Showing 78 of total 78 results (show query)
statnet
ergm:Fit, Simulate and Diagnose Exponential-Family Models for Networks
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) <doi:10.18637/jss.v024.i03> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.
Maintained by Pavel N. Krivitsky. Last updated 20 days ago.
100 stars 15.36 score 1.4k scripts 36 dependents
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-modelingepidemicsepidemiologyinfectious-diseasesnetwork-graphcpp
250 stars 11.43 score 315 scripts
statdivlab
corncob:Count Regression for Correlated Observations with the Beta-Binomial
Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.
Maintained by Amy D Willis. Last updated 11 days ago.
106 stars 9.82 score 248 scripts 1 dependentsstatnet
ergm.multi:Fit, Simulate and Diagnose Exponential-Family Models for Multiple or Multilayer Networks
A set of extensions for the 'ergm' package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. 'ergm.multi' is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Krivitsky, Coletti, and Hens (2023) <doi:10.1080/01621459.2023.2242627>.
Maintained by Pavel N. Krivitsky. Last updated 4 months ago.
14 stars 9.67 score 11 scripts 5 dependents
donaldrwilliams
BGGM:Bayesian Gaussian Graphical Models
Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.
Maintained by Philippe Rast. Last updated 3 months ago.
bayes-factorsbayesian-hypothesis-testinggaussian-graphical-modelsopenblascppopenmp
55 stars 9.61 score 102 scripts 1 dependentsstatnet
tergm:Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis. See Krivitsky and Handcock (2014) <doi:10.1111/rssb.12014> and Carnegie, Krivitsky, Hunter, and Goodreau (2015) <doi:10.1080/10618600.2014.903087>.
Maintained by Pavel N. Krivitsky. Last updated 5 months ago.
27 stars 9.29 score 78 scripts 3 dependentsstatnet
ergm.count:Fit, Simulate and Diagnose Exponential-Family Models for Networks with Count Edges
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts. See Krivitsky (2012) <doi:10.1214/12-EJS696> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.
Maintained by Pavel N. Krivitsky. Last updated 5 months ago.
10 stars 8.78 score 140 scripts 1 dependentsstatnet
statnet:Software Tools for the Statistical Analysis of Network Data
This package is designed to make it easy to install and load key packages from the 'statnet' suite in a single step. The `statnet` suite is a collection of packages for statistical network analysis that are designed to work together; they share common data representations, 'API' design and a uniform user interface. Together they provide an integrated set of tools for the exploration, visualization, statistical analysis, and simulation of many different forms of network data. Learn more about 'statnet' at <https://www.statnet.org>. Tutorials for many packages can be found at <https://github.com/statnet/Workshops/wiki>. For an introduction to functions in this package, type help(package='statnet').
Maintained by Martina Morris. Last updated 4 years ago.
41 stars 8.69 score 896 scriptsstatnet
latentnet:Latent Position and Cluster Models for Statistical Networks
Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <doi:10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <doi:10.1016/j.socnet.2009.04.001>.
Maintained by Pavel N. Krivitsky. Last updated 19 days ago.
19 stars 8.36 score 191 scripts 4 dependents
statnet
ergm.ego:Fit, Simulate and Diagnose Exponential-Family Random Graph Models to Egocentrically Sampled Network Data
Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data. See Krivitsky and Morris (2017) <doi:10.1214/16-AOAS1010>.
Maintained by Pavel N. Krivitsky. Last updated 2 months ago.
14 stars 7.70 score 67 scripts 1 dependents
asheshrambachan
HonestDiD:Robust Inference in Difference-in-Differences and Event Study Designs
Provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2023) <doi:10.1093/restud/rdad018>, "A More Credible Approach to Parallel Trends" [Previously titled "An Honest Approach..."]. Inference is conducted under a weaker version of the parallel trends assumption. Uniformly valid confidence sets are constructed based upon conditional confidence sets, fixed-length confidence sets and hybridized confidence sets.
