R-universe search: dynamic

martin3141

spant:MR Spectroscopy Analysis Tools

Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.

Maintained by Martin Wilson. Last updated 29 days ago.

brain mri mrs mrshub spectroscopy fortran

66.0 match 24 stars 8.55 score 81 scripts

melissagwolf

dynamic:DFI Cutoffs for Latent Variable Models

Returns dynamic fit index (DFI) cutoffs for latent variable models that are tailored to the user's model statement, model type, and sample size. This is the counterpart of the Shiny Application, <https://dynamicfit.app>.

Maintained by Melissa G. Wolf. Last updated 2 months ago.

72.9 match 16 stars 7.13 score 139 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 14 hours ago.

49.4 match 5 stars 6.71 score 44 scripts

yihui

knitr:A General-Purpose Package for Dynamic Report Generation in R

Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.

Maintained by Yihui Xie. Last updated 12 hours ago.

dynamic-documents knitr literate-programming rmarkdown sweave

12.6 match 2.4k stars 23.62 score 116k scripts 4.2k dependents

nicholasjclark

mvgam:Multivariate (Dynamic) Generalized Additive Models

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

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

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

29.8 match 139 stars 9.85 score 117 scripts

greta-dev

greta.dynamics:Modelling Structured Dynamical Systems in 'greta'

A 'greta' extension for analysing transition matrices and ordinary differential equations representing dynamical systems. Provides functions for analysing transition matrices by iteration, and solving ordinary differential equations. This is an extension to the 'greta' software, Golding (2019) <doi:10.21105/joss.01601>.

Maintained by Nicholas Tierney. Last updated 4 months ago.

47.7 match 6 stars 5.72 score 11 scripts

stocnet

goldfish:Statistical Network Models for Dynamic Network Data

Tools for fitting statistical network models to dynamic network data. Can be used for fitting both dynamic network actor models ('DyNAMs') and relational event models ('REMs'). Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>, Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>, Stadtfeld and Block (2017) <doi:10.15195/v4.a14>, Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.

Maintained by Alvaro Uzaheta. Last updated 6 months ago.

dynam network-modelling rem statistical-network-analysis openblas cpp openmp

30.3 match 61 stars 7.91 score 44 scripts

sizespectrum

mizer:Dynamic Multi-Species Size Spectrum Modelling

A set of classes and methods to set up and run multi-species, trait based and community size spectrum ecological models, focused on the marine environment.

Maintained by Gustav Delius. Last updated 2 months ago.

ecosystem-model fish-population-dynamics fisheries fisheries-management marine-ecosystem population-dynamics simulation size-structure species-interactions transport-equation cpp

21.0 match 38 stars 9.43 score 207 scripts

epimodel

EpiModel:Mathematical Modeling of Infectious Disease Dynamics

Tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).

Maintained by Samuel Jenness. Last updated 2 months ago.

agent-based-modeling epidemics epidemiology infectious-diseases network-graph cpp

15.8 match 250 stars 11.57 score 315 scripts

ropensci

tarchetypes:Archetypes for Targets

Function-oriented Make-like declarative pipelines for Statistics and data science are supported in the 'targets' R package. As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the 'targets' R package. by Will Landau (2018) <doi:10.21105/joss.00550>.

Maintained by William Michael Landau. Last updated 19 days ago.

data-science high-performance-computing peer-reviewed pipeline r-targetopia reproducibility targets workflow

16.0 match 141 stars 11.43 score 1.7k scripts 10 dependents

bioc

MetaboDynamics:Bayesian analysis of longitudinal metabolomics data

MetaboDynamics is an R-package that provides a framework of probabilistic models to analyze longitudinal metabolomics data. It enables robust estimation of mean concentrations despite varying spread between timepoints and reports differences between timepoints as well as metabolite specific dynamics profiles that can be used for identifying "dynamics clusters" of metabolites of similar dynamics. Provides probabilistic over-representation analysis of KEGG functional modules and pathways as well as comparison between clusters of different experimental conditions.

Maintained by Katja Danielzik. Last updated 11 hours ago.

software metabolomics bayesian functionalprediction multiplecomparison kegg pathways dynamics functional-analysis longitudinal-analysis metabolomics-data metabolomics-pipeline cpp

32.4 match 5 stars 5.24 score 3 scripts

haotianxu

changepoints:A Collection of Change-Point Detection Methods

Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: <doi:10.1214/20-EJS1710>, <arXiv:2006.03283>; 2) univariate polynomials: <doi:10.1214/21-EJS1963>; 3) univariate and multivariate nonparametric settings: <doi:10.1214/21-EJS1809>, <doi:10.1109/TIT.2021.3130330>; 4) high-dimensional covariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional networks with and without missing values: <doi:10.1214/20-AOS1953>, <arXiv:2101.05477>, <arXiv:2110.06450>; 6) high-dimensional linear regression models: <arXiv:2010.10410>, <arXiv:2207.12453>; 7) high-dimensional vector autoregressive models: <arXiv:1909.06359>; 8) high-dimensional self exciting point processes: <arXiv:2006.03572>; 9) dependent dynamic nonparametric random dot product graphs: <arXiv:1911.07494>; 10) univariate mean against adversarial attacks: <arXiv:2105.10417>.

Maintained by Haotian Xu. Last updated 1 years ago.

openblas cpp

28.2 match 12 stars 5.78 score 25 scripts

skyebend

networkDynamic:Dynamic Extensions for Network Objects

Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.

Maintained by Skye Bender-deMoll. Last updated 4 months ago.

24.4 match 3 stars 6.47 score 132 scripts 11 dependents

choonghyunryu

dlookr:Tools for Data Diagnosis, Exploration, Transformation

A collection of tools that support data diagnosis, exploration, and transformation. Data diagnostics provides information and visualization of missing values, outliers, and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and the relationship between the target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputes missing values and outliers, and resolves skewness. And it creates automated reports that support these three tasks.

Maintained by Choonghyun Ryu. Last updated 9 months ago.

13.9 match 212 stars 11.05 score 748 scripts 2 dependents

ropensci

targets:Dynamic Function-Oriented 'Make'-Like Declarative Pipelines

Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, <doi:10.21105/joss.00550>).

Maintained by William Michael Landau. Last updated 14 hours ago.

data-science high-performance-computing make peer-reviewed pipeline r-targetopia reproducibility reproducible-research targets workflow

9.9 match 973 stars 15.20 score 4.6k scripts 22 dependents

ecospat

ecospat:Spatial Ecology Miscellaneous Methods

Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre, core and post modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) <doi:10.1111/ecog.02671> for details.

Maintained by Olivier Broennimann. Last updated 1 months ago.

15.9 match 32 stars 9.35 score 418 scripts 1 dependents

bart1

move:Visualizing and Analyzing Animal Track Data

Contains functions to access movement data stored in 'movebank.org' as well as tools to visualize and statistically analyze animal movement data, among others functions to calculate dynamic Brownian Bridge Movement Models. Move helps addressing movement ecology questions.

Maintained by Bart Kranstauber. Last updated 4 months ago.

cpp

16.7 match 8.74 score 690 scripts 3 dependents

mspinillos

ecoregime:Analysis of Ecological Dynamic Regimes

A toolbox for implementing the Ecological Dynamic Regime framework (Sánchez-Pinillos et al., 2023 <doi:10.1002/ecm.1589>) to characterize and compare groups of ecological trajectories in multidimensional spaces defined by state variables. The package includes the RETRA-EDR algorithm to identify representative trajectories, functions to generate, summarize, and visualize representative trajectories, and several metrics to quantify the distribution and heterogeneity of trajectories in an ecological dynamic regime and quantify the dissimilarity between two or more ecological dynamic regimes. The package also includes a set of functions to assess ecological resilience based on ecological dynamic regimes (Sánchez-Pinillos et al., 2024 <doi:10.1016/j.biocon.2023.110409>).

Maintained by Martina Sánchez-Pinillos. Last updated 11 months ago.

26.4 match 7 stars 5.32 score 8 scripts

r-a-dobson

dynamicSDM:Species Distribution and Abundance Modelling at High Spatio-Temporal Resolution

A collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution.

Maintained by Rachel Dobson. Last updated 25 days ago.

dynamicsdm google-earth-engine googledrive sdm spatiotemporal spatiotemporal-data-analysis spatiotemporal-forecasting species-distribution-modelling species-distributions

22.6 match 6 stars 6.16 score 20 scripts

kingaa

pomp:Statistical Inference for Partially Observed Markov Processes

Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

Maintained by Aaron A. King. Last updated 1 months ago.

abc b-spline differential-equations dynamical-systems iterated-filtering likelihood likelihood-free markov-chain-monte-carlo markov-model mathematical-modelling measurement-error particle-filter sequential-monte-carlo simulation-based-inference sobol-sequence state-space statistical-inference stochastic-processes time-series openblas

11.8 match 115 stars 11.81 score 1.3k scripts 4 dependents

genentech

psborrow2:Bayesian Dynamic Borrowing Analysis and Simulation

Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <https://stan-dev.r-universe.dev>.

Maintained by Matt Secrest. Last updated 1 months ago.

bayesian-dynamic-borrowing psborrow2 simulation-study

17.2 match 18 stars 7.87 score 16 scripts

biodiverse

unmarked:Models for Data from Unmarked Animals

Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

Maintained by Ken Kellner. Last updated 1 months ago.

openblas cpp openmp

10.2 match 4 stars 13.02 score 652 scripts 12 dependents

dkesada

dbnR:Dynamic Bayesian Network Learning and Inference

Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the 'bnlearn' package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014) <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021) <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the 'visNetwork' package.

Maintained by David Quesada. Last updated 9 months ago.

bayesian-networks dynamic-bayesian-networks forecasting inference time-series cpp

26.6 match 52 stars 4.98 score 37 scripts

tonigi

dtw:Dynamic Time Warping Algorithms

A comprehensive implementation of dynamic time warping (DTW) algorithms in R. DTW computes the optimal (least cumulative distance) alignment between points of two time series. Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on. Provides cumulative distances, alignments, specialized plot styles, etc., as described in Giorgino (2009) <doi:10.18637/jss.v031.i07>.

Maintained by Toni Giorgino. Last updated 2 years ago.

15.3 match 5 stars 8.48 score 582 scripts 49 dependents

biostatomics

Coxmos:Cox MultiBlock Survival

This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) <https://CRAN.R-project.org/package=survival>, Noah Simon et al. (2011) <doi:10.18637/jss.v039.i05>, Philippe Bastien et al. (2005) <doi:10.1016/j.csda.2004.02.005>, Philippe Bastien (2008) <doi:10.1016/j.chemolab.2007.09.009>, Philippe Bastien et al. (2014) <doi:10.1093/bioinformatics/btu660>, Kassu Mehari Beyene and Anouar El Ghouch (2020) <doi:10.1002/sim.8671>, Florian Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>.

Maintained by Pedro Salguero García. Last updated 10 days ago.

24.2 match 1 stars 5.30 score 5 scripts

forestgeo

fgeo:Analyze Forest Diversity and Dynamics

To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to install and load the entire package-collection with a single R command, and provides convenient ways to find relevant documentation. Most commonly, you should not worry about the individual packages that make up the package-collection as you can access all features via this package. To learn more about ForestGEO visit <http://www.forestgeo.si.edu/>.

Maintained by Mauro Lepore. Last updated 5 years ago.

abundance demography dynamic dynamics ecology fgeo forestgeo forests habitat metapackage tree

23.1 match 31 stars 5.50 score 12 scripts

graemeleehickey

joineRML:Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

Maintained by Graeme L. Hickey. Last updated 1 months ago.

armadillo biostatistics clinical-trials cox dynamic joint-models longitudinal-data multivariate-analysis multivariate-data multivariate-longitudinal-data prediction rcpp regression-models statistics survival openblas cpp openmp

13.5 match 30 stars 8.93 score 146 scripts 1 dependents

r-lib

rlang:Functions for Base Types and Core R and 'Tidyverse' Features

A toolbox for working with base types, core R features like the condition system, and core 'Tidyverse' features like tidy evaluation.

