R-universe search: needs:fBasics

pecanproject

PEcAn.assim.batch:PEcAn Functions Used for Ecological Forecasts and Reanalysis

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

Maintained by Istem Fer. Last updated 3 days ago.

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

216 stars 9.96 score 20 scripts 2 dependents

pecanproject

PEcAnRTM:PEcAn Functions Used for Radiative Transfer Modeling

Functions for performing forward runs and inversions of radiative transfer models (RTMs). Inversions can be performed using maximum likelihood, or more complex hierarchical Bayesian methods. Underlying numerical analyses are optimized for speed using Fortran code.

Maintained by Alexey Shiklomanov. Last updated 3 days ago.

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

216 stars 9.70 score 132 scripts

paulponcet

modeest:Mode Estimation

Provides estimators of the mode of univariate data or univariate distributions.

Maintained by Paul Poncet. Last updated 5 years ago.

9 stars 9.62 score 900 scripts 47 dependents

pecanproject

PEcAn.data.land:PEcAn Functions Used for Ecological Forecasts and Reanalysis

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

Maintained by Mike Dietze. Last updated 3 days ago.

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

216 stars 9.33 score 19 scripts 10 dependents

pecanproject

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

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

Maintained by David LeBauer. Last updated 3 days ago.

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

216 stars 9.00 score 266 scripts

pecanproject

PEcAn.BIOCRO:PEcAn Package for Integration of the BioCro Model

This module provides functions to link BioCro to PEcAn.

Maintained by David LeBauer. Last updated 3 days ago.

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

216 stars 8.94 score 23 scripts

pecanproject

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

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. This package provides workhorse functions that can be used to run the major steps of a PEcAn analysis.

Maintained by David LeBauer. Last updated 3 days ago.

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

216 stars 8.83 score 15 scripts 4 dependents

pecanproject

PEcAn.ED2:PEcAn Package for Integration of ED2 Model

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

Maintained by Mike Dietze. Last updated 3 days ago.

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

216 stars 8.74 score 145 scripts

hmorlon

RPANDA:Phylogenetic ANalyses of DiversificAtion

Implements macroevolutionary analyses on phylogenetic trees. See Morlon et al. (2010) <DOI:10.1371/journal.pbio.1000493>, Morlon et al. (2011) <DOI:10.1073/pnas.1102543108>, Condamine et al. (2013) <DOI:10.1111/ele.12062>, Morlon et al. (2014) <DOI:10.1111/ele.12251>, Manceau et al. (2015) <DOI:10.1111/ele.12415>, Lewitus & Morlon (2016) <DOI:10.1093/sysbio/syv116>, Drury et al. (2016) <DOI:10.1093/sysbio/syw020>, Manceau et al. (2016) <DOI:10.1093/sysbio/syw115>, Morlon et al. (2016) <DOI:10.1111/2041-210X.12526>, Clavel & Morlon (2017) <DOI:10.1073/pnas.1606868114>, Drury et al. (2017) <DOI:10.1093/sysbio/syx079>, Lewitus & Morlon (2017) <DOI:10.1093/sysbio/syx095>, Drury et al. (2018) <DOI:10.1371/journal.pbio.2003563>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, Maliet et al. (2019) <DOI:10.1038/s41559-019-0908-0>, Billaud et al. (2019) <DOI:10.1093/sysbio/syz057>, Lewitus et al. (2019) <DOI:10.1093/sysbio/syz061>, Aristide & Morlon (2019) <DOI:10.1111/ele.13385>, Maliet et al. (2020) <DOI:10.1111/ele.13592>, Drury et al. (2021) <DOI:10.1371/journal.pbio.3001270>, Perez-Lamarque & Morlon (2022) <DOI:10.1111/mec.16478>, Perez-Lamarque et al. (2022) <DOI:10.1101/2021.08.30.458192>, Mazet et al. (2023) <DOI:10.1111/2041-210X.14195>, Drury et al. (2024) <DOI:10.1016/j.cub.2023.12.055>.

Maintained by Hélène Morlon. Last updated 3 months ago.

24 stars 8.50 score 255 scripts

pecanproject

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

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

Maintained by Mike Dietze. Last updated 3 days ago.

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

216 stars 8.36 score 61 scripts

pecanproject

PEcAn.LINKAGES:PEcAn Package for Integration of the LINKAGES Model

This module provides functions to link the (LINKAGES) to PEcAn.

Maintained by Ann Raiho. Last updated 3 days ago.

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

216 stars 8.35 score 59 scripts

robjhyndman

demography:Forecasting Mortality, Fertility, Migration and Population Data

Functions for demographic analysis including lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.

Maintained by Rob Hyndman. Last updated 4 months ago.

actuarial demography forecasting

74 stars 8.21 score 241 scripts 6 dependents

pecanproject

PEcAnAssimSequential:PEcAn Functions Used for Ecological Forecasts and Reanalysis

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

Maintained by Mike Dietze. Last updated 3 days ago.

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

216 stars 8.12 score 35 scripts

jonasmoss

univariateML:Maximum Likelihood Estimation for Univariate Densities

User-friendly maximum likelihood estimation (Fisher (1921) <doi:10.1098/rsta.1922.0009>) of univariate densities.

Maintained by Jonas Moss. Last updated 27 days ago.

density estimation maximum-likelihood

8 stars 8.10 score 62 scripts 7 dependents

bioc

compcodeR:RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods

This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data. Finally, it provides convenient interfaces to several packages for performing the differential expression analysis. These can also be used as templates for setting up and running a user-defined differential analysis workflow within the framework of the package.

Maintained by Charlotte Soneson. Last updated 3 months ago.

immunooncology rnaseq differentialexpression

12 stars 8.10 score 26 scripts

pecanproject

PEcAn.LDNDC:PEcAn package for integration of the LDNDC model

This module provides functions to link the (LDNDC) to PEcAn.

Maintained by Henri Kajasilta. Last updated 3 days ago.

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

216 stars 7.58 score

r-forge

fExtremes:Rmetrics - Modelling Extreme Events in Finance

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

Maintained by Paul J. Northrop. Last updated 10 days ago.

1 stars 7.29 score 118 scripts 4 dependents

ykang

gratis:Generating Time Series with Diverse and Controllable Characteristics

Generates synthetic time series based on various univariate time series models including MAR and ARIMA processes. Kang, Y., Hyndman, R.J., Li, F.(2020) <doi:10.1002/sam.11461>.

Maintained by Feng Li. Last updated 12 months ago.

data-generation mixture-autoregressive statistical-computing time-series

76 stars 6.98 score 25 scripts

bioc

animalcules:Interactive microbiome analysis toolkit

animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights.

