copy of part of document contained in initial exploratory work on defining categories, and contained in statistical-software
repo.
simode
: inference, dynamical systems, likelihood, differential equations; input: equations; output: parameter estimatesmlr3
: ML, workflow; input: parameters; output: trained ML modelperccalc
: categorical variables, estimates; input: data.frame; output: numeric vectors, parametersxrnet
: regression, high-dimensional data; input: data.frame, parameters; output: model estimatesunivariateML
: estimates, maximum liklihood, probability density, probability distributions; input: numeric vector; output: model estimatestabula
: applied statistics, chronology, discrete data, index; input: matrix; output: summary statistics, visualizationmemochange
: time series, non-stationarity; input: numeric vector, time series; output: summary statistics, model estimateshopit
: discrete data, ordered response data, reporting, summary statistics; input: data.frame, categorical data; output: model estimates, summary statisticsmodelStudio
: ML, workflow, EDA; input: data.frame, ML data; output: model prediction, visualizationDscoreApp
: categorical variables, estimates, error estimates; input: data.frame, application-specific data format; output: summary statistics, visualizationthurstonianIRT
: latent variables, categorical variables, pairwise comparisons, model probability; input: data.frame, categorical data; output: model estimates, summary statisticsmolic
: outlier detection, high-dimensional data, networks; input: application-specific data format; output: summary statistics, model estimateskdensity
: non-parametric, density estimator, probability density, probability distributions, kernel density, maximum likelihood; input: data.frame, numeric vector; output: S3 functionccostr
: non-stationarity, estimates, survival; input: numeric vector; output: summary statisticsSmartEDA
: EDA, workflow, variable selection, descriptive statistics, information statistics, summary statistics; input: numeric vectors; output: summary statisticsmodeLLtest
: model selection, cross-validation, likelihood; input: data.frame, formula; output: summary statisticssurvPen
: survival, regression, parameter estimation, multidimensional model, splines; input: numeric vector; output: model estimatesbayestestR
: Bayesian, null-hypothesis testing; input: numeric vectors, formula; output: summary statisticsgreta
: Bayesian, Monte Carlo, likelihood; input: numeric vectors, formula; output: summary statistics, model predictionivis
: dimensionality reduction, neural network, ML; input: data.frame; output: model estimateslfda
: dimensionality reduction; input: data.frame; output: model estimatestcherry
: trees, networks, maximum likelihood, categorical variables; input: data.frame; output: network, adjacency matrixfmcmc
: Monte Carlo; input: numeric vector, data.frame; output: model estimates, summary statisticsR-fitODBOD
: binomial distribution, over-dispersion; input: data.frame; output: summary statisticsqtl2pleio
: gene loci, random effects, generalized least squares; input: genetic data; output: summary statisticsmodelDown
: ML, summary statistics, graphical output; input: ML data; output: summary web pageinsight
: statistical models, summary statistics, algorithm choice; input: data.frame, numeric vector; output: summary statisticsReinforcementLearning
: reinforcement, ML, sampling, model strategy, dimensionality reduction; input: data.frame, numeric vectors; output: model estimates, summary statisticstsfeaturex
: time series, dimensionality reduction, feature selection; input: time series; output: summary statistics, model estimatesareal
: interpolation, areal weights; input: spatial data; output: model estimatesriskclustr
: risk, clustering, dimensionality reduction; input: data.frame, numeric vectors; output: summary statistics, model estimatesChiRP
: Monte Carlo, regression, clustering; input: data.frame, numeric vectors; output: model estimatesklrfome
: kernel density, kernel logistic regression, spatial, areal statistics, similarity statistics, focal windows; input: data.frame; output: summary statisticsBoltzMM
: ML, probability density, probability distributions, matrix algebra; input: binary vectors; output: summary statistics, probabilitypolyCub
: integration, cubature; input: numeric vector; output: summary statisticsmbir
: inference, effect sizes, test selection; input: numeric vectors; output: statistical parameters, summary statisticsarviz
: Bayesian, model selection, EDA; input: ML data, data.frame; output: summary statisticsoverlapping
: kernel density, probability distributions; input: numeric vectors; output: summary statisticsiRF
: random forests, dimensionality reduction; input: ML data; output: model estimatesqsort
: categorical variables; input: application-specific data format; output: model estimatesssdtools
: probability distributions, information statistics, confidence intervals, maximum likelihood; input: data.frame; output: model estimatesgravity
: probability distributions, regression; input: data.frame; output: model estimates, summary statisticssurvxai
: ML, regression, model selection, survival, visualization; input: ML data; output: summary statistics, reportsrr2