Maintained by Lionel Henry. Last updated 18 days ago.

5.8 match 517 stars 20.53 score 9.8k scripts 15k dependents

marshalllab

MGDrivE2:Mosquito Gene Drive Explorer 2

A simulation modeling framework which significantly extends capabilities from the 'MGDrivE' simulation package via a new mathematical and computational framework based on stochastic Petri nets. For more information about 'MGDrivE', see our publication: <https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13318>. Some of the notable capabilities of 'MGDrivE2' include: incorporation of human populations, epidemiological dynamics, time-varying parameters, and a continuous-time simulation framework with various sampling algorithms for both deterministic and stochastic interpretations. 'MGDrivE2' relies on the genetic inheritance structures provided in package 'MGDrivE', so we suggest installing that package initially.

Maintained by Sean L. Wu. Last updated 4 years ago.

17.3 match 6 stars 6.33 score 30 scripts

mjg211

phaseR:Phase Plane Analysis of One- And Two-Dimensional Autonomous ODE Systems

Performs a qualitative analysis of one- and two-dimensional autonomous ordinary differential equation systems, using phase plane methods. Programs are available to identify and classify equilibrium points, plot the direction field, and plot trajectories for multiple initial conditions. In the one-dimensional case, a program is also available to plot the phase portrait. Whilst in the two-dimensional case, programs are additionally available to plot nullclines and stable/unstable manifolds of saddle points. Many example systems are provided for the user. For further details can be found in Grayling (2014) <doi:10.32614/RJ-2014-023>.

Maintained by Michael J Grayling. Last updated 3 years ago.

biological-modeling differential-equations dynamical-systems ecological-modelling lotka-volterra manifolds modeling-dynamic-systems morris-lecar perturbation-analysis phase-plane sir-model species-interactions van-der-pol

16.5 match 15 stars 6.63 score 94 scripts 1 dependents

functionaldata

fdapace:Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Maintained by Yidong Zhou. Last updated 9 months ago.

cpp

9.4 match 31 stars 11.46 score 474 scripts 25 dependents

james-thorson-noaa

dsem:Fit Dynamic Structural Equation Models

Applies dynamic structural equation models to time-series data with generic and simplified specification for simultaneous and lagged effects. Methods are described in Thorson et al. (2024) "Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms."

Maintained by James Thorson. Last updated 4 days ago.

cpp openmp

15.3 match 11 stars 6.90 score 24 scripts

ropensci

dynamite:Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2024) <doi:10.1016/j.alcr.2024.100617>. The package supports joint modeling of multiple measurements per individual, time-varying and time-invariant effects, and a wide range of discrete and continuous distributions. Estimation of these dynamic multivariate panel models is carried out via 'Stan'. For an in-depth tutorial of the package, see (Tikka and Helske, 2024) <doi:10.48550/arXiv.2302.01607>.

Maintained by Santtu Tikka. Last updated 18 days ago.

bayesian-inference panel-data stan statistical-models

13.0 match 29 stars 7.92 score 20 scripts

great-northern-diver

loon:Interactive Statistical Data Visualization

An extendable toolkit for interactive data visualization and exploration.

Maintained by R. Wayne Oldford. Last updated 2 years ago.

data-analysis data-science data-visualization exploratory-analysis exploratory-data-analysis high-dimensional-data interactive-graphics interactive-visualizations loon python statistical-analysis statistical-graphics statistics tcl-extension tk

11.1 match 48 stars 9.00 score 93 scripts 5 dependents

markusfritsch

pdynmc:Moment Condition Based Estimation of Linear Dynamic Panel Data Models

Linear dynamic panel data modeling based on linear and nonlinear moment conditions as proposed by Holtz-Eakin, Newey, and Rosen (1988) <doi:10.2307/1913103>, Ahn and Schmidt (1995) <doi:10.1016/0304-4076(94)01641-C>, and Arellano and Bover (1995) <doi:10.1016/0304-4076(94)01642-D>. Estimation of the model parameters relies on the Generalized Method of Moments (GMM) and instrumental variables (IV) estimation, numerical optimization (when nonlinear moment conditions are employed) and the computation of closed form solutions (when estimation is based on linear moment conditions). One-step, two-step and iterated estimation is available. For inference and specification testing, Windmeijer (2005) <doi:10.1016/j.jeconom.2004.02.005> and doubly corrected standard errors (Hwang, Kang, Lee, 2021 <doi:10.1016/j.jeconom.2020.09.010>) are available. Additionally, serial correlation tests, tests for overidentification, and Wald tests are provided. Functions for visualizing panel data structures and modeling results obtained from GMM estimation are also available. The plot methods include functions to plot unbalanced panel structure, coefficient ranges and coefficient paths across GMM iterations (the latter is implemented according to the plot shown in Hansen and Lee, 2021 <doi:10.3982/ECTA16274>). For a more detailed description of the GMM-based functionality, please see Fritsch, Pua, Schnurbus (2021) <doi:10.32614/RJ-2021-035>. For more details on the IV-based estimation routines, see Fritsch, Pua, and Schnurbus (WP, 2024) and Han and Phillips (2010) <doi:10.1017/S026646660909063X>.

Maintained by Markus Fritsch. Last updated 12 days ago.

14.7 match 4 stars 6.65 score 106 scripts

bioboot

bio3d:Biological Structure Analysis

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

Maintained by Barry Grant. Last updated 5 months ago.

zlib cpp

11.4 match 5 stars 8.49 score 1.4k scripts 10 dependents

jandraor

readsdr:Translate Models from System Dynamics Software into 'R'

The goal of 'readsdr' is to bridge the design capabilities from specialised System Dynamics software with the powerful numerical tools offered by 'R' libraries. The package accomplishes this goal by parsing 'XMILE' files ('Vensim' and 'Stella') models into 'R' objects to construct networks (graph theory); 'ODE' functions for 'Stan'; and inputs to simulate via 'deSolve' as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.

Maintained by Jair Andrade. Last updated 10 months ago.

stan system-dynamics

14.6 match 19 stars 6.62 score 62 scripts

albgarre

bioinactivation:Mathematical Modelling of (Dynamic) Microbial Inactivation

Functions for modelling microbial inactivation under isothermal or dynamic conditions. The calculations are based on several mathematical models broadly used by the scientific community and industry. Functions enable to make predictions for cases where the kinetic parameters are known. It also implements functions for parameter estimation for isothermal and dynamic conditions. The model fitting capabilities include an Adaptive Monte Carlo method for a Bayesian approach to parameter estimation.

Maintained by Alberto Garre. Last updated 2 years ago.

food food-safety inactivation-models isothermal-experiments prediction

24.1 match 3.95 score 18 scripts

jedalong

wildlifeDI:Calculate Indices of Dynamic Interaction for Wildlife Tracking Data

Dynamic interaction refers to spatial-temporal associations in the movements of two (or more) animals. This package provides tools for calculating a suite of indices used for quantifying dynamic interaction with wildlife telemetry data. For more information on each of the methods employed see the references within. The package (as of version >= 0.3) also has new tools for automating contact analysis in large tracking datasets. The package (as of version 1.0) uses the 'move2' class of objects for working with tracking dataset.

Maintained by Jed Long. Last updated 10 months ago.

14.2 match 16 stars 6.70 score 31 scripts

waternumbers

dynatop:An Implementation of Dynamic TOPMODEL Hydrological Model in R

An R implementation and enhancement of the Dynamic TOPMODEL semi-distributed hydrological model originally proposed by Beven and Freer (2001) <doi:10.1002/hyp.252>. The 'dynatop' package implements code for simulating models which can be created using the 'dynatopGIS' package.

Maintained by Paul Smith. Last updated 4 months ago.

cpp

16.8 match 3 stars 5.08 score 9 scripts

fishr-core-team

FSA:Simple Fisheries Stock Assessment Methods

A variety of simple fish stock assessment methods.

Maintained by Derek H. Ogle. Last updated 2 months ago.

fish fisheries fisheries-management fisheries-stock-assessment population-dynamics stock-assessment

7.5 match 68 stars 11.08 score 1.7k scripts 6 dependents

rstudio

shiny:Web Application Framework for R

Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.

Maintained by Winston Chang. Last updated 12 days ago.

reactive rstudio shiny web-app web-development

3.8 match 5.4k stars 21.28 score 108k scripts 1.8k dependents

softwareliteracy

rEDM:Empirical Dynamic Modeling ('EDM')

An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO'). The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library. It implements the 'simplex' projection method from Sugihara & May (1990) <doi:10.1038/344734a0>, the 'S-map' algorithm from Sugihara (1994) <doi:10.1098/rsta.1994.0106>, convergent cross mapping described in Sugihara et al. (2012) <doi:10.1126/science.1227079>, and, 'multiview embedding' described in Ye & Sugihara (2016) <doi:10.1126/science.aag0863>.

Maintained by Joseph Park. Last updated 11 months ago.

openblas cpp

13.0 match 2 stars 6.05 score 319 scripts 1 dependents

bioc

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

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

Maintained by Francesco Gazzo. Last updated 3 months ago.

biomedicalinformatics sequencing singlecell

13.4 match 3 stars 5.83 score 15 scripts

canmod

macpan2:Fast and Flexible Compartmental Modelling

Fast and flexible compartmental modelling with Template Model Builder.

Maintained by Steve Walker. Last updated 15 hours ago.

compartmental-models epidemiology forecasting mixed-effects model-fitting optimization simulation simulation-modeling cpp

8.8 match 4 stars 8.89 score 246 scripts 1 dependents

nceas

codyn:Community Dynamics Metrics

Univariate and multivariate temporal and spatial diversity indices, rank abundance curves, and community stability measures. The functions implement measures that are either explicitly temporal and include the option to calculate them over multiple replicates, or spatial and include the option to calculate them over multiple time points. Functions fall into five categories: static diversity indices, temporal diversity indices, spatial diversity indices, rank abundance curves, and community stability measures. The diversity indices are temporal and spatial analogs to traditional diversity indices. Specifically, the package includes functions to calculate community richness, evenness and diversity at a given point in space and time. In addition, it contains functions to calculate species turnover, mean rank shifts, and lags in community similarity between two time points. Details of the methods are available in Hallett et al. (2016) <doi:10.1111/2041-210X.12569> and Avolio et al. (2019) <doi:10.1002/ecs2.2881>.

Maintained by Matthew B. Jones. Last updated 4 years ago.

8.5 match 34 stars 9.07 score 230 scripts

emilhvitfeldt

paletteer:Comprehensive Collection of Color Palettes

The choices of color palettes in R can be quite overwhelming with palettes spread over many packages with many different API's. This packages aims to collect all color palettes across the R ecosystem under the same package with a streamlined API.

Maintained by Emil Hvitfeldt. Last updated 9 months ago.

color-palette palettes

5.5 match 957 stars 13.50 score 6.9k scripts 23 dependents

openair-project

openairmaps:Create Maps of Air Pollution Data

Combine the air quality data analysis methods of 'openair' with the JavaScript 'Leaflet' (<https://leafletjs.com/>) library. Functionality includes plotting site maps, "directional analysis" figures such as polar plots, and air mass trajectories.

Maintained by Jack Davison. Last updated 5 days ago.

openair

12.4 match 21 stars 6.04 score 47 scripts

robson-fernandes

dbnlearn:Dynamic Bayesian Network Structure Learning, Parameter Learning and Forecasting

It allows to learn the structure of univariate time series, learning parameters and forecasting. Implements a model of Dynamic Bayesian Networks with temporal windows, with collections of linear regressors for Gaussian nodes, based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.

