Showing 200 of total 237 results (show query)

kurthornik

clue:Cluster Ensembles

CLUster Ensembles.

Maintained by Kurt Hornik. Last updated 4 months ago.

19.0 match 2 stars 9.85 score 496 scripts 401 dependents

mrc-ide

naomi:Naomi Model for Subnational HIV Estimates

This package implements the Naomi model for subnational HIV estimates.

Maintained by Jeff Eaton. Last updated 6 days ago.

cpp

10.9 match 9 stars 7.74 score 54 scripts 2 dependents

vlarmet

cppRouting:Algorithms for Routing and Solving the Traffic Assignment Problem

Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>), bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://archive.siam.org/meetings/alenex05/papers/03agoldberg.pdf>), A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>), new bidirectional A* (Pijls & Post (2009) <https://repub.eur.nl/pub/16100/ei2009-10.pdf>), Contraction hierarchies (R. Geisberger, P. Sanders, D. Schultes and D. Delling (2008) <doi:10.1007/978-3-540-68552-4_24>), PHAST (D. Delling, A.Goldberg, A. Nowatzyk, R. Werneck (2011) <doi:10.1016/j.jpdc.2012.02.007>). Algorithms for solving the traffic assignment problem are All-or-Nothing assignment, Method of Successive Averages, Frank-Wolfe algorithm (M. Fukushima (1984) <doi:10.1016/0191-2615(84)90029-8>), Conjugate and Bi-Conjugate Frank-Wolfe algorithms (M. Mitradjieva, P. O. Lindberg (2012) <doi:10.1287/trsc.1120.0409>), Algorithm-B (R. B. Dial (2006) <doi:10.1016/j.trb.2006.02.008>).

Maintained by Vincent Larmet. Last updated 9 months ago.

algorithmalgorithm-bbidirectional-a-star-algorithmc-plus-pluscontraction-hierarchiesdijkstra-algorithmdistancefrank-wolfeisochronesparallel-computingrcppshortest-pathstraffic-assignmentcpp

10.0 match 112 stars 7.42 score 39 scripts 4 dependents

usaid-oha-si

Wavelength:Wavelength

USAID OHA Office. Munging of mission weekly HFR data.

Maintained by Aaron Chafetz. Last updated 2 years ago.

12.8 match 3 stars 3.39 score 55 scripts

r-dbi

odbc:Connect to ODBC Compatible Databases (using the DBI Interface)

A DBI-compatible interface to ODBC databases.

Maintained by Hadley Wickham. Last updated 13 days ago.

databaseodbcunixodbccpp

1.8 match 396 stars 16.22 score 2.9k scripts 22 dependents

chavent

ClustOfVar:Clustering of Variables

Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.

Maintained by Marie Chavent. Last updated 5 years ago.

3.9 match 7 stars 6.47 score 142 scripts 2 dependents

davidsjoberg

hablar:Non-Astonishing Results in R

Simple tools for converting columns to new data types. Intuitive functions for columns with missing values.

Maintained by David Sjoberg. Last updated 2 years ago.

3.0 match 59 stars 8.30 score 468 scripts

beckerbenj

eatGADS:Data Management of Large Hierarchical Data

Import 'SPSS' data, handle and change 'SPSS' meta data, store and access large hierarchical data in 'SQLite' data bases.

Maintained by Benjamin Becker. Last updated 23 days ago.

3.1 match 1 stars 7.36 score 34 scripts 1 dependents

briencj

asremlPlus:Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences

Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' <https://vsni.co.uk/> as 'asreml-R', who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 28 days ago.

asremlmixed-models

2.1 match 19 stars 9.34 score 200 scripts

merliseclyde

BAS:Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Maintained by Merlise Clyde. Last updated 4 months ago.

bayesianbayesian-inferencegeneralized-linear-modelslinear-regressionlogistic-regressionmcmcmodel-selectionpoisson-regressionpredictive-modelingregressionvariable-selectionfortranopenblas