: correlation, variance; input: numeric vectors; output: summary statisticsdml
: ML, distance metrics; input: data.frame; output: summary statisticsdata.adapt.multi.test
: inference, effect sizes, dimensionality reduction; input: data.frame, ML data; output: summary statisticscompboost
: dimensionality reduction, feature selection, regression, splines, visualization, survival, functional data analysis; input: data.frame; output: model prediction, summary statisticsmultistateutils
: discrete data, multi-state model, survival; input: numeric & categorical variables; output: model estimates, model predictionsungroup
: histograms, grouped data, latent variables, regression; input: binned data; output: model estimatesblendR
: sampling, probability density, probability distributions, estimates, bias; input: numeric vectors; output: summary statisticsTDAstats
: dimensionality reduction, feature selection, workflow; input: data.frame, numeric variables; output: model estimates, summary statisticshhi
: index, visualization, overlap; input: data.frame, numeric variables; output: summary statistics, visualizationlogKDE
: EDA, density estimator, kernel density; input: data.frame, numeric variables; output: model estimatesggeffects
: regression, interaction terms, marginal effects; input: data.frame, numeric variables; output: summary statistics, model predictionsiml
: ML, feature selection; input: ML data; output: model estimatesrsimsum
: Monte Carlo, estimates, simulation; input: data.frame, ML data; output: summary statistics, visualizationphilentropy
: similarity statistics, distance metrics; input: numeric variables, data.frame; output: summary statisticsdisclapmix
: probability distributions; input: application-specific data; output: probability estimates, model estimates, summary statisticsqicharts2
: networks, stationarity; input: data.frame; output: summary statistics, visualizationscanstatistics
: spatial, clustering; input: data.frame; output: summary statisticshumanleague
: synthetic data, probability distributions, sampling; input: parameters; output: populationsautoplotly
: workflow, data transformation, visualization; input: data.frame, numeric variables; output: visualizationMCMCvis
: Monte Carlo, summary statistics; input: data.frame, ML data; output: summary statisticscomorbidity
: categorical variables, index; input: data.frame; output: model estimatesvtreat
: ML, data preparation, missing value processing, categorical variables; input: data.frame; output: transformed data.framecoalitions
: uncertainty, redistribution, threshold, aggregation, Monte Carlo, Bayesian; input: data.frame; output: model predictionivprobit-1.0
: probit model, misspecification; input: data.frame; output: model estimates, summary statisticsregistr
: functional data analysis, regression, warping; input: time series; output: model estimatesgrapherator
: networks, optimization, benchmarking; input: parameters; output: custom graph formatEFAshiny
: EDA, factor analysis, visualization; input: data.frame; output: web-based visualizationreper
: annotation, classification; input: application-specific data format; output: application-specific data formatpsycho.R
: reporting, model selection; input: data.frame, arbitrary data; output: summary statistics, visualizationorigami
: cross-validation, ML; input: data.frame; output: model predictionGammaGompertzCR
: survival, Bayesian, Monte Carlo; input: matrix; output: model predictionBayesianNetwork
: Bayesian, networks, ML; input: application-specific data format; output: summary statistics, visualizationhei
: index; input: data.frame, application-specific data format; output: index scoresmcMST
: networks, optimization; input: application-specific data format, graph; output: application-specific data format, graphvaristran
: noise, variance; input: application-specific data format; output: summary statistics, model estimates, visualizationbiotmle
: optimization, covariates; input: application-specific data format; output: summary statistics, visualizationlearningCurve
: learning curve, estimates, aggregation; input: data.frame; output: model estimateswalkr
: Monte Carlo, spatial, sampling, dimensionality reduction; input: matrix, data.frame; output: model estimates, summary statisticsremBoot
: synthetic data, variance; input: data.frame; output: model estimatesKraljicMatrix
: sensitivity, matrix algebra; input: data.frame; output: summary statistics, model predictionnse
: standard error, estimates; input: data.frame, numeric vectors; output: summary statisticsRiskPortfolios
: risk, variance, covariance; input: data.frame, numeric vectors; output: model estimates, summary statisticsvbvs.concurrent
: functional data analysis, Bayesian; input: data.frame, time series; output: model predictions, summary statisticsrucrdtw
: warping, time series; input: time series; output: model estimatesCTLmapping
: correlation, ANCOVA; input: application-specific data format; output: summary statisticsedarf
: EDA, random forests, covariates, trees; input: data.frame; output: model predictiongwdegree
: networks, maximum entropy; input: application-specific data format; output: summary statistics