Maintained by Robson Fernandes. Last updated 5 years ago.

bayesian-inference bayesian-networks dynamic-bayesian-networks probabilistic-graphical-models time-series

17.2 match 16 stars 4.32 score 26 scripts

rozetasimonovska

SDPDmod:Spatial Dynamic Panel Data Modeling

Spatial model calculation for static and dynamic panel data models, weights matrix creation and Bayesian model comparison. Bayesian model comparison methods were described by 'LeSage' (2014) <doi:10.1016/j.spasta.2014.02.002>. The 'Lee'-'Yu' transformation approach is described in 'Yu', 'De Jong' and 'Lee' (2008) <doi:10.1016/j.jeconom.2008.08.002>, 'Lee' and 'Yu' (2010) <doi:10.1016/j.jeconom.2009.08.001> and 'Lee' and 'Yu' (2010) <doi:10.1017/S0266466609100099>.

Maintained by Rozeta Simonovska. Last updated 11 months ago.

14.9 match 5 stars 4.98 score 19 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 6 months ago.

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

6.7 match 64 stars 10.97 score 242 scripts 16 dependents

critical-infrastructure-systems-lab

ldsr:Linear Dynamical System Reconstruction

Streamflow (and climate) reconstruction using Linear Dynamical Systems. The advantage of this method is the additional state trajectory which can reveal more information about the catchment or climate system. For details of the method please refer to Nguyen and Galelli (2018) <doi:10.1002/2017WR022114>.

Maintained by Hung Nguyen. Last updated 5 years ago.

expectation-maximization-algorithm hydrology kalman-smoother linear-dynamical-systems paleoclimate openblas cpp openmp

15.0 match 8 stars 4.86 score 18 scripts

rstudio

rmarkdown:Dynamic Documents for R

Convert R Markdown documents into a variety of formats.

Maintained by Yihui Xie. Last updated 4 months ago.

literate-programming markdown pandoc rmarkdown

3.3 match 2.9k stars 21.79 score 14k scripts 3.7k dependents

braverock

PerformanceAnalytics:Econometric Tools for Performance and Risk Analysis

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

Maintained by Brian G. Peterson. Last updated 3 months ago.

4.5 match 222 stars 15.93 score 4.8k scripts 20 dependents

epiverse-trace

epidemics:Composable Epidemic Scenario Modelling

A library of compartmental epidemic models taken from the published literature, and classes to represent affected populations, public health response measures including non-pharmaceutical interventions on social contacts, non-pharmaceutical and pharmaceutical interventions that affect disease transmissibility, vaccination regimes, and disease seasonality, which can be combined to compose epidemic scenario models.

Maintained by Rosalind Eggo. Last updated 9 months ago.

decision-support epidemic-modelling epidemic-simulations epidemiology epiverse infectious-disease-dynamics model-library non-pharmaceutical-interventions rcpp rcppeigen scenario-analysis vaccination cpp

9.6 match 9 stars 7.48 score 59 scripts

vegandevs

vegan3d:Static and Dynamic 3D and Editable Interactive Plots for the 'vegan' Package

Static and dynamic 3D plots to be used with ordination results and in diversity analysis, especially with the vegan package.

Maintained by Jari Oksanen. Last updated 23 days ago.

10.4 match 19 stars 6.86 score 67 scripts 1 dependents

rstudio

bslib:Custom 'Bootstrap' 'Sass' Themes for 'shiny' and 'rmarkdown'

Simplifies custom 'CSS' styling of both 'shiny' and 'rmarkdown' via 'Bootstrap' 'Sass'. Supports 'Bootstrap' 3, 4 and 5 as well as their various 'Bootswatch' themes. An interactive widget is also provided for previewing themes in real time.

Maintained by Carson Sievert. Last updated 10 days ago.

bootstrap htmltools rmarkdown sass shiny

3.9 match 511 stars 18.02 score 5.1k scripts 4.3k dependents

abhirupkgp

dnr:Simulate Dynamic Networks using Exponential Random Graph Models (ERGM) Family

Functions are provided to fit temporal lag models to dynamic networks. The models are build on top of exponential random graph models (ERGM) framework. There are functions for simulating or forecasting networks for future time points. Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, <DOI: 10.1080/10618600.2019.1594834>.

Maintained by Abhirup Mallik. Last updated 4 years ago.

23.5 match 2.95 score 18 scripts

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 6 months ago.

dynamic-factor-models time-series openblas cpp

12.4 match 31 stars 5.57 score 12 scripts

flujoo

gm:Create Music with Ease

Provides a simple and intuitive high-level language for music representation. Generates and embeds music scores and audio files in 'RStudio', 'R Markdown' documents, and R 'Jupyter Notebooks'. Internally, uses 'MusicXML' <https://github.com/w3c/musicxml> to represent music, and 'MuseScore' <https://musescore.org/> to convert 'MusicXML'.

Maintained by Renfei Mao. Last updated 8 months ago.

algorithmic-composition music-programming musicxml

8.5 match 207 stars 8.06 score 35 scripts

schw4b

DGM:Dynamic Graphical Models

Dynamic graphical models for multivariate time series data to estimate directed dynamic networks in functional magnetic resonance imaging (fMRI), see Schwab et al. (2017) <doi:10.1016/j.neuroimage.2018.03.074>.

Maintained by Simon Schwab. Last updated 3 years ago.

dynamic-graphical-models functional-connectivity time-varying-connectivity openblas cpp openmp

12.4 match 25 stars 5.49 score 25 scripts

goldingn

pop:A Flexible Syntax for Population Dynamic Modelling

Population dynamic models underpin a range of analyses and applications in ecology and epidemiology. The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways.

Maintained by Nick Golding. Last updated 9 years ago.

13.9 match 10 stars 4.88 score 15 scripts

paramita-sc

risksetROC:Riskset ROC Curve Estimation from Censored Survival Data

Compute time-dependent Incident/dynamic accuracy measures (ROC curve, AUC, integrated AUC )from censored survival data under proportional or non-proportional hazard assumption of Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).

Maintained by Paramita Saha-Chaudhuri. Last updated 3 years ago.

18.3 match 3.71 score 57 scripts 3 dependents

constantino-garcia

nonlinearTseries:Nonlinear Time Series Analysis

Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).

Maintained by Constantino A. Garcia. Last updated 6 months ago.

chaos chaotic-systems nonlinear-dynamics nonlinear-time-series time-series openblas cpp

7.5 match 35 stars 8.98 score 123 scripts 7 dependents

divdyn

divDyn:Diversity Dynamics using Fossil Sampling Data

Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well as other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) <doi:10.1101/423780>.

Maintained by Adam T. Kocsis. Last updated 4 months ago.

diversity extinction fossil-data occurrences origination paleobiology cpp

10.3 match 11 stars 6.48 score 137 scripts

geco-bern

rsofun:The P-Model and BiomeE Modelling Framework

Implements the Simulating Optimal FUNctioning framework for site-scale simulations of ecosystem processes, including model calibration. It contains 'Fortran 90' modules for the P-model (Stocker et al. (2020) <doi:10.5194/gmd-13-1545-2020>), SPLASH (Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>) and BiomeE (Weng et al. (2015) <doi:10.5194/bg-12-2655-2015>).

Maintained by Benjamin Stocker. Last updated 12 days ago.

dgvm growth modeling p-model simulation vegetation-dynamics fortran

7.5 match 26 stars 8.77 score 119 scripts

slwu89

MicroMoB:Discrete Time Simulation of Mosquito-Borne Pathogen Transmission

Provides a framework based on S3 dispatch for constructing models of mosquito-borne pathogen transmission which are constructed from submodels of various components (i.e. immature and adult mosquitoes, human populations). A consistent mathematical expression for the distribution of bites on hosts means that different models (stochastic, deterministic, etc.) can be coherently incorporated and updated over a discrete time step.

Maintained by Sean L. Wu. Last updated 2 years ago.

15.3 match 4.16 score 32 scripts

hujiebai

DPTM:Dynamic Panel Multiple Threshold Model with Fixed Effects

Compute the fixed effects dynamic panel threshold model suggested by Ramírez-Rondán (2020) <doi:10.1080/07474938.2019.1624401>, and dynamic panel linear model suggested by Hsiao et al. (2002) <doi:10.1016/S0304-4076(01)00143-9>, where maximum likelihood type estimators are used. Multiple threshold estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed.

Maintained by Bai Hujie. Last updated 1 months ago.

dynamic panel-data thresholds cpp

17.1 match 2 stars 3.70 score

jhstaudacher

EvolutionaryGames:Important Concepts of Evolutionary Game Theory

Evolutionary game theory applies game theory to evolving populations in biology, see e.g. one of the books by Weibull (1994, ISBN:978-0262731218) or by Sandholm (2010, ISBN:978-0262195874) for more details. A comprehensive set of tools to illustrate the core concepts of evolutionary game theory, such as evolutionary stability or various evolutionary dynamics, for teaching and academic research is provided.

Maintained by Jochen Staudacher. Last updated 3 years ago.

20.3 match 2 stars 3.11 score 32 scripts

stscl

spEDM:Spatial Empirical Dynamic Modeling

Inferring causal associations in cross-sectional earth system data through empirical dynamic modeling (EDM), with extensions to convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping as outlined in Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality as described in Tao et al. (2023)<doi:10.1016/j.fmre.2023.01.007>.

Maintained by Wenbo Lv. Last updated 22 hours ago.

causal-inference cpp empirical-dynamic-modeling geoinformatics geospatial-causality spatial-statistics openblas cpp openmp

10.3 match 15 stars 6.05 score 2 scripts

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

8.1 match 9 stars 7.65 score 470 scripts 11 dependents

franzmohr

bvartools:Bayesian Inference of Vector Autoregressive and Error Correction Models

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

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

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

9.1 match 31 stars 6.80 score 34 scripts 1 dependents

sciurus365

simlandr:Simulation-Based Landscape Construction for Dynamical Systems

A toolbox for constructing potential landscapes for dynamical systems using Monte Carlo simulation. The method is based on the potential landscape definition by Wang et al. (2008) <doi:10.1073/pnas.0800579105> (also see Zhou & Li, 2016 <doi:10.1063/1.4943096> for further mathematical discussions) and can be used for a large variety of models.

Maintained by Jingmeng Cui. Last updated 1 months ago.

research-tool

9.6 match 6 stars 6.41 score 12 scripts 2 dependents

nimble-dev

nimbleEcology:Distributions for Ecological Models in 'nimble'

Common ecological distributions for 'nimble' models in the form of nimbleFunction objects. Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models. (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).

Maintained by Benjamin R. Goldstein. Last updated 2 months ago.

8.8 match 18 stars 7.01 score 94 scripts

acguidoum

Sim.DiffProc:Simulation of Diffusion Processes

It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.

Maintained by Arsalane Chouaib Guidoum. Last updated 1 years ago.

dynamic-system moment-equations monte-carlo-simulation parallel-computing stochastic-calculus stochastic-differential-equation transition-density

8.0 match 13 stars 7.69 score 86 scripts 4 dependents

tsmodels

tsmarch:Multivariate ARCH Models

Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.

Maintained by Alexios Galanos. Last updated 3 months ago.

econometrics finance garch multivariate-timeseries time-series openblas cpp

10.9 match 6 stars 5.65 score 3 scripts

bcallaway11

did:Treatment Effects with Multiple Periods and Groups

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

Maintained by Brantly Callaway. Last updated 4 months ago.

5.1 match 327 stars 12.01 score 696 scripts 3 dependents

bioc

DTA:Dynamic Transcriptome Analysis

Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements.

