Showing 8 of total 8 results (show query)
virgesmith
humanleague:Synthetic Population Generator
Generates high-entropy integer synthetic populations from marginal and (optionally) seed data using quasirandom sampling, in arbitrary dimensionality (Smith, Lovelace and Birkin (2017) <doi:10.18564/jasss.3550>). The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) <doi:10.13140/2.1.2480.9923>).
Maintained by Andrew Smith. Last updated 5 months ago.
c-plus-plus-11microsynthesisnodejspython3quasirandomsampling-methodscpp
18 stars 4.81 score 12 scripts
jmanitz
samplingbook:Survey Sampling Procedures
Sampling procedures from the book 'Stichproben - Methoden und praktische Umsetzung mit R' by Goeran Kauermann and Helmut Kuechenhoff (2010).
Maintained by Juliane Manitz. Last updated 4 years ago.
sampling-methodssampling-procedures
2 stars 4.78 score 54 scripts
stevenranney
AnglerCreelSurveySimulation:Simulate a Bus Route Creel Survey of Anglers
Simulate an angler population, sample the simulated population with a user-specified survey times, and calculate metrics from a bus route-type creel survey.
Maintained by Ranney Steven H.. Last updated 11 months ago.
bus-routecreel-surveyfisheriessampling-methods
1 stars 4.18 score 9 scripts
allenzhuaz
PPQplan:Process Performance Qualification (PPQ) Plans in Chemistry, Manufacturing and Controls (CMC) Statistical Analysis
Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham.
Maintained by Yalin Zhu. Last updated 3 years ago.
biostatisticspharmaceuticalssampling-methods
1 stars 4.11 score 13 scripts
jcatwood
VeccTMVN:Multivariate Normal Probabilities using Vecchia Approximation
Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" <doi:10.48550/arXiv.2311.09426>. Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" <doi:10.1080/10618600.1992.10477010> and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" <doi:10.48550/arXiv.1603.04166>.
Maintained by Jian Cao. Last updated 4 months ago.
normal-distributionsampling-methodsstatisticsfortranopenblascppopenmp
2 stars 3.56 score 36 scripts
francescopantalone
Spbsampling:Spatially Balanced Sampling
Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) <doi:10.18637/jss.v103.c02>, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194>, and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'.
Maintained by Francesco Pantalone. Last updated 3 years ago.
sampling-methodsspatial-datacpp
3 stars 3.18 score 5 scripts
nickkunz
crassmat:Conditional Random Sampling Sparse Matrices
Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.
Maintained by Nick Kunz. Last updated 5 years ago.
matrix-functionsmatrix-librarysampling-methods
1 stars 2.70 score
inbo
grtsdb:Create a Sampling Frame Using GRTS
Create an SQLite database containing the sampling framework for different levels of resolution. Take a sample from this framework.
Maintained by Thierry Onkelinx. Last updated 3 years ago.
grtssamplingsampling-methodssqlite
1 stars 2.70 score 9 scripts