Showing 8 of total 8 results (show query)
flr
FLCore:Core Package of FLR, Fisheries Modelling in R
Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.
Maintained by Iago Mosqueira. Last updated 10 days ago.
fisheriesflrfisheries-modelling
16 stars 8.78 score 956 scripts 23 dependents
yuimaproject
yuima:The YUIMA Project Package for SDEs
Simulation and Inference for SDEs and Other Stochastic Processes.
Maintained by Stefano M. Iacus. Last updated 4 days ago.
9 stars 7.02 score 92 scripts 2 dependents
fbartos
BayesTools:Tools for Bayesian Analyses
Provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating 'JAGS' and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.
Maintained by František Bartoš. Last updated 2 months ago.
7 stars 6.06 score 17 scripts 3 dependents
bpfaff
evir:Extreme Values in R
Functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions.
Maintained by Bernhard Pfaff. Last updated 9 years ago.
2 stars 5.89 score 211 scripts 6 dependents
mikejareds
hermiter:Efficient Sequential and Batch Estimation of Univariate and Bivariate Probability Density Functions and Cumulative Distribution Functions along with Quantiles (Univariate) and Nonparametric Correlation (Bivariate)
Facilitates estimation of full univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation (bivariate) using Hermite series based estimators. These estimators are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 <doi:10.1214/17-EJS1245>, Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) <doi:10.1007/s00184-020-00785-z> and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) <doi:10.1016/j.jmva.2021.104783>.
Maintained by Michael Stephanou. Last updated 7 months ago.
cumulative-distribution-functionkendall-correlation-coefficientonline-algorithmsprobability-density-functionquantilespearman-correlation-coefficientstatisticsstreaming-algorithmsstreaming-datacpp
15 stars 5.11 score 17 scripts
manueleleonelli
extrememix:Bayesian Estimation of Extreme Value Mixture Models
Fits extreme value mixture models, which are models for tails not requiring selection of a threshold, for continuous data. It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) <doi:10.1191/1471082X04st075oa>. FF do Nascimento, D. Gamerman, HF Lopes <doi:10.1007/s11222-011-9270-z>.
Maintained by Manuele Leonelli. Last updated 5 months ago.
2 stars 4.48 score 4 scripts
cran
CB2:CRISPR Pooled Screen Analysis using Beta-Binomial Test
Provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. Details are in Jeong et al. (2019) <doi:10.1101/gr.245571.118> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.
Maintained by Hyun-Hwan Jeong. Last updated 5 years ago.
3.00 score
druegamer
deepregression:Fitting Deep Distributional Regression
Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>. Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.
Maintained by David Ruegamer. Last updated 4 months ago.
2.28 score 63 scripts 1 dependents