Maintained by Bjoern Schwalb. Last updated 5 months ago.

microarray differentialexpression geneexpression transcription

12.8 match 4.78 score 5 scripts 1 dependents

asardaes

dtwclust:Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance

Time series clustering along with optimized techniques related to the Dynamic Time Warping distance and its corresponding lower bounds. Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included.

Maintained by Alexis Sarda. Last updated 8 months ago.

clustering dtw time-series openblas cpp

5.0 match 261 stars 12.39 score 406 scripts 14 dependents

blasbenito

distantia:Advanced Toolset for Efficient Time Series Dissimilarity Analysis

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

Maintained by Blas M. Benito. Last updated 24 days ago.

10.5 match 23 stars 5.76 score 11 scripts

teos-10

gsw:Gibbs Sea Water Functions

Provides an interface to the Gibbs 'SeaWater' ('TEOS-10') C library, version 3.06-16-0 (commit '657216dd4f5ea079b5f0e021a4163e2d26893371', dated 2022-10-11, available at <https://github.com/TEOS-10/GSW-C>, which stems from 'Matlab' and other code written by members of Working Group 127 of 'SCOR'/'IAPSO' (Scientific Committee on Oceanic Research / International Association for the Physical Sciences of the Oceans).

Maintained by Dan Kelley. Last updated 7 days ago.

gibbs oceanography seawater teos-10

7.1 match 8 stars 8.53 score 286 scripts 19 dependents

tidyverse

rvest:Easily Harvest (Scrape) Web Pages

Wrappers around the 'xml2' and 'httr' packages to make it easy to download, then manipulate, HTML and XML.

Maintained by Hadley Wickham. Last updated 5 months ago.

html web-scraping

3.0 match 1.5k stars 19.62 score 29k scripts 546 dependents

traitecoevo

plant:A Package for Modelling Forest Trait Ecology and Evolution

Solves trait, size and patch structured model from (Falster et al. 2016) using either method of characteristics or as stochastic, finite-sized population.

Maintained by Daniel Falster. Last updated 6 days ago.

c-plus-plus demography dynamic ecology evolution forests plant-physiology science-research simulation trait cpp

10.0 match 53 stars 5.87 score

statistics-in-portfolio-theory

DOSPortfolio:Dynamic Optimal Shrinkage Portfolio

Constructs dynamic optimal shrinkage estimators for the weights of the global minimum variance portfolio which are reconstructed at given reallocation points as derived in Bodnar, Parolya, and Thorsén (2021) (<arXiv:2106.02131>). Two dynamic shrinkage estimators are available in this package. One using overlapping samples while the other use nonoverlapping samples.

Maintained by Erik Thorsén. Last updated 4 years ago.

13.5 match 4 stars 4.30 score 4 scripts

critical-infrastructure-systems-lab

reservoir:Tools for Analysis, Design, and Operation of Water Supply Storages

Measure single-storage water supply system performance using resilience, reliability, and vulnerability metrics; assess storage-yield- reliability relationships; determine no-fail storage with sequent peak analysis; optimize release decisions for water supply, hydropower, and multi-objective reservoirs using deterministic and stochastic dynamic programming; generate inflow replicates using parametric and non-parametric models; evaluate inflow persistence using the Hurst coefficient.

Maintained by Sean Turner. Last updated 4 years ago.

hydrology reservoir simulation water-resources

14.5 match 28 stars 4.00 score 18 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

9.2 match 12 stars 6.18 score 28 scripts

fate-ewi

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

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

Maintained by Eric J. Ward. Last updated 5 months ago.

cpp

6.8 match 28 stars 8.28 score 101 scripts

epiverse-trace

epichains:Simulating and Analysing Transmission Chain Statistics Using Branching Process Models

Provides methods to simulate and analyse the size and length of branching processes with an arbitrary offspring distribution. These can be used, for example, to analyse the distribution of chain sizes or length of infectious disease outbreaks, as discussed in Farrington et al. (2003) <doi:10.1093/biostatistics/4.2.279>.

Maintained by James M. Azam. Last updated 3 days ago.

branching-processes epidemic-dynamics epidemic-modelling epidemic-simulations epidemiology epidemiology-models outbreak-simulator transmission-chain transmission-chain-reconstruction

7.5 match 7 stars 7.48 score 8 scripts

jeromeecoac

seewave:Sound Analysis and Synthesis

Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound). This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) <doi:10.1080/09524622.2008.9753600> and Sueur (2018) <doi:10.1007/978-3-319-77647-7>.

Maintained by Jerome Sueur. Last updated 1 years ago.

6.3 match 18 stars 8.84 score 880 scripts 23 dependents

kvasilopoulos

exuber:Econometric Analysis of Explosive Time Series

Testing for and dating periods of explosive dynamics (exuberance) in time series using the univariate and panel recursive unit root tests proposed by Phillips et al. (2015) <doi:10.1111/iere.12132> and Pavlidis et al. (2016) <doi:10.1007/s11146-015-9531-2>.The recursive least-squares algorithm utilizes the matrix inversion lemma to avoid matrix inversion which results in significant speed improvements. Simulation of a variety of periodically-collapsing bubble processes. Details can be found in Vasilopoulos et al. (2022) <doi:10.18637/jss.v103.i10>.

Maintained by Kostas Vasilopoulos. Last updated 12 months ago.

dickey-fuller explosive-dynamics simulation time-series openblas cpp

8.0 match 29 stars 6.83 score 77 scripts

iainmstott

popdemo:Demographic Modelling Using Projection Matrices

Tools for modelling populations and demography using matrix projection models, with deterministic and stochastic model implementations. Includes population projection, indices of short- and long-term population size and growth, perturbation analysis, convergence to stability or stationarity, and diagnostic and manipulation tools.

Maintained by Iain Stott. Last updated 3 years ago.

10.5 match 5.16 score 172 scripts 7 dependents

higuchip

forestdynR:Calculate Forest Dynamics

Determines the dynamics of tree species communities (mortality rates, recruitment, loss and gain in basal area, net changes and turnover). Important notes are a) The 'forest_df' argument (data) must contain the columns 'plot' (plot identification), 'spp' (species identification), DBH_1 (Diameter at breast height in first year of measure) and DBH_2 (Diameter at breast height in second year of measure). DBH_1 and DBH_2 must be numeric values; b) example input file in 'data(forest_df_example)'; c) The argument 'inv_time' represents the time between inventories, in years; d) The 'coord' argument must be of the type 'c(longitude, latitude)', with decimal degree values; e) Argument 'add_wd' represents a dataframe with wood density values (g cm-3) format with three columns ('genus', 'species', 'wd'). This argument is set to NULL by default, and if isn't provided, the wood density will be estimated with the getWoodDensity() function from the 'BIOMASS' package.

Maintained by Pedro Higuchi. Last updated 4 months ago.

11.3 match 4 stars 4.78 score 4 scripts

drizopoulos

JMbayes:Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach

Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.

Maintained by Dimitris Rizopoulos. Last updated 4 years ago.

joint-models longitudinal-responses prediction-model survival-analysis openblas cpp openmp jags

7.7 match 60 stars 6.98 score 80 scripts

sbgraves237

Ecdat:Data Sets for Econometrics

Data sets for econometrics, including political science.

Maintained by Spencer Graves. Last updated 3 months ago.

7.3 match 2 stars 7.25 score 740 scripts 3 dependents

rstudio

shinydashboard:Create Dashboards with 'Shiny'

Create dashboards with 'Shiny'. This package provides a theme on top of 'Shiny', making it easy to create attractive dashboards.

Maintained by Winston Chang. Last updated 3 years ago.

admin-dashboard dashboard reactivity rstudio shiny shinydashboard web-app web-development

3.5 match 906 stars 15.30 score 17k scripts 208 dependents

nanxstats

liftr:Containerize R Markdown Documents for Continuous Reproducibility

Persistent reproducible reporting by containerization of R Markdown documents.

Maintained by Nan Xiao. Last updated 1 years ago.

containerization docker dynamic-documents knitr liftr reproducible-research reproducible-science rmarkdown statistical-computing

7.5 match 172 stars 7.03 score 21 scripts

bodkan

slendr:A Simulation Framework for Spatiotemporal Population Genetics

A framework for simulating spatially explicit genomic data which leverages real cartographic information for programmatic and visual encoding of spatiotemporal population dynamics on real geographic landscapes. Population genetic models are then automatically executed by the 'SLiM' software by Haller et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes, using a custom built-in simulation 'SLiM' script. Additionally, fully abstract spatial models not tied to a specific geographic location are supported, and users can also simulate data from standard, non-spatial, random-mating models. These can be simulated either with the 'SLiM' built-in back-end script, or using an efficient coalescent population genetics simulator 'msprime' by Baumdicker et al. (2022) <doi:10.1093/genetics/iyab229> with a custom-built 'Python' script bundled with the R package. Simulated genomic data is saved in a tree-sequence format and can be loaded, manipulated, and summarised using tree-sequence functionality via an R interface to the 'Python' module 'tskit' by Kelleher et al. (2019) <doi:10.1038/s41588-019-0483-y>. Complete model configuration, simulation and analysis pipelines can be therefore constructed without a need to leave the R environment, eliminating friction between disparate tools for population genetic simulations and data analysis.

Maintained by Martin Petr. Last updated 11 days ago.

popgen population-genetics simulations spatial-statistics

5.7 match 56 stars 9.15 score 88 scripts

usccana

netdiffuseR:Analysis of Diffusion and Contagion Processes on Networks

Empirical statistical analysis, visualization and simulation of diffusion and contagion processes on networks. The package implements algorithms for calculating network diffusion statistics such as transmission rate, hazard rates, exposure models, network threshold levels, infectiousness (contagion), and susceptibility. The package is inspired by work published in Valente, et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN: 9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others (2011) <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among others.

Maintained by George Vega Yon. Last updated 3 months ago.

contagion diffusion-network network-analysis network-visualization openblas cpp openmp

5.8 match 88 stars 8.88 score 217 scripts

pfh

langevitour:Langevin Tour

An HTML widget that randomly tours 2D projections of numerical data. A random walk through projections of the data is shown. The user can manipulate the plot to use specified axes, or turn on Guided Tour mode to find an informative projection of the data. Groups within the data can be hidden or shown, as can particular axes. Points can be brushed, and the selection can be linked to other widgets using crosstalk. The underlying method to produce the random walk and projection pursuit uses Langevin dynamics. The widget can be used from within R, or included in a self-contained R Markdown or Quarto document or presentation, or used in a Shiny app.

Maintained by Paul Harrison. Last updated 1 months ago.

javascript-applications langevin-dynamics tour visualization

8.0 match 26 stars 6.41 score 22 scripts 1 dependents

leifeld

btergm:Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.

Maintained by Philip Leifeld. Last updated 12 months ago.

complex-networks dynamic-analysis ergm estimation goodness-of-fit inference longitudinal-data network-analysis prediction tergm

7.5 match 17 stars 6.70 score 83 scripts 2 dependents

jongheepark

MCMCpack:Markov Chain Monte Carlo (MCMC) Package

Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.

Maintained by Jong Hee Park. Last updated 7 months ago.

cpp

5.3 match 13 stars 9.40 score 2.6k scripts 150 dependents

nredell

forecastML:Time Series Forecasting with Machine Learning Methods

The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.

Maintained by Nickalus Redell. Last updated 5 years ago.

deep-learning direct-forecasting forecast forecasting machine-learning multi-step-ahead-forecasting neural-network python time-series

6.5 match 131 stars 7.64 score 134 scripts

rsetienne

DAISIE:Dynamical Assembly of Islands by Speciation, Immigration and Extinction

Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See Valente et al. (2015) <doi:10.1111/ele.12461>.