Maintained by Jessica McClintock. Last updated 5 months ago.

microbiome metagenomics coverage visualization

55 stars 6.95 score 23 scripts

jonasmoss

kdensity:Kernel Density Estimation with Parametric Starts and Asymmetric Kernels

Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>. Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>, the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>. User-supplied kernels, parametric starts, and bandwidths are supported.

Maintained by Jonas Moss. Last updated 26 days ago.

asymmetric-kernels density-estimation kernel-density-estimation non-parametric

16 stars 6.87 score 153 scripts 1 dependents

r-forge

fPortfolio:Rmetrics - Portfolio Selection and Optimization

A collection of functions to optimize portfolios and to analyze them from different points of view.

Maintained by Stefan Theussl. Last updated 10 days ago.

1 stars 6.65 score 179 scripts 2 dependents

mingzehuang

latentcor:Fast Computation of Latent Correlations for Mixed Data

The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated), comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation. The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <arXiv:1809.06255>. For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>. For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.

Maintained by Mingze Huang. Last updated 3 years ago.

data-analysis data-mining data-processing data-science data-structures machine-learning mixed-types statistics

16 stars 6.65 score 46 scripts 1 dependents

r-forge

fUnitRoots:Rmetrics - Modelling Trends and Unit Roots

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

Maintained by Georgi N. Boshnakov. Last updated 10 days ago.

fortran

1 stars 6.64 score 292 scripts

ericarcher

swfscMisc:Miscellaneous Functions for Southwest Fisheries Science Center

Collection of conversion, analytical, geodesic, mapping, and plotting functions. Used to support packages and code written by researchers at the Southwest Fisheries Science Center of the National Oceanic and Atmospheric Administration.

Maintained by Eric Archer. Last updated 3 days ago.

2 stars 6.64 score 101 scripts 20 dependents

swfsc

swfscDAS:Processing DAS Data Files

Process and summarize DAS data files. These files are typically, but do not have to be DAS <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> data produced by the Southwest Fisheries Science Center (SWFSC) program 'WinCruz'. This package standardizes and streamlines basic DAS data processing, and includes a PDF with the DAS data format requirements expected by the package.

Maintained by Sam Woodman. Last updated 15 days ago.

jags cpp

4 stars 6.64 score 24 scripts 3 dependents

wilkelab

sicegar:Analysis of Single-Cell Viral Growth Curves

Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model best describes the data. This method was first developed in the context of single-cell viral growth analysis (for details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>), and the package name stands for "SIngle CEll Growth Analysis in R".

Maintained by Claus O. Wilke. Last updated 4 years ago.

9 stars 6.57 score 41 scripts

rtsay1

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

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

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

cpp

6 stars 6.52 score 272 scripts 6 dependents

tripartio

ale:Interpretable Machine Learning and Statistical Inference with Accumulated Local Effects (ALE)

Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package reimplements the algorithms for calculating ALE data and develops highly interpretable visualizations for plotting these ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. <arXiv:2310.09877>. <doi:10.48550/arXiv.2310.09877>.

Maintained by Chitu Okoli. Last updated 1 months ago.

4 stars 6.38 score 27 scripts

cran

fGarch:Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

Analyze and model heteroskedastic behavior in financial time series.

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

fortran

7 stars 6.33 score 51 dependents

bioc

gscreend:Analysis of pooled genetic screens

Package for the analysis of pooled genetic screens (e.g. CRISPR-KO). The analysis of such screens is based on the comparison of gRNA abundances before and after a cell proliferation phase. The gscreend packages takes gRNA counts as input and allows detection of genes whose knockout decreases or increases cell proliferation.

Maintained by Katharina Imkeller. Last updated 5 months ago.

software statisticalmethod pooledscreens crispr

11 stars 6.04 score 7 scripts

brry

extremeStat:Extreme Value Statistics and Quantile Estimation

Fit, plot and compare several (extreme value) distribution functions. Compute (truncated) distribution quantile estimates and plot return periods on a linear scale. On the fitting method, see Asquith (2011): Distributional Analysis with L-moment Statistics [...] ISBN 1463508417.

Maintained by Berry Boessenkool. Last updated 1 years ago.

14 stars 5.88 score 36 scripts 1 dependents

r-forge

fAssets:Rmetrics - Analysing and Modelling Financial Assets

A collection of functions to manage, to investigate and to analyze data sets of financial assets from different points of view.

Maintained by Stefan Theussl. Last updated 10 days ago.

1 stars 5.85 score 26 scripts 3 dependents

bioc

benchdamic:Benchmark of differential abundance methods on microbiome data

Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.

Maintained by Matteo Calgaro. Last updated 4 months ago.

metagenomics microbiome differentialexpression multiplecomparison normalization preprocessing software benchmark differential-abundance-methods

8 stars 5.78 score 8 scripts

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.

2 stars 5.53 score 12 scripts

swfsc

rfPermute:Estimate Permutation p-Values for Random Forest Importance Metrics

Estimate significance of importance metrics for a Random Forest model by permuting the response variable. Produces null distribution of importance metrics for each predictor variable and p-value of observed. Provides summary and visualization functions for 'randomForest' results.

Maintained by Eric Archer. Last updated 4 days ago.

jags cpp

27 stars 5.44 score

r-forge

fRegression:Rmetrics - Regression Based Decision and Prediction

A collection of functions for linear and non-linear regression modelling. It implements a wrapper for several regression models available in the base and contributed packages of R.

Maintained by Paul J. Northrop. Last updated 10 days ago.

1 stars 5.44 score 23 scripts

mangothecat

BLCOP:Black-Litterman and Copula Opinion Pooling Frameworks

An implementation of the Black-Litterman Model and Attilio Meucci's copula opinion pooling framework as described in Meucci, Attilio (2005) <doi:10.2139/ssrn.848407>, Meucci, Attilio (2006) <doi:10.2139/ssrn.872577> and Meucci, Attilio (2008) <doi:10.2139/ssrn.1117574>.

Maintained by Joe Russell. Last updated 4 years ago.

6 stars 5.37 score 39 scripts

rafromb

SynergyLMM:Statistical Framework for in Vivo Drug Combination Studies

A framework for evaluating drug combination effects in preclinical in vivo studies. 'SynergyLMM' provides functions to analyze longitudinal tumor growth experiments using linear mixed-effects models, perform time-dependent analyses of synergy and antagonism, evaluate model diagnostics and performance, and assess both post-hoc and a priori statistical power. The calculation of drug combination synergy follows the statistical framework provided by Demidenko and Miller (2019, <doi:10.1371/journal.pone.0224137>). The implementation and analysis of linear mixed-effect models is based on the methods described by Pinheiro and Bates (2000, <doi:10.1007/b98882>), and Gałecki and Burzykowski (2013, <doi:10.1007/978-1-4614-3900-4>).

Maintained by Rafael Romero-Becerra. Last updated 2 months ago.