Maintained by Ashesh Rambachan. Last updated 1 months ago.
difference-in-differencesevent-studiesrobust-inference
197 stars 7.05 score 63 scripts
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 11 days ago.
complex-networksdynamic-analysisergmestimationgoodness-of-fitinferencelongitudinal-datanetwork-analysispredictiontergm
18 stars 7.03 score 83 scripts 2 dependentsstatnet
ergm.rank:Fit, Simulate and Diagnose Exponential-Family Models for Rank-Order Relational Data
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are ranks. See Krivitsky and Butts (2017) <doi:10.1177/0081175017692623> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.
Maintained by Pavel N. Krivitsky. Last updated 5 months ago.
4 stars 6.98 score 9 scripts 1 dependents
data-cleaning
editrules:Parsing, Applying, and Manipulating Data Cleaning Rules
Please note: active development has moved to packages 'validate' and 'errorlocate'. Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the 'igraph' package.
Maintained by Edwin de Jonge. Last updated 10 months ago.
22 stars 6.97 score 140 scripts 1 dependents
acaimo
Bergm:Bayesian Exponential Random Graph Models
Bayesian analysis for exponential random graph models using advanced computational algorithms. More information can be found at: <https://acaimo.github.io/Bergm/>.
Maintained by Alberto Caimo. Last updated 2 months ago.
16 stars 6.37 score 31 scripts 4 dependentsdata-cleaning
errorlocate:Locate Errors with Validation Rules
Errors in data can be located and removed using validation rules from package 'validate'. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, chapter 7.
Maintained by Edwin de Jonge. Last updated 10 months ago.
data-cleaningerrorsinvalidation
22 stars 6.11 score 59 scripts
tillbirkner
metadeconfoundR:Covariate-Sensitive Analysis of Cross-Sectional High-Dimensional Data
Using non-parametric tests, naive associations between omics features and metadata in cross-sectional data-sets are detected. In a second step, confounding effects between metadata associated to the same omics feature are detected and labeled using nested post-hoc model comparison tests, as first described in Forslund, Chakaroun, Zimmermann-Kogadeeva, et al. (2021) <doi:10.1038/s41586-021-04177-9>. The generated output can be graphically summarized using the built-in plotting function.
Maintained by Till Birkner. Last updated 5 months ago.
18 stars 5.91 score 15 scripts
dppalomar
highOrderPortfolios:Design of High-Order Portfolios Including Skewness and Kurtosis
The classical Markowitz's mean-variance portfolio formulation ignores heavy tails and skewness. High-order portfolios use higher order moments to better characterize the return distribution. Different formulations and fast algorithms are proposed for high-order portfolios based on the mean, variance, skewness, and kurtosis. The package is based on the papers: R. Zhou and D. P. Palomar (2021). "Solving High-Order Portfolios via Successive Convex Approximation Algorithms." <arXiv:2008.00863>. X. Wang, R. Zhou, J. Ying, and D. P. Palomar (2022). "Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution." <arXiv:2206.02412>.
Maintained by Daniel P. Palomar. Last updated 2 years ago.
24 stars 5.90 score 22 scripts
bioc
benchdamic:Benchmark of differential abundance methods on microbiome data
Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.
Maintained by Matteo Calgaro. Last updated 4 months ago.
metagenomicsmicrobiomedifferentialexpressionmultiplecomparisonnormalizationpreprocessingsoftwarebenchmarkdifferential-abundance-methods
8 stars 5.78 score 8 scriptsstatnet
statnetWeb:A Shiny App for Network Modeling with 'statnet'
A graphical user interface for cross-sectional network modeling with the 'statnet' software suite <https://github.com/statnet>.
Maintained by Martina Morris. Last updated 9 months ago.
29 stars 5.59 score 19 scripts
muriteams
ergmito:Exponential Random Graph Models for Small Networks
Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (2020) <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the 'ergm' package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms.
Maintained by George Vega Yon. Last updated 2 years ago.
ergmexponential-random-graph-modelsstatisticsopenblascppopenmp
9 stars 5.49 score 34 scripts
riksrevisjonen
pioneeR:Productivity and Efficiency Analysis using DEA
Measure productivity and efficiency using Data Envelopment Analysis (DEA). Available methods include DEA under different technology assumptions, bootstrapping of efficiency scores and calculation of the Malmquist productivity index. Analyses can be performed either in the console or with the provided 'shiny' app. See Banker, R.; Charnes, A.; Cooper, W.W. (1984) <doi:10.1287/mnsc.30.9.1078>, Färe, R.; Grosskopf, S. (1996) <doi:10.1007/978-94-009-1816-0>.