Maintained by Rampal S. Etienne. Last updated 1 months ago.

fortran cpp

5.7 match 9 stars 8.59 score 55 scripts 1 dependents

sehellmann

dynConfiR:Dynamic Models for Confidence and Response Time Distributions

Provides density functions for the joint distribution of choice, response time and confidence for discrete confidence judgments as well as functions for parameter fitting, prediction and simulation for various dynamical models of decision confidence. All models are explained in detail by Hellmann et al. (2023; Preprint available at <https://osf.io/9jfqr/>, published version: <doi:10.1037/rev0000411>). Implemented models are the dynaViTE model, dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, <doi:10.1037/a0019737>), and various race models. C++ code for dynWEV and 2DSD is based on the 'rtdists' package by Henrik Singmann.

Maintained by Sebastian Hellmann. Last updated 15 days ago.

cpp

9.0 match 3 stars 5.43 score 18 scripts

forestgeo

fgeo.tool:Import and Manipulate 'ForestGEO' Data

To help you access, transform, analyze, and visualize 'ForestGEO' data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to easily import, filter, and modify 'ForestGEO' data. To learn more about 'ForestGEO' visit <https://forestgeo.si.edu/>.

Maintained by Mauro Lepore. Last updated 3 years ago.

dynamics ecology fgeo forestgeo miscelaneas tools tree utils

10.0 match 2 stars 4.86 score 27 scripts 3 dependents

dylanb95

statespacer:State Space Modelling in 'R'

A tool that makes estimating models in state space form a breeze. See "Time Series Analysis by State Space Methods" by Durbin and Koopman (2012, ISBN: 978-0-19-964117-8) for details about the algorithms implemented.

Maintained by Dylan Beijers. Last updated 2 years ago.

cpp dynamic-linear-model forecasting gaussian-models kalman-filter mathematical-modelling state-space statistical-inference statistical-models structural-analysis time-series openblas cpp openmp

7.9 match 15 stars 6.14 score 37 scripts

rinterface

shinydashboardPlus:Add More 'AdminLTE2' Components to 'shinydashboard'

Extend 'shinydashboard' with 'AdminLTE2' components. 'AdminLTE2' is a free 'Bootstrap 3' dashboard template available at <https://adminlte.io>. Customize boxes, add timelines and a lot more.

Maintained by David Granjon. Last updated 8 months ago.

dashboard hacktoberfest2022 shiny shiny-apps shinydashboard

3.5 match 459 stars 13.79 score 1.1k scripts 28 dependents

rsetienne

DDD:Diversity-Dependent Diversification

Implements maximum likelihood and bootstrap methods based on the diversity-dependent birth-death process to test whether speciation or extinction are diversity-dependent, under various models including various types of key innovations. See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, <DOI:10.1098/rspb.2011.1439>, Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, <DOI:10.1086/667574>, Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099, <DOI:10.1111/2041-210X.12565> and Laudanno et al. 2021. Syst. Biol. 70: 389–407, <DOI:10.1093/sysbio/syaa048>. Also contains functions to simulate the diversity-dependent process.

Maintained by Rampal S. Etienne. Last updated 4 months ago.

cpp

6.8 match 4 stars 7.10 score 80 scripts 7 dependents

xavi-rp

LPDynR:Land Productivity Dynamics Indicator

It uses 'phenological' and productivity-related variables derived from time series of vegetation indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing productivity. See www.sciencedirect.com/science/article/pii/S1470160X21010517/ for a description of the methods used in the package to calculate the indicator.

Maintained by Xavier Rotllan-Puig. Last updated 6 months ago.

copernicus-global-land-service earth-observation land-degradation land-productivity vegetation

9.7 match 8 stars 4.90 score 5 scripts

r-forge

carData:Companion to Applied Regression Data Sets

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

Maintained by John Fox. Last updated 5 months ago.

3.8 match 12.41 score 944 scripts 919 dependents

raim

dpseg:Piecewise Linear Segmentation by Dynamic Programming

Piecewise linear segmentation of ordered data by a dynamic programming algorithm. The algorithm was developed for time series data, e.g. growth curves, and for genome-wide read-count data from next generation sequencing, but is broadly applicable. Generic implementations of dynamic programming routines allow to scan for optimal segmentation parameters and test custom segmentation criteria ("scoring functions").

Maintained by Rainer Machne. Last updated 25 days ago.

cpp

9.8 match 4.71 score 17 scripts 1 dependents

esmucler

gdpc:Generalized Dynamic Principal Components

Functions to compute the Generalized Dynamic Principal Components introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02> for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy.

Maintained by Ezequiel Smucler. Last updated 1 years ago.

openblas cpp

12.8 match 5 stars 3.57 score 15 scripts

statnet

ndtv:Network Dynamic Temporal Visualizations

Renders dynamic network data from 'networkDynamic' objects as movies, interactive animations, or other representations of changing relational structures and attributes.

Maintained by Skye Bender-deMoll. Last updated 8 months ago.

5.5 match 52 stars 8.36 score 254 scripts 1 dependents

fbrun-acta

ZeBook:Working with Dynamic Models for Agriculture and Environment

R package accompanying the book Working with dynamic models for agriculture and environment, by Daniel Wallach (INRA), David Makowski (INRA), James W. Jones (U.of Florida), Francois Brun (ACTA). 3rd edition 2018-09-27.

Maintained by Francois Brun. Last updated 6 years ago.

19.1 match 4 stars 2.37 score 59 scripts

mattiminn

dpasurv:Dynamic Path Analysis of Survival Data via Aalen's Additive Hazards Model

Dynamic path analysis with estimation of the corresponding direct, indirect, and total effects, based on Fosen et al., (2006) <doi:10.1007/s10985-006-9004-2>. The main outcome of interest is a counting process from survival analysis (or recurrent events) data. At each time of event, ordinary linear regression is used to estimate the relation between the covariates, while Aalen's additive hazard model is used for the regression of the counting process on the covariates.

Maintained by Matthias Kormaksson. Last updated 6 months ago.

13.4 match 3.36 score 23 scripts

fmestre1

MetaLandSim:Landscape and Range Expansion Simulation

Tools to generate random landscape graphs, evaluate species occurrence in dynamic landscapes, simulate future landscape occupation and evaluate range expansion when new empty patches are available (e.g. as a result of climate change). References: Mestre, F., Canovas, F., Pita, R., Mira, A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., Risk, B., Mira, A., Beja, P., Pita, R. (2017) <doi:10.1016/j.ecolmodel.2017.06.013>; Mestre, F., Pita, R., Mira, A., Beja, P. (2020) <doi:10.1186/s12898-019-0273-5>.

Maintained by Frederico Mestre. Last updated 2 years ago.

biogeography ecology metapopulation species

8.8 match 3 stars 5.10 score 28 scripts

uzh-peg

microxanox:Oxic-Anoxic Regime Shifts in Microbial Communities

Model to simulate a three functional group system with four chemical substrates using a set of ordinary differential equations. Simulations can be run individually or over a parameter range, to find stable states. The model features multiple species per functional group, where the number is only limited by computational constraints. The R package is constructed in such a way, that the results contain the input parameter used, so that a saved results can be loaded again and thesimulation be repeated.

Maintained by Owen L. Petchey. Last updated 1 years ago.

11.7 match 3.85 score 35 scripts

jobnmadu

Dyn4cast:Dynamic Modeling and Machine Learning Environment

Estimates, predict and forecast dynamic models as well as Machine Learning metrics which assists in model selection for further analysis. The package also have capabilities to provide tools and metrics that are useful in machine learning and modeling. For example, there is quick summary, percent sign, Mallow's Cp tools and others. The ecosystem of this package is analysis of economic data for national development. The package is so far stable and has high reliability and efficiency as well as time-saving.

Maintained by Job Nmadu. Last updated 5 days ago.

data-science equal-lenght-forecast forecasting knots machine-learning nigeria prediction regression-models spline-models statistics time-series

8.8 match 4 stars 5.06 score 38 scripts

rinterface

bs4Dash:A 'Bootstrap 4' Version of 'shinydashboard'

Make 'Bootstrap 4' Shiny dashboards. Use the full power of 'AdminLTE3', a dashboard template built on top of 'Bootstrap 4' <https://github.com/ColorlibHQ/AdminLTE>.

Maintained by David Granjon. Last updated 6 months ago.

bootstrap4 dashboard-templates hacktoberfest2022 shiny shiny-apps shinydashboard

3.5 match 442 stars 12.87 score 1.2k scripts 15 dependents

cran

astrochron:A Computational Tool for Astrochronology

Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.

Maintained by Stephen Meyers. Last updated 6 months ago.

fortran

11.5 match 5 stars 3.85 score 141 scripts

rstudio

swagger:Dynamically Generates Documentation from a 'Swagger' Compliant API

A collection of 'HTML', 'JavaScript', and 'CSS' assets that dynamically generate beautiful documentation from a 'Swagger' compliant API: <https://swagger.io/specification/>.

Maintained by Bruno Tremblay. Last updated 9 months ago.

5.2 match 54 stars 8.56 score 14 scripts 19 dependents

marco-bee

FitDynMix:Estimation of Dynamic Mixtures

Estimation of a dynamic lognormal - Generalized Pareto mixture via the Approximate Maximum Likelihood and the Cross-Entropy methods. See Bee, M. (2023) <doi:10.1016/j.csda.2023.107764>.

Maintained by Marco Bee. Last updated 4 months ago.

16.2 match 2.70 score 3 scripts

skyebend

networkDynamicData:Dynamic (Longitudinal) Network Datasets

A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.

Maintained by Skye Bender-deMoll. Last updated 9 years ago.

21.0 match 3 stars 2.07 score 39 scripts

statnet

tsna:Tools for Temporal Social Network Analysis

Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.

Maintained by Skye Bender-deMoll. Last updated 1 years ago.

5.6 match 7 stars 7.65 score 93 scripts 2 dependents

yihui

servr:A Simple HTTP Server to Serve Static Files or Dynamic Documents

Start an HTTP server in R to serve static files, or dynamic documents that can be converted to HTML files (e.g., R Markdown) under a given directory.

Maintained by Yihui Xie. Last updated 2 months ago.

http-server web-server websocket

3.3 match 283 stars 12.51 score 190 scripts 94 dependents

noctiluc3nt

meteoEVT:Computation and Visualization of Energetic and Vortical Atmospheric Quantities

Energy-Vorticity theory (EVT) is the fundamental theory to describe processes in the atmosphere by combining conserved quantities from hydrodynamics and thermodynamics. The package 'meteoEVT' provides functions to calculate many energetic and vortical quantities, like potential vorticity, Bernoulli function and dynamic state index (DSI) [e.g. Weber and Nevir, 2008, <doi:10.1111/j.1600-0870.2007.00272.x>], for given gridded data, like ERA5 reanalyses. These quantities can be studied directly or can be used for many applications in meteorology, e.g., the objective identification of atmospheric fronts. For this purpose, separate function are provided that allow the detection of fronts based on the thermic front parameter [Hewson, 1998, <doi:10.1017/S1350482798000553>], the F diagnostic [Parfitt et al., 2017, <doi:10.1002/2017GL073662>] and the DSI [Mack et al., 2022, <arXiv:2208.11438>].

Maintained by Laura Mack. Last updated 10 months ago.

atmospheric-dynamics dsi meteorology vorticity

9.9 match 4.18 score

globalecologylab

poems:Pattern-Oriented Ensemble Modeling System

A framework of interoperable R6 classes (Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,2005, <doi:10.1126/science.1116681>). The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, <doi:10.1080/03610928008827996>); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, <doi:10.1093/genetics/162.4.2025>), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes.