2 stars 5.32 score

dcgerard

ldsep:Linkage Disequilibrium Shrinkage Estimation for Polyploids

Estimate haplotypic or composite pairwise linkage disequilibrium (LD) in polyploids, using either genotypes or genotype likelihoods. Support is provided to estimate the popular measures of LD: the LD coefficient D, the standardized LD coefficient D', and the Pearson correlation coefficient r. All estimates are returned with corresponding standard errors. These estimates and standard errors can then be used for shrinkage estimation. The main functions are ldfast(), ldest(), mldest(), sldest(), plot.lddf(), format_lddf(), and ldshrink(). Details of the methods are available in Gerard (2021a) <doi:10.1111/1755-0998.13349> and Gerard (2021b) <doi:10.1038/s41437-021-00462-5>.

Maintained by David Gerard. Last updated 2 years ago.

openblas cpp openmp

9 stars 5.26 score 20 scripts

aplantin

MiRKAT:Microbiome Regression-Based Kernel Association Tests

Test for overall association between microbiome composition data and phenotypes via phylogenetic kernels. The phenotype can be univariate continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>). The package can also use robust regression (unpublished work) and integrated quantile regression (Wang et al. (2021) <doi:10.1093/bioinformatics/btab668>). In each case, the microbiome community effect is modeled nonparametrically through a kernel function, which can incorporate phylogenetic tree information.

Maintained by Anna Plantinga. Last updated 2 years ago.

3 stars 5.22 score 183 scripts 1 dependents

yijuanhu

LDM:Testing Hypotheses About the Microbiome using the Linear Decomposition Model

A single analysis path that includes distance-based ordination, global tests of any effect of the microbiome, and tests of the effects of individual taxa with false-discovery-rate (FDR) control. It accommodates both continuous and discrete covariates as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based p-values that can control for sample correlations. It can be applied to transformed data, and an omnibus test can combine results from analyses conducted on different transformation scales. It can also be used for testing presence-absence associations based on infinite number of rarefaction replicates, testing mediation effects of the microbiome, analyzing censored time-to-event outcomes, and for compositional analysis by fitting linear models to centered-log-ratio taxa count data.

Maintained by Yi-Juan Hu. Last updated 2 years ago.

7 stars 4.91 score 23 scripts

davidvandijcke

DiSCos:Distributional Synthetic Controls Estimation

The method of synthetic controls is a widely-adopted tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) <doi:10.3982/ECTA18260>, that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them.

Maintained by David Van Dijcke. Last updated 16 days ago.

1 stars 4.81 score 8 scripts

irinagain

mixedCCA:Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data

Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.

Maintained by Irina Gaynanova. Last updated 3 years ago.

openblas cpp

21 stars 4.75 score 27 scripts

bioc

ChIPseqR:Identifying Protein Binding Sites in High-Throughput Sequencing Data

ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well.

Maintained by Peter Humburg. Last updated 5 months ago.

chipseq infrastructure

4.70 score 1 scripts

cran

ftsa:Functional Time Series Analysis

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

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

6 stars 4.61 score 10 dependents

hpetren

chemodiv:Analysing Chemodiversity of Phytochemical Data

Quantify and visualise various measures of chemical diversity and dissimilarity, for phytochemical compounds and other sets of chemical composition data. Importantly, these measures can incorporate biosynthetic and/or structural properties of the chemical compounds, resulting in a more comprehensive quantification of diversity and dissimilarity. For details, see Petrén, Köllner and Junker (2023) <doi:10.1111/nph.18685>.

Maintained by Hampus Petrén. Last updated 2 years ago.

5 stars 4.57 score 15 scripts

kbose28

FarmSelect:Factor Adjusted Robust Model Selection

Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.

Maintained by Kaizheng Wang. Last updated 6 years ago.

openblas cpp

7 stars 4.54 score 8 scripts

cran

GUniFrac:Generalized UniFrac Distances, Distance-Based Multivariate Methods and Feature-Based Univariate Methods for Microbiome Data Analysis

A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances, Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.

Maintained by Jun Chen. Last updated 2 years ago.

cpp

4.53 score 8 dependents

gasparl

neatStats:Neat and Painless Statistical Reporting

User-friendly, clear and simple statistics, primarily for publication in psychological science. The main functions are wrappers for other packages, but there are various additions as well. Every relevant step from data aggregation to reportable printed statistics is covered for basic experimental designs.

Maintained by Gáspár Lukács. Last updated 2 years ago.

bayesfactor confidence-intervals pipeline statistical-analysis statistics

4 stars 4.30 score

haghbinh

Rfssa:Functional Singular Spectrum Analysis

Methods and tools for implementing functional singular spectrum analysis and related techniques.

Maintained by Hossein Haghbin. Last updated 1 years ago.

cpp

7 stars 4.30 score 19 scripts

mightymetrika

npboottprm:Nonparametric Bootstrap Test with Pooled Resampling

Addressing crucial research questions often necessitates a small sample size due to factors such as distinctive target populations, rarity of the event under study, time and cost constraints, ethical concerns, or group-level unit of analysis. Many readily available analytic methods, however, do not accommodate small sample sizes, and the choice of the best method can be unclear. The 'npboottprm' package enables the execution of nonparametric bootstrap tests with pooled resampling to help fill this gap. Grounded in the statistical methods for small sample size studies detailed in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, the package facilitates a range of statistical tests, encompassing independent t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests. The nonparboot() function undertakes essential computations, yielding detailed outputs which include test statistics, effect sizes, confidence intervals, and bootstrap distributions. Further, 'npboottprm' incorporates an interactive 'shiny' web application, nonparboot_app(), offering intuitive, user-friendly data exploration.

Maintained by Mackson Ncube. Last updated 6 months ago.

datascience nonparametric statistics

1 stars 4.26 score 5 scripts 2 dependents

jrvanderdoes

fChange:Functional Change Point Detection and Analysis

Analyze functional data and its change points. Includes functionality to store and process data, summarize and validate assumptions, characterize and perform inference of change points, and provide visualizations. Data is stored as discretely collected observations without requiring the selection of basis functions. For more details see chapter 8 of Horvath and Rice (2024) <doi:10.1007/978-3-031-51609-2>. Additional papers are forthcoming. Focused works are also included in the documentation of corresponding functions.

Maintained by Jeremy VanderDoes. Last updated 17 hours ago.

openblas cpp openmp

1 stars 4.04 score

tripartio

autogam:Automate the Creation of Generalized Additive Models (GAMs)

This wrapper package for 'mgcv' makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, 'AutoGAM' tries to automate the procedure of configuring a highly accurate GAM which performs at reasonably high speed, even for large datasets.

Maintained by Chitu Okoli. Last updated 1 months ago.