Maintained by Ove Haugland Jakobsen. Last updated 5 months ago.
7 stars 5.09 score 5 scripts
craig-parylo
plotor:Produces an Odds Ratio Plot from a Logistic Regression Model
Produces an Odds Ratio (OR) Plot to visualise the result of a logistic regression analysis. Provide it with a binomial regression model produced by 'glm()' and it will convert the estimates to odds ratios with a 95% confidence interval and plot the results using 'ggplot2'.
Maintained by Craig Parylo. Last updated 13 days ago.
ggplot2glmlogistic-regressionodds-ratio
2 stars 5.04 score 7 scriptsstatnet
ergm.userterms:User-specified Terms for the statnet Suite of Packages
A non-CRAN template package to demonstrate the use of user-specified statistics for use in 'ergm' models as part of the Statnet suite of packages.
Maintained by Pavel N. Krivitsky. Last updated 2 months ago.
4 stars 5.04 score 91 scripts
benjamin-w-campbell
fergm:Estimation and Fit Assessment of Frailty Exponential Random Graph Models
Frailty Exponential Random Graph Models estimated through pseudo likelihood with frailty terms estimated using 'Stan' as per Box-Steffensmeier et. al (2017) <doi:10.7910/DVN/K3D1M2>. Goodness of fit for Frailty Exponential Random Graph Models is also available, with easy visualizations for comparison to fit Exponential Random Graph Models.
Maintained by Benjamin W. Campbell. Last updated 3 years ago.
4 stars 4.86 score 18 scripts
miriamesteve
eat:Efficiency Analysis Trees
Functions are provided to determine production frontiers and technical efficiency measures through non-parametric techniques based upon regression trees. The package includes code for estimating radial input, output, directional and additive measures, plotting graphical representations of the scores and the production frontiers by means of trees, and determining rankings of importance of input variables in the analysis. Additionally, an adaptation of Random Forest by a set of individual Efficiency Analysis Trees for estimating technical efficiency is also included. More details in: <doi:10.1016/j.eswa.2020.113783>.
Maintained by Miriam Esteve. Last updated 3 years ago.
5 stars 4.68 score 19 scriptsbioc
openPrimeR:Multiplex PCR Primer Design and Analysis
An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package.
Maintained by Matthias Döring. Last updated 9 days ago.
softwaretechnologycoveragemultiplecomparison
4.64 score 22 scripts
feddelegrand7
farrell:Interactive Interface to Data Envelopment Analysis Modeling
Allows the user to execute interactively radial data envelopment analysis models. The user has the ability to upload a data frame, select the input/output variables, choose the technology assumption to adopt and decide whether to run an input or an output oriented model. When the model is executed a set of results are displayed which include efficiency scores, peers' determination, scale efficiencies' evaluation and slacks' calculation. Fore more information about the theoretical background of the package, please refer to Bogetoft & Otto (2011) <doi:10.1007/978-1-4419-7961-2>.
Maintained by Mohamed El Fodil Ihaddaden. Last updated 4 years ago.
data-envelopment-analysisdeashiny
8 stars 4.60 score 6 scripts
karolinehuth
easybgm:Extracting and Visualizing Bayesian Graphical Models
Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.
Maintained by Karoline Huth. Last updated 5 months ago.
4.51 score 27 scriptsdata-cleaning
validatetools:Checking and Simplifying Validation Rule Sets
Rule sets with validation rules may contain redundancies or contradictions. Functions for finding redundancies and problematic rules are provided, given a set a rules formulated with 'validate'.
Maintained by Edwin de Jonge. Last updated 10 months ago.
15 stars 4.47 score 39 scripts
roigrp
ROI.plugin.lpsolve:'lp_solve' Plugin for the 'R' Optimization Infrastructure
Enhances the 'R' Optimization Infrastructure ('ROI') package with the 'lp_solve' solver.
Maintained by Florian Schwendinger. Last updated 4 years ago.
1 stars 4.32 score 14 scripts 7 dependentsdata-cleaning
deducorrect:Deductive Correction, Deductive Imputation, and Deterministic Correction
A collection of methods for automated data cleaning where all actions are logged. NOTE: active development has moved to the 'deductive' package.
