Showing 24 of total 24 results (show query)
r-forge
mvtnorm:Multivariate Normal and t Distributions
Computes multivariate normal and t probabilities, quantiles, random deviates, and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae parameterised by Cholesky factors of covariance or precision matrices are implemented for interval-censored and exact data, or a mix thereof. Score functions for these log-likelihoods are available. A class representing multiple lower triangular matrices and corresponding methods are part of this package.
Maintained by Torsten Hothorn. Last updated 1 months ago.
15.89 score 13k scripts 2.7k dependents
philchalmers
SimDesign:Structure for Organizing Monte Carlo Simulation Designs
Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.
Maintained by Phil Chalmers. Last updated 2 days ago.
monte-carlo-simulationsimulationsimulation-framework
62 stars 13.41 score 253 scripts 47 dependents
dsy109
mixtools:Tools for Analyzing Finite Mixture Models
Analyzes finite mixture models for various parametric and semiparametric settings. This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria). Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).
Maintained by Derek Young. Last updated 10 months ago.
mixture-modelsmixture-of-expertssemiparametric-regression
20 stars 11.34 score 1.4k scripts 56 dependents
reinhardfurrer
spam:SPArse Matrix
Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation("spam")' for details.
Maintained by Reinhard Furrer. Last updated 2 months ago.
1 stars 9.36 score 420 scripts 439 dependents
avehtari
aaltobda:Functionality and Data for the Aalto Course on Bayesian Data Analysis
Functionality and Data for the Aalto University Course on Bayesian Data Analysis.
Maintained by Aki Vehtari. Last updated 4 months ago.
bayesbayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-workflow
2.2k stars 8.93 score 159 scripts
bioc
qpgraph:Estimation of Genetic and Molecular Regulatory Networks from High-Throughput Genomics Data
Estimate gene and eQTL networks from high-throughput expression and genotyping assays.
Maintained by Robert Castelo. Last updated 2 days ago.
microarraygeneexpressiontranscriptionpathwaysnetworkinferencegraphandnetworkgeneregulationgeneticsgeneticvariabilitysnpsoftwareopenblas
3 stars 8.72 score 20 scripts 3 dependents
briencj
dae:Functions Useful in the Design and ANOVA of Experiments
The content falls into the following groupings: (i) Data, (ii) Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v) Matrix functions, (vi) Projector and canonical efficiency functions, and (vii) Miscellaneous functions. There is a vignette describing how to use the design functions for randomizing and assessing designs available as a vignette called 'DesignNotes'. The ANOVA functions facilitate the extraction of information when the 'Error' function has been used in the call to 'aov'. The package 'dae' can also be installed from <http://chris.brien.name/rpackages/>.
Maintained by Chris Brien. Last updated 4 months ago.
1 stars 8.62 score 356 scripts 7 dependents
fishfollower
stockassessment:State-Space Assessment Model
Fitting SAM...
Maintained by Anders Nielsen. Last updated 27 days ago.
49 stars 7.76 score 324 scripts 2 dependents
rezamoammadi
BDgraph:Bayesian Structure Learning in Graphical Models using Birth-Death MCMC
Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>, and Mohammadi et al. (2023) <doi:10.48550/arXiv.2307.00127>.
Maintained by Reza Mohammadi. Last updated 7 months ago.
8 stars 7.46 score 223 scripts 7 dependents
spkaluzny
splus2R:Supplemental S-PLUS Functionality in R
Currently there are many functions in S-PLUS that are missing in R. To facilitate the conversion of S-PLUS packages to R packages, this package provides some missing S-PLUS functionality in R.
Maintained by Stephen Kaluzny. Last updated 1 years ago.
1 stars 6.56 score 82 scripts 30 dependents
loelschlaeger
RprobitB:Bayesian Probit Choice Modeling
Bayes estimation of probit choice models, both in the cross-sectional and panel setting. The package can analyze binary, multivariate, ordered, and ranked choices, as well as heterogeneity of choice behavior among deciders. The main functionality includes model fitting via Markov chain Monte Carlo m ethods, tools for convergence diagnostic, choice data simulation, in-sample and out-of-sample choice prediction, and model selection using information criteria and Bayes factors. The latent class model extension facilitates preference-based decider classification, where the number of latent classes can be inferred via the Dirichlet process or a weight-based updating heuristic. This allows for flexible modeling of choice behavior without the need to impose structural constraints. For a reference on the method see Oelschlaeger and Bauer (2021) <https://trid.trb.org/view/1759753>.