3 stars 4.02 score 3 scripts

mightymetrika

bootwar:Nonparametric Bootstrap Test with Pooled Resampling Card Game

The card game War is simple in its rules but can be lengthy. In another domain, the nonparametric bootstrap test with pooled resampling (nbpr) methods, as outlined in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, is optimal for comparing paired or unpaired means in non-normal data, especially for small sample size studies. However, many researchers are unfamiliar with these methods. The 'bootwar' package bridges this gap by enabling users to grasp the concepts of nbpr via Boot War, a variation of the card game War designed for small samples. The package provides functions like score_keeper() and play_round() to streamline gameplay and scoring. Once a predetermined number of rounds concludes, users can employ the analyze_game() function to derive game results. This function leverages the 'npboottprm' package's nonparboot() to report nbpr results and, for comparative analysis, also reports results from the 'stats' package's t.test() function. Additionally, 'bootwar' features an interactive 'shiny' web application, bootwar(). This offers a user-centric interface to experience Boot War, enhancing understanding of nbpr methods across various distributions, sample sizes, number of bootstrap resamples, and confidence intervals.

Maintained by Mackson Ncube. Last updated 1 years ago.

bootstrap data-science resampling statistics

4.00 score 6 scripts

swfsc

CruzPlot:Plot Shipboard DAS Data

A utility program oriented to create maps, plot data, and do basic data summaries of DAS data files. These files are typically, but do not have to be DAS <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> data produced by the Southwest Fisheries Science Center (SWFSC) program 'WinCruz'.

Maintained by Sam Woodman. Last updated 6 months ago.

jags cpp

2 stars 3.85 score 2 scripts

klebermsousa

jackstrap:Correcting Nonparametric Frontier Measurements for Outliers

Provides method used to check whether data have outlier in efficiency measurement of big samples with data envelopment analysis (DEA). In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.

Maintained by Kleber Morais de Sousa. Last updated 5 years ago.

dea jackstrap nonparametric outlier-detection

1 stars 3.85 score 14 scripts

geobosh

StableEstim:Estimate the Four Parameters of Stable Laws using Different Methods

Estimate the four parameters of stable laws using maximum likelihood method, generalised method of moments with finite and continuum number of points, iterative Koutrouvelis regression and Kogon-McCulloch method. The asymptotic properties of the estimators (covariance matrix, confidence intervals) are also provided.

Maintained by Georgi N. Boshnakov. Last updated 5 months ago.

characteristic-functions estimation simulation stable-distribution

3.73 score 18 scripts 2 dependents

guillaumeevin

GWEX:Multi-Site Stochastic Models for Daily Precipitation and Temperature

Application of multi-site models for daily precipitation and temperature data. This package is designed for an application to 105 precipitation and 26 temperature gauges located in Switzerland. It applies fitting procedures and provides weather generators described in the following references: - Evin, G., A.-C. Favre, and B. Hingray. (2018) <doi:10.5194/hess-22-655-2018>. - Evin, G., A.-C. Favre, and B. Hingray. (2018) <doi:10.1007/s00704-018-2404-x>.

Maintained by Guillaume Evin. Last updated 4 months ago.

cpp

2 stars 3.70 score 6 scripts

swfsc

swfscAirDAS:Southwest Fisheries Science Center Aerial DAS Data Processing

Process and summarize aerial survey 'DAS' data (AirDAS) <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-185.PDF> collected using an aerial survey program from the Southwest Fisheries Science Center (SWFSC) <https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>. PDF files detailing the relevant AirDAS data formats are included in this package.

Maintained by Sam Woodman. Last updated 6 months ago.

jags cpp

3.70 score 7 scripts

bioc

ClustAll:ClustAll: Data driven strategy to robustly identify stratification of patients within complex diseases

Data driven strategy to find hidden groups of patients with complex diseases using clinical data. ClustAll facilitates the unsupervised identification of multiple robust stratifications. ClustAll, is able to overcome the most common limitations found when dealing with clinical data (missing values, correlated data, mixed data types).

Maintained by Asier Ortega-Legarreta. Last updated 5 months ago.

software statisticalmethod clustering dimensionreduction principalcomponent

3.70 score 1 scripts

wagathu

StockDistFit:A Package for Fitting Stock Price Distributions

The `StockDistFit` package provides functions for fitting probability distributions to stock price data. The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution.

Maintained by Brian Njuguna. Last updated 2 years ago.

3.70 score 9 scripts

theussl

fMultivar:Rmetrics - Modeling of Multivariate Financial Return Distributions

A collection of functions inspired by Venables and Ripley (2002) <doi:10.1007/978-0-387-21706-2> and Azzalini and Capitanio (1999) <arXiv:0911.2093> to manage, investigate and analyze bivariate and multivariate data sets of financial returns.

Maintained by Stefan Theussl. Last updated 2 years ago.

3.69 score 99 scripts 7 dependents

swfsc

banter:BioAcoustic eveNT classifiER

Create a hierarchical acoustic event species classifier out of multiple call type detectors as described in Rankin et al (2017) <doi:10.1111/mms.12381>.

Maintained by Eric Archer. Last updated 1 years ago.

acoustics bioacoustics cetaceans classification dolphins machine-learning noaa random-forest species-identification supervised-learning supervised-machine-learning whales jags cpp

9 stars 3.65 score

chiliubio

mecoturn:Decipher Microbial Turnover along a Gradient

Two pipelines are provided to study microbial turnover along a gradient, including the beta diversity and microbial abundance change. The 'betaturn' class consists of the steps of community dissimilarity matrix generation, matrix conversion, differential test and visualization. The workflow of 'taxaturn' class includes the taxonomic abundance calculation, abundance transformation, abundance change summary, statistical analysis and visualization. Multiple statistical approaches can contribute to the analysis of microbial turnover.

Maintained by Chi Liu. Last updated 6 months ago.

3 stars 3.48 score 6 scripts

jeroengoedhart

EBcoBART:Co-Data Learning for Bayesian Additive Regression Trees

Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the 'dbarts' 'R' package. See Goedhart and others (2023) <doi:10.48550/arXiv.2311.09997> for details.

Maintained by Jeroen M. Goedhart. Last updated 7 months ago.

3.30 score 1 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

3.30 score 2 scripts

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

4 stars 3.30 score 6 scripts

r-forge

distrRmetrics:Distribution Classes for Distributions from Rmetrics

S4-distribution classes based on package distr for distributions from packages 'fBasics' and 'fGarch'.

Maintained by Peter Ruckdeschel. Last updated 2 months ago.

3.20 score 7 scripts

nenuial

geographer:Geography Vizualisations

Provides function and objects to establish vizualisations for my Geography lessons.

Maintained by Pascal Burkhard. Last updated 1 months ago.

2 stars 3.08 score

david-hammond

piecenorms:Calculate a Piecewise Normalised Score Using Class Intervals

Provides an implementation of piecewise normalisation techniques useful when dealing with the communication of skewed and highly skewed data. It also provides utilities that recommends a normalisation technique based on the distribution of the data.