Maintained by July Pilowsky. Last updated 19 days ago.

biogeography population-model process-based

5.1 match 10 stars 8.05 score 59 scripts 2 dependents

radicalcommecol

cxr:A Toolbox for Modelling Species Coexistence in R

Recent developments in modern coexistence theory have advanced our understanding on how species are able to persist and co-occur with other species at varying abundances. However, applying this mathematical framework to empirical data is still challenging, precluding a larger adoption of the theoretical tools developed by empiricists. This package provides a complete toolbox for modelling interaction effects between species, and calculate fitness and niche differences. The functions are flexible, may accept covariates, and different fitting algorithms can be used. A full description of the underlying methods is available in García-Callejas, D., Godoy, O., and Bartomeus, I. (2020) <doi:10.1111/2041-210X.13443>. Furthermore, the package provides a series of functions to calculate dynamics for stage-structured populations across sites.

Maintained by David Garcia-Callejas. Last updated 1 months ago.

6.3 match 10 stars 6.51 score 27 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 21 days ago.

6.9 match 6 stars 5.95 score 96 scripts 10 dependents

jfrench

smerc:Statistical Methods for Regional Counts

Implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering. The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and Kulldorff (1997) <doi:10.1080/03610929708831995>.

Maintained by Joshua French. Last updated 5 months ago.

cpp

6.7 match 3 stars 6.11 score 45 scripts 3 dependents

cecileproust-lima

lcmm:Extended Mixed Models Using Latent Classes and Latent Processes

Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).

Maintained by Cecile Proust-Lima. Last updated 1 months ago.

fortran

3.6 match 62 stars 11.41 score 249 scripts 7 dependents

marce10

dynaSpec:Dynamic Spectrogram Visualizations

A set of tools to generate dynamic spectrogram visualizations in video format.

Maintained by Marcelo Araya-Salas. Last updated 15 days ago.

animal-sounds bioacoustics spectrogram

7.4 match 23 stars 5.50 score 34 scripts

gaborcsardi

prompt:Dynamic 'R' Prompt

Set the 'R' prompt dynamically, from a function. The package contains some examples to include various useful dynamic information in the prompt: the status of the last command (success or failure); the amount of memory allocated by the current 'R' process; the name of the R package(s) loaded by 'pkgload' and/or 'devtools'; various 'git' information: the name of the active branch, whether it is dirty, if it needs pushes pulls. You can also create your own prompt if you don't like the predefined examples.

Maintained by Gábor Csárdi. Last updated 2 years ago.

6.1 match 229 stars 6.59 score 57 scripts 1 dependents

ropensci

drake:A Pipeline Toolkit for Reproducible Computation at Scale

A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website <https://docs.ropensci.org/drake/> and the online manual <https://books.ropensci.org/drake/>.

Maintained by William Michael Landau. Last updated 3 months ago.

data-science drake high-performance-computing makefile peer-reviewed pipeline reproducibility reproducible-research ropensci workflow

3.5 match 1.3k stars 11.49 score 1.7k scripts 1 dependents

waternumbers

dynatopGIS:Algorithms for Helping Build Dynamic TOPMODEL Implementations from Spatial Data

A set of algorithms based on Quinn et al. (1991) <doi:10.1002/hyp.3360050106> for processing river network and digital elevation data to build implementations of Dynamic TOPMODEL, a semi-distributed hydrological model proposed in Beven and Freer (2001) <doi:10.1002/hyp.252>. The 'dynatop' package implements simulation code for Dynamic TOPMODEL based on the output of 'dynatopGIS'.

Maintained by Paul Smith. Last updated 10 months ago.

10.0 match 1 stars 4.00 score

rinterface

shinyMobile:Mobile Ready 'shiny' Apps with Standalone Capabilities

Develop outstanding 'shiny' apps for 'iOS' and 'Android' as well as beautiful 'shiny' gadgets. 'shinyMobile' is built on top of the latest 'Framework7' template <https://framework7.io>. Discover 14 new input widgets (sliders, vertical sliders, stepper, grouped action buttons, toggles, picker, smart select, ...), 2 themes (light and dark), 12 new widgets (expandable cards, badges, chips, timelines, gauges, progress bars, ...) combined with the power of server-side notifications such as alerts, modals, toasts, action sheets, sheets (and more) as well as 3 layouts (single, tabs and split).

Maintained by David Granjon. Last updated 2 months ago.

android hacktoberfest2022 pwa shiny shinyapps template

3.3 match 409 stars 11.91 score 1.1k scripts 2 dependents

cran

Spillover:Spillover/Connectedness Index Based on VAR Modelling

A user-friendly tool for estimating both total and directional connectedness spillovers based on Diebold and Yilmaz (2009, 2012). It also provides the user with rolling estimation for total and net indices. User can find both orthogonalized and generalized versions for each kind of measures. See Diebold and Yilmaz (2009, 2012) find them at <doi:10.1111/j.1468-0297.2008.02208.x> and <doi:10.1016/j.ijforecast.2011.02.006>.

Maintained by Jilber Urbina. Last updated 1 years ago.

10.3 match 8 stars 3.86 score

steve-the-bayesian

bsts:Bayesian Structural Time Series

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

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

cpp

6.0 match 33 stars 6.54 score 338 scripts 3 dependents

cran

ConnectednessApproach:Connectedness Approach

The estimation of static and dynamic connectedness measures is created in a modular and user-friendly way. Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers.

Maintained by David Gabauer. Last updated 14 days ago.

15.1 match 4 stars 2.60 score

conig

revise:Dynamic Revision Letters for 'Rmarkdown' Manuscripts

Extracts tagged text from markdown manuscripts for inclusion in dynamically generated revision letters. Provides an R markdown template based on papaja::revision_letter_pdf() with comment cross-referencing, a system for managing multiple sections of extracted text, and a way to automatically determine the page number of quoted sections from PDF manuscripts.

Maintained by James Conigrave. Last updated 11 days ago.

9.1 match 3 stars 4.26 score

ropensci

assertr:Assertive Programming for R Analysis Pipelines

Provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly. Similar to 'stopifnot()' but more powerful, friendly, and easier for use in pipelines.

Maintained by Tony Fischetti. Last updated 11 months ago.

analysis-pipeline assertion-library assertion-methods assertions peer-reviewed predicate-functions

3.3 match 478 stars 11.39 score 452 scripts 12 dependents

hanase

dma:Dynamic Model Averaging

Dynamic model averaging for binary and continuous outcomes.

Maintained by Hana Sevcikova. Last updated 6 years ago.

9.5 match 6 stars 3.95 score 8 scripts

ncss-tech

soilDB:Soil Database Interface

A collection of functions for reading soil data from U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and National Cooperative Soil Survey (NCSS) databases.

Maintained by Andrew Brown. Last updated 5 days ago.

kssl nasis nrcs soil soil-data-access soil-survey soilweb sql usda

3.3 match 87 stars 11.34 score 1.0k scripts 1 dependents

bioc

iSEEu:iSEE Universe

iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

immunooncology visualization gui dimensionreduction featureextraction clustering transcription geneexpression transcriptomics singlecell cellbasedassays hacktoberfest

5.2 match 9 stars 7.15 score 35 scripts 1 dependents

fchamroukhi

flamingos:Functional Latent Data Models for Clustering Heterogeneous Curves ('FLaMingos')

Provides a variety of original and flexible user-friendly statistical latent variable models for the simultaneous clustering and segmentation of heterogeneous functional data (i.e time series, or more generally longitudinal data, fitted by unsupervised algorithms, including EM algorithms. Functional Latent Data Models for Clustering heterogeneous curves ('FLaMingos') are originally introduced and written in 'Matlab' by Faicel Chamroukhi <https://github.com/fchamroukhi?utf8=?&tab=repositories&q=mix&type=public&language=matlab>. The references are mainly the following ones. Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) <doi:10.1016/j.neucom.2009.12.023>. Chamroukhi F., Same A., Aknin P. and Govaert G. (2011) <doi:10.1109/IJCNN.2011.6033590>. Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) <doi:10.1007/s11634-011-0096-5>. Chamroukhi F., and Glotin H. (2012) <doi:10.1109/IJCNN.2012.6252818>. Chamroukhi F., Glotin H. and Same A. (2013) <doi:10.1016/j.neucom.2012.10.030>. Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>. Chamroukhi F. and Nguyen H-D. (2019) <doi:10.1002/widm.1298>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence baum-welch-algorithm curve-clustering data-science dynamic-programming em-algorithm functional-data-analysis functional-data-clustering hidden-markov-models hidden-process-regression mixture-models piecewise-regression statistical-analysis statistical-inference statistical-learning time-series-analysis unsupervised-learning openblas cpp

7.5 match 6 stars 4.95 score 9 scripts

mingstat

ZIM:Zero-Inflated Models (ZIM) for Count Time Series with Excess Zeros

Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression by Yang et al. (2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al. (2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and parameter-driven models respectively in the time series literature. The functions used for Markov regression or observation-driven models can also be used to fit ordinary regression models with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, quantile, and generate random numbers from ZIP and ZINB distributions.

Maintained by Ming Yang. Last updated 1 years ago.

6.2 match 8 stars 5.95 score 32 scripts

sachaepskamp

psychonetrics:Structural Equation Modeling and Confirmatory Network Analysis

Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.

Maintained by Sacha Epskamp. Last updated 11 days ago.

openblas cpp

5.4 match 51 stars 6.82 score 41 scripts 1 dependents

kgoldfeld

simstudy:Simulation of Study Data

Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).

Maintained by Keith Goldfeld. Last updated 8 months ago.

data-generation data-simulation simulation statistical-models cpp

3.3 match 82 stars 11.00 score 972 scripts 1 dependents

maxar

IncDTW:Incremental Calculation of Dynamic Time Warping

The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>. For details about this package, Dynamic Time Warping and Incremental Dynamic Time Warping please see "IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping" by Leodolter et al. (2021) <doi:10.18637/jss.v099.i09>.

Maintained by Maximilian Leodolter. Last updated 3 years ago.

cpp

16.8 match 2.18 score 15 scripts

andreamrau

ebdbNet:Empirical Bayes Estimation of Dynamic Bayesian Networks

Infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.

Maintained by Andrea Rau. Last updated 2 years ago.

openblas

8.5 match 4 stars 4.28 score 19 scripts

rstudio

sortable:Drag-and-Drop in 'shiny' Apps with 'SortableJS'

Enables drag-and-drop behaviour in Shiny apps, by exposing the functionality of the 'SortableJS' <https://sortablejs.github.io/Sortable/> JavaScript library as an 'htmlwidget'. You can use this in Shiny apps and widgets, 'learnr' tutorials as well as R Markdown. In addition, provides a custom 'learnr' question type - 'question_rank()' - that allows ranking questions with drag-and-drop.

Maintained by Andrie de Vries. Last updated 6 months ago.

htmlwidget

3.1 match 135 stars 11.62 score 368 scripts 13 dependents

appsilon

shiny.semantic:Semantic UI Support for Shiny

Creating a great user interface for your Shiny apps can be a hassle, especially if you want to work purely in R and don't want to use, for instance HTML templates. This package adds support for a powerful UI library Fomantic UI - <https://fomantic-ui.com/> (before Semantic). It also supports universal UI input binding that works with various DOM elements.

Maintained by Jakub Nowicki. Last updated 11 months ago.

appsilon fomantic-ui rhinoverse semantic semantic-components semantic-ui shiny

2.8 match 506 stars 13.00 score 586 scripts 3 dependents

polinasuter

BiDAG:Bayesian Inference for Directed Acyclic Graphs

Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>, N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>, D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.

Maintained by Polina Suter. Last updated 2 years ago.

cpp

11.0 match 4 stars 3.29 score 81 scripts 2 dependents

jeremyroos

gmgm:Gaussian Mixture Graphical Model Learning and Inference

Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.