Maintained by Mark van der Loo. Last updated 10 months ago.
9 stars 4.18 score 34 scripts
xluo11
xxIRT:Item Response Theory and Computer-Based Testing
A suite of psychometric analysis tools for research and operation, including: (1) computation of probability, information, and likelihood for the 3PL, GPCM, and GRM; (2) parameter estimation using joint or marginal likelihood estimation method; (3) simulation of computerized adaptive testing using built-in or customized algorithms; (4) assembly and simulation of multistage testing. The full documentation and tutorials are at <https://github.com/xluo11/xxIRT>.
Maintained by Xiao Luo. Last updated 6 years ago.
25 stars 4.10 score 10 scripts
houjiewang
Rtropical:Data Analysis Tools over Space of Phylogenetic Trees Using Tropical Geometry
Process phylogenetic trees with tropical support vector machine and principal component analysis defined with tropical geometry. Details about tropical support vector machine are available in : Tang, X., Wang, H. & Yoshida, R. (2020) <arXiv:2003.00677>. Details about tropical principle component analysis are available in : Page, R., Yoshida, R. & Zhang L. (2020) <doi:10.1093/bioinformatics/btaa564> and Yoshida, R., Zhang, L. & Zhang, X. (2019) <doi:10.1007/s11538-018-0493-4>.
Maintained by Houjie Wang. Last updated 3 years ago.
4.00 score 5 scripts
mariaguilleng
boostingDEA:A Boosting Approach to Data Envelopment Analysis
Includes functions to estimate production frontiers and make ideal output predictions in the Data Envelopment Analysis (DEA) context using both standard models from DEA and Free Disposal Hull (FDH) and boosting techniques. In particular, EATBoosting (Guillen et al., 2023 <doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package includes code for estimating several technical efficiency measures using different models such as the input and output-oriented radial measures, the input and output-oriented Russell measures, the Directional Distance Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based Measure (SBM).
Maintained by Maria D. Guillen. Last updated 2 years ago.
2 stars 4.00 score 3 scripts
handcock
RDS:Respondent-Driven Sampling
Provides functionality for carrying out estimation with data collected using Respondent-Driven Sampling. This includes Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential Sampling estimator. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data. See Gile and Handcock (2010) <doi:10.1111/j.1467-9531.2010.01223.x>, Gile and Handcock (2015) <doi:10.1111/rssa.12091> and Gile, Beaudry, Handcock and Ott (2018) <doi:10.1146/annurev-statistics-031017-100704>.
Maintained by Mark S. Handcock. Last updated 7 months ago.
1 stars 3.87 score 82 scripts 3 dependents
klebermsousa
jackstrap:Correcting Nonparametric Frontier Measurements for Outliers
Provides method used to check whether data have outlier in efficiency measurement of big samples with data envelopment analysis (DEA). In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.
Maintained by Kleber Morais de Sousa. Last updated 5 years ago.
deajackstrapnonparametricoutlier-detection
1 stars 3.85 score 14 scriptsxluo11
Rata:Automated Test Assembly
Automated test assembly of linear and adaptive tests using the mixed-integer programming. The full documentation and tutorials are at <https://github.com/xluo11/Rata>.
Maintained by Xiao Luo. Last updated 5 years ago.
3 stars 3.65 score 1 dependents
forest-economics-goettingen
optimLanduse:Robust Land-Use Optimization
Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) <doi:10.1038/ncomms11877> and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) <doi:10.1016/j.jenvman.2020.110248>.
Maintained by Kai Husmann. Last updated 1 years ago.
2 stars 3.60 score 2 scriptsstatnet
ergmgp:Tools for Modeling ERGM Generating Processes
Provides tools for simulating draws from continuous time processes with well-defined exponential family random graph (ERGM) equilibria, i.e. ERGM generating processes (EGPs). A number of EGPs are supported, including the families identified in Butts (2023) <doi:10.1080/0022250X.2023.2180001>, as are functions for hazard calculation and timing calibration.
Maintained by Carter T. Butts. Last updated 6 months ago.