Maintained by David Hammond. Last updated 8 months ago.

1 stars 3.00 score 3 scripts

mightymetrika

npboottprmFBar:Informative Nonparametric Bootstrap Test with Pooled Resampling

Sample sizes are often small due to hard to reach target populations, rare target events, time constraints, limited budgets, or ethical considerations. Two statistical methods with promising performance in small samples are the nonparametric bootstrap test with pooled resampling method, which is the focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, and informative hypothesis testing, which is implemented in the 'restriktor' package. The 'npboottprmFBar' package uses the nonparametric bootstrap test with pooled resampling method to implement informative hypothesis testing. The bootFbar() function can be used to analyze data with this method and the persimon() function can be used to conduct performance simulations on type-one error and statistical power.

Maintained by Mackson Ncube. Last updated 7 months ago.

3.00 score 5 scripts

almutveraart

ambit:Simulation and Estimation of Ambit Processes

Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes.

Maintained by Almut E. D. Veraart. Last updated 3 years ago.

cpp

3.00 score 5 scripts

radivot

SEERaBomb:SEER and Atomic Bomb Survivor Data Analysis Tools

Creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries from ASCII sources and provides tools for estimating SEER second cancer risks. Methods are described in <doi:10.1038/leu.2015.258>.

Maintained by Tomas Radivoyevitch. Last updated 5 years ago.

cpp

2.85 score 176 scripts

borishejblum

ludic:Linkage Using Diagnosis Codes

Probabilistic record linkage without direct identifiers using only diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, Churchill, Szolovits, Murphy, Kohane & Cai (2019) <doi: 10.1038/sdata.2018.298> ; Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane & Cai (2021) <doi: 10.1101/2021.05.02.21256490>.

Maintained by Boris P Hejblum. Last updated 4 years ago.

openblas cpp openmp

2.81 score 13 scripts

geobosh

mixAR:Mixture Autoregressive Models

Model time series using mixture autoregressive (MAR) models. Implemented are frequentist (EM) and Bayesian methods for estimation, prediction and model evaluation. See Wong and Li (2002) <doi:10.1111/1467-9868.00222>, Boshnakov (2009) <doi:10.1016/j.spl.2009.04.009>), and the extensive references in the documentation.

Maintained by Georgi N. Boshnakov. Last updated 5 months ago.

assymetric heteroskedasticity mixture-autoregressive student-t time-series

1 stars 2.70 score 6 scripts

cran

BRVM:Retrieve Historical Data of Companies Listed on the 'BRVM' Stock Exchange

Provide real-time access to data from the Regional Securities Exchange SA(<https://www.brvm.org/en>), commonly known as the 'BRVM' stock exchange. The goal is to facilitate data access for users of the R programming language. The package includes a variety of data that can be accessed by calling functions.

Maintained by Sessie Koffi Frederic. Last updated 1 years ago.

2.70 score

tmoek

TempStable:A Collection of Methods to Estimate Parameters of Different Tempered Stable Distributions

A collection of methods to estimate parameters of different tempered stable distributions (TSD). Currently, there are seven different tempered stable distributions to choose from: Tempered stable subordinator distribution, classical TSD, generalized classical TSD, normal TSD, modified TSD, rapid decreasing TSD, and Kim-Rachev TSD. The package also provides functions to compute density and probability functions and tools to run Monte Carlo simulations. This package has already been used for the estimation of tempered stable distributions (Massing (2023) <arXiv:2303.07060>). The following references form the theoretical background for various functions in this package. References for each function are explicitly listed in its documentation: Bianchi et al. (2010) <doi:10.1007/978-88-470-1481-7_4> Bianchi et al. (2011) <doi:10.1137/S0040585X97984632> Carrasco (2017) <doi:10.1017/S0266466616000025> Feuerverger (1981) <doi:10.1111/j.2517-6161.1981.tb01143.x> Hansen et al. (1996) <doi:10.1080/07350015.1996.10524656> Hansen (1982) <doi:10.2307/1912775> Hofert (2011) <doi:10.1145/2043635.2043638> Kawai & Masuda (2011) <doi:10.1016/j.cam.2010.12.014> Kim et al. (2008) <doi:10.1016/j.jbankfin.2007.11.004> Kim et al. (2009) <doi:10.1007/978-3-7908-2050-8_5> Kim et al. (2010) <doi:10.1016/j.jbankfin.2010.01.015> Kuechler & Tappe (2013) <doi:10.1016/j.spa.2013.06.012> Rachev et al. (2011) <doi:10.1002/9781118268070>.

Maintained by Till Massing. Last updated 1 years ago.

1 stars 2.70 score 6 scripts

cran

RCNA:Robust Copy Number Alteration Detection (RCNA)

Detects copy number alteration events in targeted exon sequencing data for tumor samples without matched normal controls. The advantage of this method is that it can be applied to smaller sequencing panels including evaluations of exon, transcript, gene, or even user specified genetic regions of interest. Functions in the package include steps for GC-content correction, calculation of quantile based normal karyotype ranges, and calculation of feature score. Cutoffs for "normal" quantile and score are user-adjustable.

Maintained by Matt Bradley. Last updated 4 months ago.

2.70 score

cran

WRI:Wasserstein Regression and Inference

Implementation of the methodologies described in 1) Alexander Petersen, Xi Liu and Afshin A. Divani (2021) <doi:10.1214/20-aos1971>, including global F tests, partial F tests, intrinsic Wasserstein-infinity bands and Wasserstein density bands, and 2) Chao Zhang, Piotr Kokoszka and Alexander Petersen (2022) <doi:10.1111/jtsa.12590>, including estimation, prediction, and inference of the Wasserstein autoregressive models.

Maintained by Xi Liu. Last updated 3 years ago.

openblas cpp

2.70 score

swfsc

sprex:Species Richness and Extrapolation

Functions for calculating species richness for rarefaction and extrapolation, primarily non-parametric species richness such as jackknife, Chao1, and ACE. Also available are functions for plotting species richness and extrapolation curves, and computing standard diversity and entropy indices.

Maintained by Eric Archer. Last updated 4 days ago.

1 stars 2.70 score

metinbulus

irtDemo:Item Response Theory Demo Collection

Includes a collection of shiny applications to demonstrate or to explore fundamental item response theory (IRT) concepts such as estimation, scoring, and multidimensional IRT models.

Maintained by Metin Bulus. Last updated 3 years ago.

2.70 score 2 scripts

cran

bifurcatingr:Bifurcating Autoregressive Models

Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) <doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020) <doi:10.1002/sta4.342>. Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023) <doi:10.6339/23-JDS1092>.

Maintained by Tamer Elbayoumi. Last updated 12 months ago.

2.70 score

jivfur

Irescale:Calculate and Rectify Moran's I

Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2013) <arXiv:1606.03658>.

