volatility using the exact smoothing distributions.

Maintained by Blessings Majoni. Last updated 2 years ago.

cpp openmp

0.5 match 2.00 score

cran

markophylo:Markov Chain Models for Phylogenetic Trees

Allows for fitting of maximum likelihood models using Markov chains on phylogenetic trees for analysis of discrete character data. Examples of such discrete character data include restriction sites, gene family presence/absence, intron presence/absence, and gene family size data. Hypothesis-driven user- specified substitution rate matrices can be estimated. Allows for biologically realistic models combining constrained substitution rate matrices, site rate variation, site partitioning, branch-specific rates, allowing for non-stationary prior root probabilities, correcting for sampling bias, etc. See Dang and Golding (2016) <doi:10.1093/bioinformatics/btv541> for more details.

Maintained by Utkarsh J. Dang. Last updated 2 years ago.

openblas cpp

0.5 match 2.00 score

cran

mSTEM:Multiple Testing of Local Extrema for Detection of Change Points

A new approach to detect change points based on smoothing and multiple testing, which is for long data sequence modeled as piecewise constant functions plus stationary Gaussian noise, see Dan Cheng and Armin Schwartzman (2015) <arXiv:1504.06384>.

Maintained by Zhibing He. Last updated 6 years ago.

0.5 match 1.70 score

cran

expandFunctions:Feature Matrix Builder

Generates feature matrix outputs from R object inputs using a variety of expansion functions. The generated feature matrices have applications as inputs for a variety of machine learning algorithms. The expansion functions are based on coercing the input to a matrix, treating the columns as features and converting individual columns or combinations into blocks of columns. Currently these include expansion of columns by efficient sparse embedding by vectors of lags, quadratic expansion into squares and unique products, powers by vectors of degree, vectors of orthogonal polynomials functions, and block random affine projection transformations (RAPTs). The transformations are magrittr- and cbind-friendly, and can be used in a building block fashion. For instance, taking the cos() of the output of the RAPT transformation generates a stationary kernel expansion via Bochner's theorem, and this expansion can then be cbind-ed with other features. Additionally, there are utilities for replacing features, removing rows with NAs, creating matrix samples of a given distribution, a simple wrapper for LASSO with CV, a Freeman-Tukey transform, generalizations of the outer function, matrix size-preserving discrete difference by row, plotting, etc.

Maintained by Scott Miller. Last updated 9 years ago.

0.5 match 1.70 score

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.

0.6 match 1.52 score 33 scripts

ybai69

VARDetect:Multiple Change Point Detection in Structural VAR Models

Implementations of Thresholded Block Segmentation Scheme (TBSS) and Low-rank plus Sparse Two Step Procedure (LSTSP) algorithms for detecting multiple changes in structural VAR models. The package aims to address the problem of change point detection in piece-wise stationary VAR models, under different settings regarding the structure of their transition matrices (autoregressive dynamics); specifically, the following cases are included: (i) (weakly) sparse, (ii) structured sparse, and (iii) low rank plus sparse. It includes multiple algorithms and related extensions from Safikhani and Shojaie (2020) <doi:10.1080/01621459.2020.1770097> and Bai, Safikhani and Michailidis (2020) <doi:10.1109/TSP.2020.2993145>.

Maintained by Yue Bai. Last updated 10 months ago.

openblas cpp openmp

0.5 match 1 stars 1.30 score 1 scripts

kakoko1984

wbsts:Multiple Change-Point Detection for Nonstationary Time Series

Implements detection for the number and locations of the change-points in a time series using the Wild Binary Segmentation and the Locally Stationary Wavelet model of Korkas and Fryzlewicz (2017) <doi:10.5705/ss.202015.0262>.

Maintained by Karolos Korkas. Last updated 5 years ago.

cpp

0.5 match 1.23 score 17 scripts

cran

extremogram:Estimation of Extreme Value Dependence for Time Series Data

Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.

Maintained by Nadezda Frolova. Last updated 8 years ago.