Maintained by Lennart Oelschläger. Last updated 6 months ago.
bayesdiscrete-choiceprobitopenblascppopenmp
4 stars 5.45 score 1 scripts
staffanbetner
rethinking:Statistical Rethinking book package
Utilities for fitting and comparing models
Maintained by Richard McElreath. Last updated 4 months ago.
5.42 score 4.4k scriptsloelschlaeger
oeli:Utilities for Developing Data Science Software
Some general helper functions that I (and maybe others) find useful when developing data science software.
Maintained by Lennart Oelschläger. Last updated 4 months ago.
2 stars 5.38 score 1 scripts 4 dependents
tmsalab
cIRT:Choice Item Response Theory
Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.
Maintained by James Joseph Balamuta. Last updated 3 years ago.
armadillobayesianchoicecognitive-diagnostic-modelsgibbs-samplingitem-response-theoryrcpparmadilloopenblascppopenmp
4 stars 5.14 score 23 scripts
jfrench
SpatialTools:Tools for Spatial Data Analysis
Tools for spatial data analysis. Emphasis on kriging. Provides functions for prediction and simulation. Intended to be relatively straightforward, fast, and flexible.
Maintained by Joshua French. Last updated 2 years ago.
2 stars 4.38 score 100 scripts 4 dependents
kisungyou
Riemann:Learning with Data on Riemannian Manifolds
We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.
Maintained by Kisung You. Last updated 2 years ago.
10 stars 3.70 score 8 scripts
kylebaron
dmutate:Mutate Data Frames with Random Variates
Work within the 'dplyr' workflow to add random variates to your data frame. Variates can be added at any level of an existing column. Also, bounds can be specified for simulated variates.
Maintained by Kyle T Baron. Last updated 7 years ago.
1 stars 3.24 score 35 scripts
pdhoff
eigenmodel:Semiparametric Factor and Regression Models for Symmetric Relational Data
Estimation of the parameters in a model for symmetric relational data (e.g., the above-diagonal part of a square matrix), using a model-based eigenvalue decomposition and regression. Missing data is accommodated, and a posterior mean for missing data is calculated under the assumption that the data are missing at random. The marginal distribution of the relational data can be arbitrary, and is fit with an ordered probit specification. See Hoff (2007) <arXiv:0711.1146> for details on the model.
Maintained by Peter Hoff. Last updated 6 years ago.
2.83 score 22 scripts 6 dependentstmsalab
fourPNO:Bayesian 4 Parameter Item Response Model
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
Maintained by Steven Andrew Culpepper. Last updated 5 years ago.
armadillocognitive-diagnostic-modelsgibbs-sampleritem-response-theoryrcpprcpparmadilloopenblascppopenmp
1 stars 2.70 score 5 scripts
yunyishen
robustcov:Collection of Robust Covariance and (Sparse) Precision Matrix Estimators
Collection of methods for robust covariance and (sparse) precision matrix estimation based on Loh and Tan (2018) <doi:10.1214/18-EJS1427>.
Maintained by Yunyi Shen. Last updated 4 years ago.
precision-matrixrobust-estimatesopenblascppopenmp
1 stars 2.70 score
cran
beyondWhittle:Bayesian Spectral Inference for Time Series
Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2023) <doi:10.48550/arXiv.2303.11561>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.
Maintained by Renate Meyer. Last updated 4 months ago.
2 stars 1.30 score
mahditeimouri
mixbox:Observed Fisher Information Matrix for Finite Mixture Model
Developed for the following tasks. 1- simulating realizations from the canonical, restricted, and unrestricted finite mixture models. 2- Monte Carlo approximation for density function of the finite mixture models. 3- Monte Carlo approximation for the observed Fisher information matrix, asymptotic standard error, and the corresponding confidence intervals for parameters of the mixture models sing the method proposed by Basford et al. (1997) <https://espace.library.uq.edu.au/view/UQ:57525>.
Maintained by Mahdi Teimouri. Last updated 1 years ago.
1.00 score