Maintained by Ivan Fuentes. Last updated 5 years ago.

2.70 score 3 scripts

nenuial

geovizr:Support for Knitr (Quarto/Rmd)

Provide support functions for Quarto and Rmd documents.

Maintained by Pascal Burkhard. Last updated 1 months ago.

2.60 score 3 scripts

pifsc-protected-species-division

crputils:Miscellaneous R Utilities Useful to CRP

A collection of miscellaneous utilities that are useful for various research activities conducted by the Cetacean Research Program (CRP) at NOAA NMFS Pacific Islands Fisheries Science Center. This includes utilities for working with latitude and longitude data, gpx file creation, and more to come.

Maintained by Selene Fregosi. Last updated 3 days ago.

1 stars 2.54 score 1 scripts

cran

wwntests:Hypothesis Tests for Functional Time Series

Provides a collection of white noise hypothesis tests for functional time series and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models. These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019) <doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, and Kim et al. (2023) <doi: 10.1214/23-SS143> respectively.

Maintained by Mihyun Kim. Last updated 1 years ago.

2 stars 2.30 score

robjhyndman

addb:Australian Demographic Data Bank

These data are from the Australian Demographic Data Bank. They can be plotted and analysed using the demography package.

Maintained by Rob Hyndman. Last updated 2 years ago.

4 stars 2.30 score

tylerpittman

BiostatsUHNplus:Nested Data Summary, Adverse Events and REDCap

Tools and code snippets for summarizing nested data, adverse events and REDCap study information.

Maintained by Tyler Pittman. Last updated 2 months ago.

2.18 score 6 scripts

cran

ROKET:Optimal Transport-Based Kernel Regression

Perform optimal transport on somatic point mutations and kernel regression hypothesis testing by integrating pathway level similarities at the gene level (Little et al. (2023) <doi:10.1111/biom.13769>). The software implements balanced and unbalanced optimal transport and omnibus tests with 'C++' across a set of tumor samples and allows for multi-threading to decrease computational runtime.

Maintained by Paul Little. Last updated 23 days ago.

openblas cpp openmp

2.00 score

sandipgarai

CEEMDANML:CEEMDAN Decomposition Based Hybrid Machine Learning Models

Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.

Maintained by Mr. Sandip Garai. Last updated 2 years ago.

1.78 score 2 dependents

sandipgarai

DescribeDF:Description of a Data Frame

Helps to describe a data frame in hand. Has been developed during PhD work of the maintainer. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.

Maintained by Sandip Garai. Last updated 2 years ago.

1.78 score 2 dependents

cran

popstudy:Applied Techniques to Demographic and Time Series Analysis

The use of overparameterization is proposed with combinatorial analysis to test a broader spectrum of possible ARIMA models. In the selection of ARIMA models, the most traditional methods such as correlograms or others, do not usually cover many alternatives to define the number of coefficients to be estimated in the model, which represents an estimation method that is not the best. The popstudy package contains several tools for statistical analysis in demography and time series based in Shryock research (Shryock et. al. (1980) <https://books.google.co.cr/books?id=8Oo6AQAAMAAJ>).

Maintained by Cesar Gamboa-Sanabria. Last updated 1 years ago.

1.70 score

workshop-brg

abmR:Agent-Based Models in R

Supplies tools for running agent-based models (ABM) in R, as discussed in Gochanour et al. (2022) <doi:10.1111/2041-210X.14014>. The package contains two movement functions, each of which is based on the Ornstein-Uhlenbeck (OU) model (Ornstein & Uhlenbeck, 1930) <doi:10.1103/PhysRev.36.823>. It also contains several visualization and data summarization functions to facilitate the presentation of simulation results.

Maintained by Benjamin Gochanour. Last updated 2 years ago.

jags cpp

1 stars 1.70 score

liqgroup

AssocTests:Genetic Association Studies

Some procedures including EIGENSTRAT (a procedure for detecting and correcting for population stratification through searching for the eigenvectors in genetic association studies), PCoC (a procedure for correcting for population stratification through calculating the principal coordinates and the clustering of the subjects), Tracy-Widom test (a procedure for detecting the significant eigenvalues of a matrix), distance regression (a procedure for detecting the association between a distance matrix and some independent variants of interest), single-marker test (a procedure for identifying the association between the genotype at a biallelic marker and a trait using the Wald test or the Fisher's exact test), MAX3 (a procedure for testing for the association between a single nucleotide polymorphism and a binary phenotype using the maximum value of the three test statistics derived for the recessive, additive, and dominant models), nonparametric trend test (a procedure for testing for the association between a genetic variant and a non-normal distributed quantitative trait based on the nonparametric risk), and nonparametric MAX3 (a procedure for testing for the association between a biallelic single nucleotide polymorphism and a quantitative trait using the maximum value of the three nonparametric trend tests derived for the recessive, additive, and dominant models), which are commonly used in genetic association studies. To cite this package in publications use: Lin Wang, Wei Zhang, and Qizhai Li. AssocTests: An R Package for Genetic Association Studies. Journal of Statistical Software. 2020; 94(5): 1-26.

Maintained by Lin Wang. Last updated 4 years ago.

1 stars 1.64 score 11 scripts

pigian

proteus:Multiform Seq2Seq Model for Time-Feature Analysis

Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable.

Maintained by Giancarlo Vercellino. Last updated 4 days ago.

1.52 score 33 scripts

cran

SLBDD:Statistical Learning for Big Dependent Data

Programs for analyzing large-scale time series data. They include functions for automatic specification and estimation of univariate time series, for clustering time series, for multivariate outlier detections, for quantile plotting of many time series, for dynamic factor models and for creating input data for deep learning programs. Examples of using the package can be found in the Wiley book 'Statistical Learning with Big Dependent Data' by Daniel Peña and Ruey S. Tsay (2021). ISBN 9781119417385.

Maintained by Antonio Elias. Last updated 3 years ago.

1.48 score 1 dependents

cran

MicrobiomeStat:Statistical Methods for Microbiome Compositional Data

A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects (Zhou et al. (2022)<doi:10.1186/s13059-022-02655-5>). The methods can be applied to the analysis of other (high-dimensional) compositional data arising from sequencing experiments.

Maintained by Jun Chen. Last updated 12 months ago.

1.48 score 1 dependents

sandipgarai

WaveletML:Wavelet Decomposition Based Hybrid Machine Learning Models

Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

Maintained by Mr. Sandip Garai. Last updated 2 years ago.

1.48 score 1 dependents

cran

fTrading:Rmetrics - Trading and Rebalancing Financial Instruments

A collection of functions for trading and rebalancing financial instruments. It implements various technical indicators to analyse time series such as moving averages or stochastic oscillators.

Maintained by Tobias Setz. Last updated 7 years ago.