0.5 match 1.00 score

benowell

regspec:Non-Parametric Bayesian Spectrum Estimation for Multirate Data

Computes linear Bayesian spectral estimates from multirate data for second-order stationary time series. Provides credible intervals and methods for plotting various spectral estimates. Please see the paper `Should we sample a time series more frequently?' (doi below) for a full description of and motivation for the methodology.

Maintained by Ben Powell. Last updated 2 years ago.

0.5 match 1.00 score 6 scripts

fuyuan-li

CopCTS:Copula-Based Semiparametric Analysis for Time Series Data with Detection Limits

Semiparametric estimation for censored time series with lower detection limit. The latent response is a sequence of stationary process with Markov property of order one. Estimation of copula parameter(COPC) and Conditional quantile estimation are included for five available copula functions. Copula selection methods based on L2 distance from empirical copula function are also included.

Maintained by Fuyuan David Li. Last updated 6 years ago.

0.5 match 1 stars 1.00 score 4 scripts

leilamarvian

Hassani.SACF:Computing Lower Bound of Ljung-Box Test

The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. The Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to the number of lags involved in the test and therefore it should be used with extra caution. The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) <doi:10.1016/j.physa.2018.12.028>.

Maintained by Leila Marvian Mashhad. Last updated 2 years ago.

0.5 match 1.00 score

cran

dSTEM:Multiple Testing of Local Extrema for Detection of Change Points

Simultaneously detect the number and locations of change points in piecewise linear models under stationary Gaussian noise allowing autocorrelated random noise. The core idea is to transform the problem of detecting change points into the detection of local extrema (local maxima and local minima)through kernel smoothing and differentiation of the data sequence, see Cheng et al. (2020) <doi:10.1214/20-EJS1751>. A low-computational and fast algorithm call 'dSTEM' is introduced to detect change points based on the 'STEM' algorithm in D. Cheng and A. Schwartzman (2017) <doi:10.1214/16-AOS1458>.

Maintained by Zhibing He. Last updated 2 years ago.

0.5 match 1.00 score

cran

dbacf:Autocovariance Estimation via Difference-Based Methods

Provides methods for (auto)covariance/correlation function estimation in change point regression with stationary errors circumventing the pre-estimation of the underlying signal of the observations. Generic, first-order, (m+1)-gapped, difference-based autocovariance function estimator is based on M. Levine and I. Tecuapetla-Gómez (2023) <doi:10.48550/arXiv.1905.04578>. Bias-reducing, second-order, (m+1)-gapped, difference-based estimator is based on I. Tecuapetla-Gómez and A. Munk (2017) <doi:10.1111/sjos.12256>. Robust autocovariance estimator for change point regression with autoregressive errors is based on S. Chakar et al. (2017) <doi:10.3150/15-BEJ782>. It also includes a general projection-based method for covariance matrix estimation.

Maintained by Inder Tecuapetla-Gómez. Last updated 2 years ago.

0.5 match 1.00 score

cran

modernVA:An Implementation of Two Modern Education-Based Value-Added Models

Provides functions that fit two modern education-based value-added models. One of these models is the quantile value-added model. This model permits estimating a school's value-added based on specific quantiles of the post-test distribution. Estimating value-added based on quantiles of the post-test distribution provides a more complete picture of an education institution's contribution to learning for students of all abilities. See Page, G.L.; San Martín, E.; Orellana, J.; Gonzalez, J. (2017) <doi:10.1111/rssa.12195> for more details. The second model is a temporally dependent value-added model. This model takes into account the temporal dependence that may exist in school performance between two cohorts in one of two ways. The first is by modeling school random effects with a non-stationary AR(1) process. The second is by modeling school effects based on previous cohort's post-test performance. In addition to more efficiently estimating value-added, this model permits making statements about the persistence of a schools effectiveness. The standard value-added model is also an option.

Maintained by Garritt L. Page. Last updated 1 years ago.

0.5 match 1.00 score