8 stars 3.60 score 1 scripts
alexanderoe
hyperbolicDEA:Hyperbolic DEA Estimation
Implements Data Envelopment Analysis (DEA) with a hyperbolic orientation using a non-linear programming solver. It enables flexible estimations with weight restrictions, non-discretionary variables, and a generalized distance function. Additionally, it allows for the calculation of slacks and super-efficiency scores. The methods are detailed in Öttl et al. (2023), <doi:10.1016/j.dajour.2023.100343>. Furthermore, the package provides a non-linear profitability estimation built upon the DEA framework.
Maintained by Alexander Öttl. Last updated 4 months ago.
1 stars 3.54 score 2 scripts
barnhilldave
TML:Tropical Geometry Tools for Machine Learning
Suite of tropical geometric tools for use in machine learning applications. These methods may be summarized in the following references: Yoshida, et al. (2022) <arxiv:2209.15045>, Barnhill et al. (2023) <arxiv:2303.02539>, Barnhill and Yoshida (2023) <doi:10.3390/math11153433>, Aliatimis et al. (2023) <arXiv:2306.08796>, Yoshida et al. (2022) <arXiv:2206.04206>, and Yoshida et al. (2019) <doi:10.1007/s11538-018-0493-4>.
Maintained by David Barnhill. Last updated 8 months ago.
3 stars 3.48 score 1 scripts
ericdunipace
WpProj:Linear p-Wasserstein Projections
Performs Wasserstein projections from the predictive distributions of any model into the space of predictive distributions of linear models. We utilize L1 penalties to also reduce the complexity of the model space. This package employs the methods as described in Dunipace, Eric and Lorenzo Trippa (2020) <doi:10.48550/arXiv.2012.09999>.
Maintained by Eric Dunipace. Last updated 2 months ago.
3.48 score
mabe0033
MSCMT:Multivariate Synthetic Control Method Using Time Series
Three generalizations of the synthetic control method (which has already an implementation in package 'Synth') are implemented: first, 'MSCMT' allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klößner (2018) <doi:10.1016/j.ecosta.2017.08.002>.
Maintained by Martin Becker. Last updated 1 years ago.
2 stars 3.32 score 35 scriptsxluo11
Rmst:Computerized Adaptive Multistage Testing
Assemble the panels of computerized adaptive multistage testing by the bottom-up and the top-down approach, and simulate the administration of the assembled panels. The full documentation and tutorials are at <https://github.com/xluo11/Rmst>. Reference: Luo and Kim (2018) <doi:10.1111/jedm.12174>.
Maintained by Xiao Luo. Last updated 5 years ago.
4 stars 3.30 score 3 scripts
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.
2.95 score 18 scripts
fvidoli
Compind:Composite Indicators Functions
A collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide, 2008, 'Vidoli' and 'Fusco' and 'Mazziotta' <doi:10.1007/s11205-014-0710-y>, 'Mazziotta' and 'Pareto' (2016) <doi:10.1007/s11205-015-0998-2>, 'Van Puyenbroeck and 'Rogge' <doi:10.1016/j.ejor.2016.07.038> and other authors.
Maintained by Francesco Vidoli. Last updated 3 months ago.
1 stars 2.90 score 40 scripts
corneliusfritz
bigergm:Fit, Simulate, and Diagnose Hierarchical Exponential-Family Models for Big Networks
A toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm' implements the estimation for large networks efficiently building on the 'lighthergm' and 'hergm' packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit.
Maintained by Cornelius Fritz. Last updated 1 months ago.
2.60 score 4 scriptsleifeld
xergm.common:Common Infrastructure for Extensions of Exponential Random Graph Models
Datasets and definitions of generic functions used in dependencies of the 'xergm' package.
Maintained by Philip Leifeld. Last updated 5 years ago.
2.01 score 34 scripts 1 dependents
mmm-uca
adea:Alternate DEA Package
The meaning of adea is "alternate DEA". This package is devoted to provide the alternative method of DEA described in the paper entitled "Stepwise Selection of Variables in DEA Using Contribution Load", by F. Fernandez-Palacin, M. A. Lopez-Sanchez and M. Munoz-Marquez. Pesquisa Operacional 38 (1), pg. 1-24, 2018. <doi:10.1590/0101-7438.2018.038.01.0031>. A full functional on-line and interactive version is available at <https://knuth.uca.es/shiny/DEA/>.