3 stars 1.48 score

kdrachal

multDM:Multivariate Version of the Diebold-Mariano Test

Allows to perform the multivariate version of the Diebold-Mariano test for equal predictive ability of multiple forecast comparison. Main reference: Mariano, R.S., Preve, D. (2012) <doi:10.1016/j.jeconom.2012.01.014>.

Maintained by Krzysztof Drachal. Last updated 21 days ago.

1 stars 1.45 score 28 scripts

cran

outliers.ts.oga:Efficient Outlier Detection for Large Time Series Databases

Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. The programs implement the procedures presented in the paper entitled "Efficient Outlier Detection for Large Time Series Databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2025), working paper, Universidad Carlos III de Madrid. Version 1.0.1 contains some improvements to the algorithm, so the results may vary slightly compared to those obtained with version 0.0.1.

Maintained by Pedro Galeano. Last updated 1 months ago.

1.30 score

arolluom

RcmdrPlugin.RiskDemo:R Commander Plug-in for Risk Demonstration

R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory, demography and epidemic.

Maintained by Arto Luoma. Last updated 1 years ago.

1.30 score 20 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

1 stars 1.30 score 1 scripts

otryakhin-dmitry

deforestable:Classify RGB Images into Forest or Non-Forest

Implements two out-of box classifiers presented in <doi:10.48550/arXiv.2112.01063> for distinguishing forest and non-forest terrain images. Under these algorithms, there are frequentist approaches: one parametric, using stable distributions, and another one- non-parametric, using the squared Mahalanobis distance. The package also contains functions for data handling and building of new classifiers as well as some test data set.

Maintained by Dmitry Otryakhin. Last updated 2 years ago.

openblas cpp

1.26 score 18 scripts

bpfaff

gogarch:Generalized Orthogonal GARCH (GO-GARCH) Models

Provision of classes and methods for estimating generalized orthogonal GARCH models. This is an alternative approach to CC-GARCH models in the context of multivariate volatility modeling.

Maintained by Bernhard Pfaff. Last updated 3 years ago.

1.26 score 18 scripts

kakoko1984

segMGarch:Multiple Change-Point Detection for High-Dimensional GARCH Processes

Implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure.

Maintained by Karolos Korkas. Last updated 6 years ago.

cpp

1.20 score 16 scripts

cran

fBonds:Rmetrics - Pricing and Evaluating Bonds

It implements the Nelson-Siegel and the Nelson-Siegel-Svensson term structures.

Maintained by Tobias Setz. Last updated 7 years ago.

1.00 score

sandipgarai

AriGaMyANNSVR:Hybrid ARIMA-GARCH and Two Specially Designed ML-Based Models

Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) <doi:10.1007/s41096-022-00128-3>.

Maintained by Mr. Sandip Garai. Last updated 2 years ago.

1.00 score

cran

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

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

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

1.00 score

cran

L2DensityGoFtest:Density Goodness-of-Fit Test

Provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance.

Maintained by Dimitrios Bagkavos. Last updated 2 years ago.

1.00 score

lingweir

RMOPI:Risk Management and Optimization for Portfolio Investment

Provides functions for risk management and portfolio investment of securities with practical tools for data processing and plotting. Moreover, it contains functions which perform the COS Method, an option pricing method based on the Fourier-cosine series (Fang, F. (2008) <doi:10.1137/080718061>).

Maintained by Wei Ling. Last updated 3 years ago.

1 stars 1.00 score

anloor7

mlmts:Machine Learning Algorithms for Multivariate Time Series

An implementation of several machine learning algorithms for multivariate time series. The package includes functions allowing the execution of clustering, classification or outlier detection methods, among others. It also incorporates a collection of multivariate time series datasets which can be used to analyse the performance of new proposed algorithms. Some of these datasets are stored in GitHub data packages 'ueadata1' to 'ueadata8'. To access these data packages, run 'install.packages(c('ueadata1', 'ueadata2', 'ueadata3', 'ueadata4', 'ueadata5', 'ueadata6', 'ueadata7', 'ueadata8'), repos='<https://anloor7.github.io/drat/>')'. The installation takes a couple of minutes but we strongly encourage the users to do it if they want to have available all datasets of mlmts. Practitioners from a broad variety of fields could benefit from the general framework provided by 'mlmts'.

Maintained by Angel Lopez-Oriona. Last updated 7 months ago.

1.00 score 2 scripts

cran

BayesBEKK:Bayesian Estimation of Bivariate Volatility Model

The Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models are used for modelling the volatile multivariate data sets. In this package a variant of MGARCH called BEKK (Baba, Engle, Kraft, Kroner) proposed by Engle and Kroner (1995) <http://www.jstor.org/stable/3532933> has been used to estimate the bivariate time series data using Bayesian technique.

Maintained by Achal Lama. Last updated 2 years ago.

1.00 score

cran

LSMonteCarlo:American options pricing with Least Squares Monte Carlo method

The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.

Maintained by Mikhail A. Beketov. Last updated 12 years ago.

1 stars 1.00 score

camillef-git

HDSpatialScan:Multivariate and Functional Spatial Scan Statistics

Allows to detect spatial clusters of abnormal values on multivariate or functional data. Martin KULLDORFF and Lan HUANG and Kevin KONTY (2009) <doi:10.1186/1476-072X-8-58>, Inkyung JUNG and Ho Jin CHO (2015) <doi:10.1186/s12942-015-0024-6>, Lionel CUCALA and Michael GENIN and Caroline LANIER and Florent OCCELLI (2017) <doi:10.1016/j.spasta.2017.06.001>, Lionel CUCALA and Michael GENIN and Florent OCCELLI and Julien SOULA (2019) <doi:10.1016/j.spasta.2018.10.002>, Camille FREVENT and Mohamed-Salem AHMED and Matthieu MARBAC and Michael GENIN (2021) <doi:10.1016/j.spasta.2021.100550>, Zaineb SMIDA and Lionel CUCALA and Ali GANNOUN and Ghislain Durif (2022) <doi:10.1016/j.csda.2021.107378>, Camille FREVENT and Mohamed-Salem AHMED and Sophie DABO-NIANG and Michael GENIN (2023) <doi:10.1093/jrsssc/qlad017>.

Maintained by Camille FREVENT. Last updated 2 years ago.

openblas cpp jags

1.00 score 4 scripts

simontrimborn

IndexConstruction:Index Construction for Time Series Data

Derivation of indexes for benchmarking purposes. A methodology with flexible number of constituents is implemented. Also functions for market capitalization and volume weighted indexes with fixed number of constituents are available. The main function of the package, indexComp(), provides the derived index, suitable for analysis purposes. The functions indexUpdate(), indexMemberSelection() and indexMembersUpdate() are components of indexComp() and enable one to construct and continuously update an index, e.g. for display on a website. The methodology behind the functions provided gets introduced in Trimborn and Haerdle (2018) <doi:10.1016/j.jempfin.2018.08.004>.