Maintained by Jérémy Roos. Last updated 3 years ago.

bayesian-networks gaussian-mixture-models inference machine-learning probabilistic-graphical-models

10.6 match 5 stars 3.40 score 7 scripts

cran

GE:General Equilibrium Modeling

Some tools for developing general equilibrium models and some general equilibrium models. These models can be used for teaching economic theory and are built by the methods of new structural economics (see LI Wu, 2019, ISBN: 9787521804225, General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press). The model form and mathematical methods can be traced back to J. von Neumann (1945, A Model of General Economic Equilibrium. The Review of Economic Studies, 13. pp. 1-9), J. G. Kemeny, O. Morgenstern and G. L. Thompson (1956, A Generalization of the von Neumann Model of an Expanding Economy, Econometrica, 24, pp. 115-135) et al. By the way, J. G. Kemeny is a co-inventor of the computer language BASIC.

Maintained by Wu LI. Last updated 2 months ago.

13.6 match 1 stars 2.64 score

usepa

httk:High-Throughput Toxicokinetics

Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>) and propagating parameter uncertainty (Wambaugh et al., 2019 <doi:10.1093/toxsci/kfz205>). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

Maintained by John Wambaugh. Last updated 1 months ago.

comptox ord

3.5 match 27 stars 10.22 score 307 scripts 1 dependents

andyphilips

dynamac:Dynamic Simulation and Testing for Single-Equation ARDL Models

While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

Maintained by Soren Jordan. Last updated 4 years ago.

ardl stata time-series time-series-analysis

6.4 match 7 stars 5.59 score 37 scripts 1 dependents

mpierrejean

jointseg:Joint Segmentation of Multivariate (Copy Number) Signals

Methods for fast segmentation of multivariate signals into piecewise constant profiles and for generating realistic copy-number profiles. A typical application is the joint segmentation of total DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.

Maintained by Morgane Pierre-Jean. Last updated 6 years ago.

cpp

5.5 match 6 stars 6.50 score 44 scripts 2 dependents

projectmosaic

mosaicCalc:R-Language Based Calculus Operations for Teaching

Software to support the introductory *MOSAIC Calculus* textbook <https://www.mosaic-web.org/MOSAIC-Calculus/>), one of many data- and modeling-oriented educational resources developed by Project MOSAIC (<https://www.mosaic-web.org/>). Provides symbolic and numerical differentiation and integration, as well as support for applied linear algebra (for data science), and differential equations/dynamics. Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc. The software is suitable for general use, but intended mainly for teaching calculus.

Maintained by Daniel Kaplan. Last updated 19 days ago.

4.1 match 13 stars 8.68 score 546 scripts

vast-lib

tinyVAST:Multivariate Spatio-Temporal Models using Structural Equations

Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the 'mgcv' package to specify splines via the formula interface. See Thorson et al. (2024) <doi:10.48550/arXiv.2401.10193> for more details.

Maintained by James T. Thorson. Last updated 6 hours ago.

vector-autoregressive-spatio-temporal-model cpp

5.2 match 13 stars 6.80 score

cran

CSTools:Assessing Skill of Climate Forecasts on Seasonal-to-Decadal Timescales

Exploits dynamical seasonal forecasts in order to provide information relevant to stakeholders at the seasonal timescale. The package contains process-based methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. This package was developed in the context of the 'ERA4CS' project 'MEDSCOPE' and the 'H2020 S2S4E' project and includes contributions from 'ArticXchange' project founded by 'EU-PolarNet 2'. 'Pérez-Zanón et al. (2022) <doi:10.5194/gmd-15-6115-2022>'. 'Doblas-Reyes et al. (2005) <doi:10.1111/j.1600-0870.2005.00104.x>'. 'Mishra et al. (2018) <doi:10.1007/s00382-018-4404-z>'. 'Sanchez-Garcia et al. (2019) <doi:10.5194/asr-16-165-2019>'. 'Straus et al. (2007) <doi:10.1175/JCLI4070.1>'. 'Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>'. 'Torralba et al. (2017) <doi:10.1175/JAMC-D-16-0204.1>'. 'D'Onofrio et al. (2014) <doi:10.1175/JHM-D-13-096.1>'. 'Verfaillie et al. (2017) <doi:10.5194/gmd-10-4257-2017>'. 'Van Schaeybroeck et al. (2019) <doi:10.1016/B978-0-12-812372-0.00010-8>'. 'Yiou et al. (2013) <doi:10.1007/s00382-012-1626-3>'.

Maintained by Victoria Agudetse. Last updated 1 years ago.

fortran

6.6 match 2 stars 5.32 score 62 scripts 1 dependents

smartinsightsfromdata

rpivotTable:Build Powerful Pivot Tables and Dynamically Slice & Dice your Data

Build powerful pivot tables (aka Pivot Grid, Pivot Chart, Cross-Tab) and dynamically slice & dice / drag 'n' drop your data. 'rpivotTable' is a wrapper of 'pivottable', a powerful open-source Pivot Table library implemented in 'JavaScript' by Nicolas Kruchten. Aligned to 'pivottable' v2.19.0.

Maintained by Enzo Martoglio. Last updated 7 years ago.

3.3 match 288 stars 10.51 score 337 scripts 6 dependents

karlines

diagram:Functions for Visualising Simple Graphs (Networks), Plotting Flow Diagrams

Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, visualising webs, electrical networks, etc. Support for the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter M.J. Herman (2009), Springer. and the book "Solving Differential Equations in R" by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer. Includes demo(flowchart), demo(plotmat), demo(plotweb).

Maintained by Karline Soetaert. Last updated 4 years ago.

3.5 match 9.96 score 598 scripts 483 dependents

jenniniku

gllvm:Generalized Linear Latent Variable Models

Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).

Maintained by Jenni Niku. Last updated 17 hours ago.

cpp openmp

3.3 match 51 stars 10.52 score 176 scripts 1 dependents

stan-dev

rstanarm:Bayesian Applied Regression Modeling via Stan

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

Maintained by Ben Goodrich. Last updated 9 months ago.

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

2.2 match 393 stars 15.65 score 5.0k scripts 12 dependents

julianfaraway

faraway:Datasets and Functions for Books by Julian Faraway

Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).

Maintained by Julian Faraway. Last updated 1 months ago.

data

3.6 match 29 stars 9.43 score 1.7k scripts 1 dependents

au-bce-ee

ALFAM2:Dynamic Model of Ammonia Emission from Field-Applied Manure

An implementation of the ALFAM2 dynamic emission model for ammonia volatilization from field-applied animal slurry (manure with dry matter below about 15%). The model can be used to predict cumulative emission and emission rate of ammonia following field application of slurry. Predictions may be useful for emission inventory calculations, fertilizer management, assessment of mitigation strategies, or research aimed at understanding ammonia emission. Default parameter sets include effects of application method, slurry composition, and weather. The model structure is based on a simplified representation of the physical-chemical slurry-soil-atmosphere system. See Hafner et al. (2018) <doi:10.1016/j.atmosenv.2018.11.034> for information on the model and Hafner et al. (2019) <doi:10.1016/j.agrformet.2017.11.027> for more on the measurement data used for parameter development.

Maintained by Sasha D. Hafner. Last updated 4 months ago.

cpp

4.9 match 8 stars 6.96 score 40 scripts

hputter

dynpred:Companion Package to "Dynamic Prediction in Clinical Survival Analysis"

The dynpred package contains functions for dynamic prediction in survival analysis.

Maintained by Hein Putter. Last updated 10 years ago.

10.5 match 2 stars 3.23 score 40 scripts 1 dependents

shipei-zeng

dfvad:Diewert and Fox's Method of Value Added Growth Decomposition

Decomposing value added growth into explanatory factors. A cost constrained value added function is defined to specify the production frontier. Industry estimates can also be aggregated using a weighted average approach. Details about the methodology and data can be found in Diewert and Fox (2018) <doi:10.1093/oxfordhb/9780190226718.013.19> and Zeng, Parsons, Diewert and Fox (2018) <https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/Documents/emg2018-6_SZeng_EMG-Slides.pdf>.

Maintained by Shipei Zeng. Last updated 3 years ago.

9.0 match 3.70 score 5 scripts

hongyuanjia

rdyncall:Improved Foreign Function Interface and Dynamic Bindings to C Libraries

Provides a cross-platform framework for dynamic binding of C libraries using a flexible Foreign Function Interface ('FFI'). The FFI supports almost all fundamental C types, multiple calling conventions, symbolic access to foreign C 'struct'/'union' data types and wrapping of R functions as C callback function pointers. Dynamic bindings to shared C libraries are data-driven by cross-platform binding specification using a compact plain text format; an initial repository of bindings to a couple of common C libraries ('OpenGL', 'SDL2', 'Expat', 'glew', 'CUDA', 'OpenCL', 'ODE', 'R') comes with the package. The package includes a variety of technology demos and OS-specific notes for installation of shared libraries.

Maintained by Hongyuan Jia. Last updated 2 years ago.

8.7 match 6 stars 3.84 score 23 scripts

newmi1988

seeds:Estimate Hidden Inputs using the Dynamic Elastic Net

Algorithms to calculate the hidden inputs of systems of differential equations. These hidden inputs can be interpreted as a control that tries to minimize the discrepancies between a given model and taken measurements. The idea is also called the Dynamic Elastic Net, as proposed in the paper "Learning (from) the errors of a systems biology model" (Engelhardt, Froelich, Kschischo 2016) <doi:10.1038/srep20772>. To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: <https://bioconductor.org/packages/release/bioc/html/rsbml.html>.

Maintained by Tobias Newmiwaka. Last updated 4 years ago.

11.1 match 3.00 score 2 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.

6.3 match 4 stars 5.24 score 29 scripts

dallenmidd

IxPopDyMod:Framework for Tick Population and Infection Modeling

Code to specify, run, and then visualize and analyze the results of Ixodidae (hard-bodied ticks) population and infection dynamics models. Such models exist in the literature, but the source code to run them is not always available. 'IxPopDyMod' provides an easy way for these models to be written and shared.

Maintained by Myles Stokowski. Last updated 4 months ago.

11.1 match 2 stars 3.00 score 6 scripts

jacob-long

dpm:Dynamic Panel Models Fit with Maximum Likelihood

Implements the dynamic panel models described by Allison, Williams, and Moral-Benito (2017 <doi:10.1177/2378023117710578>) in R. This class of models uses structural equation modeling to specify dynamic (lagged dependent variable) models with fixed effects for panel data. Additionally, models may have predictors that are only weakly exogenous, i.e., are affected by prior values of the dependent variable. Options also allow for random effects, dropping the lagged dependent variable, and a number of other specification choices.

Maintained by Jacob A. Long. Last updated 1 years ago.

social-science statistics

7.2 match 16 stars 4.55 score 44 scripts

ropensci

geotargets:'Targets' Extensions for Geographic Spatial Formats

Provides extensions for various geographic spatial file formats, such as shape files and rasters. Currently provides support for the 'terra' geographic spatial formats. See the vignettes for worked examples, demonstrations, and explanations of how to use the various package extensions.

Maintained by Nicholas Tierney. Last updated 2 days ago.

geospatial pipeline r-targetopia raster reproducibility reproducible-research targets vector workflow

4.8 match 72 stars 6.78 score

yenyiho-lab

scDECO:Estimating Dynamic Correlation

Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) <doi:10.1111/biom.13701>, while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) <doi:10.1111/biom.13457>.

Maintained by Anderson Bussing. Last updated 9 months ago.

jags cpp

6.8 match 4.78 score

oobianom

shinyStorePlus:Secure in-Browser and Database Storage for 'shiny' Inputs, Outputs, Views and User Likes

Store persistent and synchronized data from 'shiny' inputs within the browser. Refresh 'shiny' applications and preserve user-inputs over multiple sessions. A database-like storage format is implemented using 'Dexie.js' <https://dexie.org>, a minimal wrapper for 'IndexedDB'. Transfer browser link parameters to 'shiny' input or output values. Store app visitor views, likes and followers.