Maintained by Manuel Munoz-Marquez. Last updated 5 months ago.
2.00 score 1 scripts
cran
npbr:Nonparametric Boundary Regression
A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods. Its use is illustrated via a number of empirical applications and simulated examples.
Maintained by Thibault Laurent. Last updated 2 years ago.
1 stars 2.00 score
ayeshaperera
SimRDS:Simulation of Respondent Driven Samples
Simulate populations with desired properties and extract respondent driven samples. To better understand the usage of the package and the algorithm used, please refer to Perera, A., and Ramanayake, A. (2019) <https://www.aimr.tirdiconference.com/assets/images/portfolio/Conference-Proceeding-AIMR-19.pdf>.
Maintained by Ayesha Perera. Last updated 5 months ago.
2.00 score
wyseja
collpcm:Collapsed Latent Position Cluster Model for Social Networks
Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) <doi:10.1007/s11222-006-9014-7> which relies on the algorithm of Carpaneto and Toth (1980) <doi:10.1145/355873.355883>.
Maintained by Jason Wyse. Last updated 11 months ago.
2.00 score 7 scripts
tgno3
DJL:Distance Measure Based Judgment and Learning
Implements various decision support tools related to the Econometrics & Technometrics. Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, network DEA, dynamic DEA, intertemporal budgeting, etc.
Maintained by Dong-Joon Lim. Last updated 2 years ago.
1 stars 1.97 score 93 scriptshandcock
sspse:Estimating Hidden Population Size using Respondent Driven Sampling Data
Estimate the size of a networked population based on respondent-driven sampling data. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data. See Handcock, Gile and Mar (2014) <doi:10.1214/14-EJS923>, Handcock, Gile and Mar (2015) <doi:10.1111/biom.12255>, Kim and Handcock (2021) <doi:10.1093/jssam/smz055>, and McLaughlin, et. al. (2023) <doi:10.1214/23-AOAS1807>.
Maintained by Mark S. Handcock. Last updated 7 months ago.
1.86 score 18 scripts
matt-smith430
ITNr:Analysis of the International Trade Network
Functions to clean and process international trade data into an international trade network (ITN) are provided. It then provides a set a functions to undertake analysis and plots of the ITN (extract the backbone, centrality, blockmodels, clustering). Examining the key players in the ITN and regional trade patterns.
Maintained by Matthew Smith. Last updated 2 years ago.
1.78 score 47 scripts
smjenness
tergmLite:Fast Simulation of Simple Temporal Exponential Random Graph Models
Provides functions for the computationally efficient simulation of dynamic networks estimated with the statistical framework of temporal exponential random graph models, implemented in the 'tergm' package.
Maintained by Samuel M. Jenness. Last updated 3 years ago.
1.70 score 5 scripts
sduxbury
netmediate:Micro-Macro Analysis for Social Networks
Estimates micro effects on macro structures (MEMS) and average micro mediated effects (AMME). URL: <https://github.com/sduxbury/netmediate>. BugReports: <https://github.com/sduxbury/netmediate/issues>. Robins, Garry, Phillipa Pattison, and Jodie Woolcock (2005) <doi:10.1086/427322>. Snijders, Tom A. B., and Christian E. G. Steglich (2015) <doi:10.1177/0049124113494573>. Imai, Kosuke, Luke Keele, and Dustin Tingley (2010) <doi:10.1037/a0020761>. Duxbury, Scott (2023) <doi:10.1177/00811750231209040>. Duxbury, Scott (2024) <doi:10.1177/00811750231220950>.
Maintained by Scott Duxbury. Last updated 9 months ago.
1.70 scorecran
GameTheoryAllocation:Tools for Calculating Allocations in Game Theory
Many situations can be modeled as game theoretic situations. Some procedures are included in this package to calculate the most important allocations rules in Game Theory: Shapley value, Owen value or nucleolus, among other. First, we must define as an argument the value of the unions of the envolved agents with the characteristic function.
Maintained by Alejandro Saavedra-Nieves. Last updated 9 years ago.