1.7 match 44 stars 10.81 score 420 scripts 3 dependents

anttonalberdi

hilldiv:Integral Analysis of Diversity Based on Hill Numbers

Tools for analysing, comparing, visualising and partitioning diversity based on Hill numbers. 'hilldiv' is an R package that provides a set of functions to assist analysis of diversity for diet reconstruction, microbial community profiling or more general ecosystem characterisation analyses based on Hill numbers, using OTU/ASV tables and associated phylogenetic trees as inputs. The package includes functions for (phylo)diversity measurement, (phylo)diversity profile plotting, (phylo)diversity comparison between samples and groups, (phylo)diversity partitioning and (dis)similarity measurement. All of these grounded in abundance-based and incidence-based Hill numbers. The statistical framework developed around Hill numbers encompasses many of the most broadly employed diversity (e.g. richness, Shannon index, Simpson index), phylogenetic diversity (e.g. Faith's PD, Allen's H, Rao's quadratic entropy) and dissimilarity (e.g. Sorensen index, Unifrac distances) metrics. This enables the most common analyses of diversity to be performed while grounded in a single statistical framework. The methods are described in Jost et al. (2007) <DOI:10.1890/06-1736.1>, Chao et al. (2010) <DOI:10.1098/rstb.2010.0272> and Chiu et al. (2014) <DOI:10.1890/12-0960.1>; and reviewed in the framework of molecularly characterised biological systems in Alberdi & Gilbert (2019) <DOI:10.1111/1755-0998.13014>.

Maintained by Antton Alberdi. Last updated 4 years ago.

4.0 match 11 stars 4.35 score 41 scripts

pharmar

riskmetric:Risk Metrics to Evaluating R Packages

Facilities for assessing R packages against a number of metrics to help quantify their robustness.

Maintained by Eli Miller. Last updated 9 days ago.

1.7 match 167 stars 8.89 score 43 scripts

bioc

GlobalAncova:Global test for groups of variables via model comparisons

The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany.

Maintained by Manuela Hummel. Last updated 5 months ago.

microarrayonechanneldifferentialexpressionpathwaysregression

2.3 match 5.32 score 9 scripts 1 dependents

christophergandrud

d3Network:The Old Package for Creating D3 JavaScript Network, Tree, Dendrogram, and Sankey Graphs

!!! NOTE: Active development has moved to the networkD3 package. !!!

Maintained by Christopher Gandrud. Last updated 10 years ago.

1.8 match 172 stars 6.63 score 82 scripts

cpfaff

rtematres:Exploit vocabularies on tematres server.

Exploit vocabularies on tematres server and annotate data frames in R.

Maintained by Claas-Thido Pfaff. Last updated 10 years ago.

4.3 match 1 stars 2.70 score 3 scripts

braverock

FinancialInstrument:Financial Instrument Model Infrastructure for R

Infrastructure for defining meta-data and relationships for financial instruments.

Maintained by Ross Bennett. Last updated 7 years ago.

1.8 match 19 stars 4.99 score 102 scripts

r-forge

stops:Structure Optimized Proximity Scaling

Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices.

Maintained by Thomas Rusch. Last updated 2 months ago.

openjdk

1.6 match 1 stars 4.48 score 23 scripts

marsdu1989

easyAHP:Analytic Hierarchy Process (AHP)

Given the scores from decision makers, the analytic hierarchy process can be conducted easily.

Maintained by Zhicheng Du. Last updated 7 years ago.

5.5 match 1.00 score 1 scripts

gagolews

genieclust:Fast and Robust Hierarchical Clustering with Noise Points Detection

A retake on the Genie algorithm (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>), which is a robust hierarchical clustering method (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>). It is now faster and more memory efficient; determining the whole cluster hierarchy for datasets of 10M points in low dimensional Euclidean spaces or 100K points in high-dimensional ones takes only a minute or so. Allows clustering with respect to mutual reachability distances so that it can act as a noise point detector or a robustified version of 'HDBSCAN*' (that is able to detect a predefined number of clusters and hence it does not dependent on the somewhat fragile 'eps' parameter). The package also features an implementation of inequality indices (e.g., Gini and Bonferroni), external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). See also the 'Python' version of 'genieclust' available on 'PyPI', which supports sparse data, more metrics, and even larger datasets.

Maintained by Marek Gagolewski. Last updated 5 days ago.

cluster-analysisclusteringclustering-algorithmdata-analysisdata-miningdata-sciencegeniehdbscanhierarchical-clusteringhierarchical-clustering-algorithmmachine-learningmachine-learning-algorithmsmlpacknmslibpythonpython3sparsecppopenmp

0.5 match 61 stars 7.29 score 13 scripts 5 dependents

jienagu

forestry:Reshape Data Tree

'forestry' a series of utility functions to help with reshaping hierarchy of data tree, and reform the structure of data tree.

Maintained by Jiena McLellan. Last updated 5 years ago.

0.5 match 21 stars 5.66 score 44 scripts