Maintained by Obinna Obianom. Last updated 19 days ago.

data data-structures shiny

3.9 match 28 stars 8.29 score 93 scripts 1 dependents

hfgolino

EGAnet:Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

Implements the Exploratory Graph Analysis (EGA) framework for dimensionality and psychometric assessment. EGA estimates the number of dimensions in psychological data using network estimation methods and community detection algorithms. A bootstrap method is provided to assess the stability of dimensions and items. Fit is evaluated using the Entropy Fit family of indices. Unique Variable Analysis evaluates the extent to which items are locally dependent (or redundant). Network loadings provide similar information to factor loadings and can be used to compute network scores. A bootstrap and permutation approach are available to assess configural and metric invariance. Hierarchical structures can be detected using Hierarchical EGA. Time series and intensive longitudinal data can be analyzed using Dynamic EGA, supporting individual, group, and population level assessments.

Maintained by Hudson Golino. Last updated 8 days ago.

4.1 match 47 stars 7.80 score 61 scripts 1 dependents

cdriveraus

ctsem:Continuous Time Structural Equation Modelling

Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . Stan based functions are not available on 32 bit Windows systems at present. <https://cdriver.netlify.app/> contains some tutorial blog posts.

Maintained by Charles Driver. Last updated 10 days ago.

stochastic-differential-equations time-series cpp

3.4 match 42 stars 9.58 score 366 scripts 1 dependents

cubiczebra

TPMplt:Tool-Kit for Dynamic Materials Model and Thermal Processing Maps

Provides a simple approach for constructing dynamic materials modeling suggested by Prasad and Gegel (1984) <doi:10.1007/BF02664902>. It can easily generate various processing-maps based on this model as well. The calculation result in this package contains full materials constants, information about power dissipation efficiency factor, and rheological properties, can be exported completely also, through which further analysis and customized plots will be applicable as well.

Maintained by Chen Zhang. Last updated 6 months ago.

6.7 match 2 stars 4.76 score 29 scripts

fishr-core-team

RFishBC:Back-Calculation of Fish Length

Helps fisheries scientists collect measurements from calcified structures and back-calculate estimated lengths at previous ages using standard procedures and models. This is intended to replace much of the functionality provided by the now out-dated 'fishBC' software (<https://fisheries.org/bookstore/all-titles/software/70317/>).

Maintained by Derek H. Ogle. Last updated 1 years ago.

fish fisheries fisheries-management fisheries-stock-assessment population-dynamics stock-assessment

7.5 match 13 stars 4.26 score 28 scripts

kdrachal

fDMA:Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes

Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.

Maintained by Krzysztof Drachal. Last updated 2 months ago.

openblas cpp openmp

7.5 match 3 stars 4.26 score 60 scripts

mrc-ide

malariasimulation:An individual based model for malaria

Specifies the latest and greatest malaria model.

Maintained by Giovanni Charles. Last updated 26 days ago.

cpp

3.9 match 16 stars 8.17 score 146 scripts

marce10

warbleR:Streamline Bioacoustic Analysis

Functions aiming to facilitate the analysis of the structure of animal acoustic signals in 'R'. 'warbleR' makes use of the basic sound analysis tools from the packages 'tuneR' and 'seewave', and offers new tools for explore and quantify acoustic signal structure. The package allows to organize and manipulate multiple sound files, create spectrograms of complete recordings or individual signals in different formats, run several measures of acoustic structure, and characterize different structural levels in acoustic signals.

Maintained by Marcelo Araya-Salas. Last updated 2 months ago.

animal-acoustic-signals audio-processing bioacoustics spectrogram streamline-analysis cpp

2.9 match 54 stars 11.01 score 270 scripts 4 dependents

rstudio

sass:Syntactically Awesome Style Sheets ('Sass')

An 'SCSS' compiler, powered by the 'LibSass' library. With this, R developers can use variables, inheritance, and functions to generate dynamic style sheets. The package uses the 'Sass CSS' extension language, which is stable, powerful, and CSS compatible.

Maintained by Carson Sievert. Last updated 11 months ago.

cpp

2.0 match 101 stars 15.56 score 252 scripts 4.3k dependents

nau-ccl

SPARSEMODr:SPAtial Resolution-SEnsitive Models of Outbreak Dynamics

Implementation of spatially-explicit, stochastic disease models with customizable time windows that describe how parameter values fluctuate during outbreaks (e.g., in response to public health or conservation interventions).

Maintained by Joseph Mihaljevic. Last updated 3 years ago.

gsl cpp

6.5 match 4.78 score 8 scripts

modeloriented

survex:Explainable Machine Learning in Survival Analysis

Survival analysis models are commonly used in medicine and other areas. Many of them are too complex to be interpreted by human. Exploration and explanation is needed, but standard methods do not give a broad enough picture. 'survex' provides easy-to-apply methods for explaining survival models, both complex black-boxes and simpler statistical models. They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023) <doi:10.1016/j.knosys.2022.110234>, SurvLIME described in Kovalev et al., (2020) <doi:10.1016/j.knosys.2020.106164> as well as extensions of existing ones described in Biecek et al., (2021) <doi:10.1201/9780429027192>.

Maintained by Mikołaj Spytek. Last updated 9 months ago.

biostatistics brier-scores censored-data cox-model cox-regression explainable-ai explainable-machine-learning explainable-ml explanatory-model-analysis interpretable-machine-learning interpretable-ml machine-learning probabilistic-machine-learning shap survival-analysis time-to-event variable-importance xai

3.7 match 110 stars 8.40 score 114 scripts

haoranpopevo

ecode:Ordinary Differential Equation Systems in Ecology

A framework to simulate ecosystem dynamics through ordinary differential equations (ODEs). You create an ODE model, tells 'ecode' to explore its behaviour, and perform numerical simulations on the model. 'ecode' also allows you to fit model parameters by machine learning algorithms. Potential users include researchers who are interested in the dynamics of ecological community and biogeochemical cycles.

Maintained by Haoran Wu. Last updated 7 months ago.

8.0 match 7 stars 3.85 score 3 scripts

jacob-long

panelr:Regression Models and Utilities for Repeated Measures and Panel Data

Provides an object type and associated tools for storing and wrangling panel data. Implements several methods for creating regression models that take advantage of the unique aspects of panel data. Among other capabilities, automates the "within-between" (also known as "between-within" and "hybrid") panel regression specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them as multilevel models (Allison, 2009 <doi:10.4135/9781412993869.d33>; Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be estimated via generalized estimating equations (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian estimation is (optionally) supported via 'Stan'. Supports estimation of asymmetric effects models via first differences (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized linear model extension thereof using GEE.

Maintained by Jacob A. Long. Last updated 1 years ago.

social-science statistics

3.5 match 101 stars 8.76 score 181 scripts 1 dependents

bioc

GRENITS:Gene Regulatory Network Inference Using Time Series

The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model.

Maintained by Edward Morrissey. Last updated 5 months ago.

networkinference generegulation timecourse graphandnetwork geneexpression network bayesian openblas cpp

7.2 match 4.20 score 2 scripts

marshalllab

MGDrivE:Mosquito Gene Drive Explorer

Provides a model designed to be a reliable testbed where various gene drive interventions for mosquito-borne diseases control. It is being developed to accommodate the use of various mosquito-specific gene drive systems within a population dynamics framework that allows migration of individuals between patches in landscape. Previous work developing the population dynamics can be found in Deredec et al. (2001) <doi:10.1073/pnas.1110717108> and Hancock & Godfray (2007) <doi:10.1186/1475-2875-6-98>, and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) <doi:10.1038/s41598-017-02744-7>.

Maintained by Héctor Manuel Sánchez Castellanos. Last updated 4 years ago.

cpp

4.3 match 6 stars 7.06 score 61 scripts

levimcclenny

BoolFilter:Optimal Estimation of Partially Observed Boolean Dynamical Systems

Tools for optimal and approximate state estimation as well as network inference of Partially-Observed Boolean Dynamical Systems.

Maintained by Levi McClenny. Last updated 7 years ago.

8.1 match 3.70 score 10 scripts

solivella

NetMix:Dynamic Mixed-Membership Network Regression Model

Stochastic collapsed variational inference on mixed-membership stochastic blockmodel for networks, incorporating node-level predictors of mixed-membership vectors, as well as dyad-level predictors. For networks observed over time, the model defines a hidden Markov process that allows the effects of node-level predictors to evolve in discrete, historical periods. In addition, the package offers a variety of utilities for exploring results of estimation, including tools for conducting posterior predictive checks of goodness-of-fit and several plotting functions. The package implements methods described in Olivella, Pratt and Imai (2019) 'Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts', available at <https://www.santiagoolivella.info/pdfs/socnet.pdf>.

Maintained by Santiago Olivella. Last updated 1 years ago.

openblas cpp openmp

7.0 match 11 stars 4.30 score 36 scripts

rstudio

tfprobability:Interface to 'TensorFlow Probability'

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

Maintained by Tomasz Kalinowski. Last updated 3 years ago.

3.4 match 54 stars 8.63 score 221 scripts 3 dependents

bergant

rapiclient:Dynamic OpenAPI/Swagger Client

Access services specified in OpenAPI (formerly Swagger) format. It is not a code generator. Client is generated dynamically as a list of R functions.

Maintained by Marcel Ramos. Last updated 6 months ago.

api openapi swagger

3.7 match 68 stars 8.07 score 32 scripts 13 dependents

henrikbengtsson

R.rsp:Dynamic Generation of Scientific Reports

The RSP markup language makes any text-based document come alive. RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'. Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries. RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things. Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'. RSP is ideal for self-contained scientific reports and R package vignettes. It's easy to use - if you know how to write an R script, you'll be up and running within minutes.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

document markup report reproducibility science

3.6 match 31 stars 8.04 score 36 scripts 9 dependents

dachosen1

formulaic:Dynamic Generation and Quality Checks of Formula Objects

Many statistical models and analyses in R are implemented through formula objects. The formulaic package creates a unified approach for programmatically and dynamically generating formula objects. Users may specify the outcome and inputs of a model directly, search for variables to include based upon naming patterns, incorporate interactions, and identify variables to exclude. A wide range of quality checks are implemented to identify issues such as misspecified variables, duplication, a lack of contrast in the inputs, and a large number of levels in categorical data. Variables that do not meet these quality checks can be automatically excluded from the model. These issues are documented and reported in a manner that provides greater accountability and useful information to guide an investigation of the data.

Maintained by Anderson Nelson. Last updated 1 years ago.

formula-objects formula-parser machine-learning quality-check statistical-models

4.8 match 10 stars 6.10 score 42 scripts 1 dependents

dankelley

oce:Analysis of Oceanographic Data

Supports the analysis of Oceanographic data, including 'ADCP' measurements, measurements made with 'argo' floats, 'CTD' measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature. This package is discussed extensively by Kelley (2018) "Oceanographic Analysis with R" <doi:10.1007/978-1-4939-8844-0>.

Maintained by Dan Kelley. Last updated 8 days ago.

oceanography fortran cpp

1.9 match 146 stars 15.45 score 4.2k scripts 18 dependents

s1107967177

dynetNLAResistance:Resisting Neighbor Label Attack in a Dynamic Network

An anonymization algorithm to resist neighbor label attack in a dynamic network.

Maintained by Jiaqi Tang. Last updated 8 years ago.

10.7 match 2.70 score 4 scripts

atsa-es

MARSS:Multivariate Autoregressive State-Space Modeling

The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB' (using the 'marssTMB' companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at <https://atsa-es.github.io/>.

Maintained by Elizabeth Eli Holmes. Last updated 1 years ago.

multivariate-timeseries state-space-models statistics time-series

2.8 match 52 stars 10.34 score 596 scripts 3 dependents