Maintained by Simon Trimborn. Last updated 5 years ago.

1.00 score

sandipgarai

WaveletMLbestFL:The Best Wavelet Filter-Level for Prepared Wavelet-Based Models

Four filters have been chosen namely 'haar', 'c6', 'la8', and 'bl14' (Kindly refer to 'wavelets' in 'CRAN' repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results are stored in 'input'. First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as 'MIN' and other values are denoted as 'NA'. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as 'MAX' and other values are denoted as 'NA'. 'output' contains the similar number of rows (which is 8) and columns (which is number filter-level combinations) as of 'input'. Values in 'output' are corresponding 'NA', 'MIN' or 'MAX'. Finally, the column containing minimum number of 'NA' values is denoted as the best ('FL'). In special case, if two columns having equal 'NA', it has been checked among these two columns which one is having least 'NA' in first five rows and has been inferred as the best. 'FL_metrics_values' are the corresponding metrics values. 'WARIGAANbest' is the data frame (dimension: 1*8) containing different metrics of the best filter-level combination. More details can be found in Garai and others (2023) <doi:10.13140/RG.2.2.11977.42087>.

Maintained by Mr. Sandip Garai. Last updated 2 years ago.

1.00 score

cran

hdftsa:High-Dimensional Functional Time Series Analysis

Offers methods for visualizing, modelling, and forecasting high-dimensional functional time series, also known as functional panel data. Documentation about 'hdftsa' is provided via the paper by Cristian F. Jimenez-Varon, Ying Sun and Han Lin Shang (2024, <doi:10.1080/10618600.2024.2319166>).

Maintained by Han Lin Shang. Last updated 2 months ago.

1.00 score

sandipgarai

CompareMultipleModels:Finding the Best Model Using Eight Metrics Values

In statistical modeling, multiple models need to be compared based on certain criteria. The method described here uses eight metrics from 'AllMetrics' package. ‘input_df’ is the data frame (at least two columns for comparison) containing metrics values in different rows of a column (which denotes a particular model’s performance). First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as ‘MIN’ and other values are denoted as ‘NA’. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as ‘MAX’ and other values are denoted as ‘NA’. ‘output_df’ contains the similar number of rows (which is 8) and columns (which is number of models to be compared) as of ‘input_df’. Values in ‘output_df’ are corresponding ‘NA’, ‘MIN’ or ‘MAX’. Finally, the column containing minimum number of ‘NA’ values is denoted as the best column. ‘min_NA_col’ gives the name of the best column (model). ‘min_NA_values’ are the corresponding metrics values. ‘BestColumn_metrics’ is the data frame (dimension: 1*8) containing different metrics of the best column (model). ‘best_column_results’ is the final result (a list) containing all of these output elements. In special case, if two columns having equal 'NA', it will be checked among these two column which one is having least 'NA' in first five rows and will be inferred as the best. More details about 'AllMetrics' can be found in Garai (2023) <doi:10.13140/RG.2.2.18688.30723>.

Maintained by Mr. Sandip Garai. Last updated 2 years ago.

1.00 score

pigian

audrex:Automatic Dynamic Regression using Extreme Gradient Boosting

Dynamic regression for time series using Extreme Gradient Boosting with hyper-parameter tuning via Bayesian Optimization or Random Search.

Maintained by Giancarlo Vercellino. Last updated 3 years ago.

1.00 score 2 scripts

pigian

lambdaTS:Variational Seq2Seq Model with Lambda Transformer for Time Series Analysis

Time series analysis based on lambda transformer and variational seq2seq, built on 'Torch'.

Maintained by Giancarlo Vercellino. Last updated 3 years ago.

1.00 score

pigian

jenga:Fast Extrapolation of Time Features using K-Nearest Neighbors

Fast extrapolation of univariate and multivariate time features using K-Nearest Neighbors. The compact set of hyper-parameters is tuned via grid or random search.

Maintained by Giancarlo Vercellino. Last updated 3 years ago.

1.00 score

pigian

tetragon:Automatic Sequence Prediction by Expansion of the Distance Matrix

Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned through random search.

Maintained by Giancarlo Vercellino. Last updated 3 years ago.

1.00 score

pigian

dymo:Dynamic Mode Decomposition for Multivariate Time Feature Prediction

An application of Dynamic Mode Decomposition for prediction of time features. Automatic search for the best model across the space of all possible feature combinations and ranks of Singular Value Decomposition.

Maintained by Giancarlo Vercellino. Last updated 3 years ago.

1.00 score 1 scripts

pigian

segen:Sequence Generalization Through Similarity Network

Proposes an application for sequence prediction generalizing the similarity within the network of previous sequences.

Maintained by Giancarlo Vercellino. Last updated 3 years ago.

1.00 score

pigian

codez:Seq2Seq Encoder-Decoder Model for Time-Feature Analysis Based on Tensorflow

Proposes Seq2seq Time-Feature Analysis using an Encoder-Decoder to project into latent space and a Forward Network to predict the next sequence.

Maintained by Giancarlo Vercellino. Last updated 3 years ago.

1.00 score 1 scripts

pigian

naive:Empirical Extrapolation of Time Feature Patterns

An application for the empirical extrapolation of time features selecting and summarizing the most relevant patterns in time sequences.

Maintained by Giancarlo Vercellino. Last updated 2 years ago.

1.00 score

pigian

spooky:Time Feature Extrapolation Using Spectral Analysis and Jack-Knife Resampling

Proposes application of spectral analysis and jack-knife resampling for multivariate sequence forecasting. The application allows for a fast random search in a compact space of hyper-parameters composed by Sequence Length and Jack-Knife Leave-N-Out.

Maintained by Giancarlo Vercellino. Last updated 3 years ago.

1.00 score

cran

CompDist:Multisection Composite Distributions

Computes density function, cumulative distribution function, quantile function and random numbers for a multisection composite distribution specified by the user. Also fits the user specified distribution to a given data set. More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017) CompDist: Multisection composite distributions.

Maintained by Saralees Nadarajah. Last updated 8 years ago.

1.00 score

joalor93

OBASpatial:Objective Bayesian Analysis for Spatial Regression Models

It makes an objective Bayesian analysis of the spatial regression model using both the normal (NSR) and student-T (TSR) distributions. The functions provided give prior and posterior objective densities and allow default Bayesian estimation of the model regression parameters. Details can be found in Ordonez et al. (2020) <arXiv:2004.04341>.

Maintained by Alejandro Ordonez. Last updated 3 years ago.

1.00 score

cran

HBSTM:Hierarchical Bayesian Space-Time Models for Gaussian Space-Time Data

Fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.

Maintained by Alberto Lopez Moreno. Last updated 3 years ago.

1 stars 1.00 score