1 stars 1.48 score 1 dependentssduxbury
ergMargins:Process Analysis for Exponential Random Graph Models
Calculates marginal effects and conducts process analysis in exponential family random graph models (ERGM). Includes functions to conduct mediation and moderation analyses and to diagnose multicollinearity. URL: <https://github.com/sduxbury/ergMargins>. BugReports: <https://github.com/sduxbury/ergMargins/issues>. Duxbury, Scott W (2021) <doi:10.1177/0049124120986178>. Long, J. Scott, and Sarah Mustillo (2018) <doi:10.1177/0049124118799374>. Mize, Trenton D. (2019) <doi:10.15195/v6.a4>. Karlson, Kristian Bernt, Anders Holm, and Richard Breen (2012) <doi:10.1177/0081175012444861>. Duxbury, Scott W (2018) <doi:10.1177/0049124118782543>. Duxbury, Scott W, Jenna Wertsching (2023) <doi:10.1016/j.socnet.2023.02.003>. Huang, Peng, Carter Butts (2023) <doi:10.1016/j.socnet.2023.07.001>.
Maintained by Scott Duxbury. Last updated 11 months ago.
1.48 score 3 scripts 1 dependents
johnnyzhz
networksem:Network Structural Equation Modeling
Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at <https://bigsem.psychstat.org>.
Maintained by Zhiyong Zhang. Last updated 9 days ago.
1.30 scorecran
VBLPCM:Variational Bayes Latent Position Cluster Model for Networks
Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013) <doi:10.1016/j.csda.2012.08.004>.
Maintained by Michael Salter-Townshend. Last updated 2 years ago.
2 stars 1.30 score
gormleyi
MEclustnet:Fit the Mixture of Experts Latent Position Cluster Model to Network Data
Functions to facilitate model-based clustering of nodes in a network in a mixture of experts setting, which incorporates covariate information on the nodes in the modelling process. Isobel Claire Gormley and Thomas Brendan Murphy (2010) <doi:10.1016/j.stamet.2010.01.002>.
Maintained by Isobel Claire Gormley. Last updated 5 years ago.
1.04 score 11 scriptscran
Inventorymodel:Inventory Models
Determination of the optimal policy in inventory problems from a game-theoretic perspective.
Maintained by Alejandro Saavedra Nieves. Last updated 2 years ago.
1 stars 1.00 scorecran
Neighboot:Neighborhood Bootstrap Method for RDS
A bootstrap method for Respondent-Driven Sampling (RDS) that relies on the underlying structure of the RDS network to estimate uncertainty.
Maintained by Mamadou Yauck. Last updated 3 years ago.
1.00 score
juan-goncalves-dosantos
ProjectManagement:Management of Deterministic and Stochastic Projects
Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities' time (Castro, Gómez & Tejada (2007) <doi:10.1016/j.orl.2007.01.003>). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) <doi:10.1016/j.dam.2017.08.012>). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out.
Maintained by Juan Carlos Gonçalves Dosantos. Last updated 5 months ago.
1.00 score 9 scriptscran
MultiplierDEA:Multiplier Data Envelopment Analysis and Cross Efficiency
Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.
Maintained by Aurobindh Kalathil Puthanpura. Last updated 3 years ago.
1 stars 1.00 scorecran
DIconvex:Finding Patterns of Monotonicity and Convexity in Data
Given an initial set of points, this package minimizes the number of elements to discard from this set such that there exists at least one monotonic and convex mapping within pre-specified upper and lower bounds.
Maintained by Liudmila Karagyaur. Last updated 7 years ago.
1.00 scorecran
GameTheory:Cooperative Game Theory
Implementation of a common set of punctual solutions for Cooperative Game Theory.
Maintained by Sebastian Cano-Berlanga. Last updated 2 years ago.
1.00 score
mrmarjan
nemBM:Using Network Evolution Models to Generate Networks with Selected Blockmodel Type
To study network evolution models and different blockmodeling approaches. Various functions enable generating (temporal) networks with a selected blockmodel type, taking into account selected local network mechanisms. The development of this package is financially supported the Slovenian Research Agency (www.arrs.gov.si) within the research program P5<96>0168 and the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).
Maintained by Marjan Cugmas. Last updated 2 years ago.
1.00 score