stat-software-categories.md

copy of part of document contained in initial exploratory work on defining categories, and contained in statistical-software repo.

4. simode: inference, dynamical systems, likelihood, differential equations; input: equations; output: parameter estimates
5. mlr3: ML, workflow; input: parameters; output: trained ML model
6. perccalc: categorical variables, estimates; input: data.frame; output: numeric vectors, parameters
7. xrnet: regression, high-dimensional data; input: data.frame, parameters; output: model estimates
8. univariateML: estimates, maximum liklihood, probability density, probability distributions; input: numeric vector; output: model estimates
9. tabula: applied statistics, chronology, discrete data, index; input: matrix; output: summary statistics, visualization
10. memochange: time series, non-stationarity; input: numeric vector, time series; output: summary statistics, model estimates
11. hopit: discrete data, ordered response data, reporting, summary statistics; input: data.frame, categorical data; output: model estimates, summary statistics
12. modelStudio: ML, workflow, EDA; input: data.frame, ML data; output: model prediction, visualization
13. DscoreApp: categorical variables, estimates, error estimates; input: data.frame, application-specific data format; output: summary statistics, visualization
14. thurstonianIRT: latent variables, categorical variables, pairwise comparisons, model probability; input: data.frame, categorical data; output: model estimates, summary statistics
15. molic: outlier detection, high-dimensional data, networks; input: application-specific data format; output: summary statistics, model estimates
16. kdensity: non-parametric, density estimator, probability density, probability distributions, kernel density, maximum likelihood; input: data.frame, numeric vector; output: S3 function
17. ccostr: non-stationarity, estimates, survival; input: numeric vector; output: summary statistics
18. SmartEDA: EDA, workflow, variable selection, descriptive statistics, information statistics, summary statistics; input: numeric vectors; output: summary statistics
19. modeLLtest: model selection, cross-validation, likelihood; input: data.frame, formula; output: summary statistics
20. survPen: survival, regression, parameter estimation, multidimensional model, splines; input: numeric vector; output: model estimates
21. bayestestR: Bayesian, null-hypothesis testing; input: numeric vectors, formula; output: summary statistics
22. greta: Bayesian, Monte Carlo, likelihood; input: numeric vectors, formula; output: summary statistics, model prediction
23. ivis: dimensionality reduction, neural network, ML; input: data.frame; output: model estimates
24. lfda: dimensionality reduction; input: data.frame; output: model estimates
25. tcherry: trees, networks, maximum likelihood, categorical variables; input: data.frame; output: network, adjacency matrix
26. fmcmc: Monte Carlo; input: numeric vector, data.frame; output: model estimates, summary statistics
27. R-fitODBOD: binomial distribution, over-dispersion; input: data.frame; output: summary statistics
28. qtl2pleio: gene loci, random effects, generalized least squares; input: genetic data; output: summary statistics
29. modelDown: ML, summary statistics, graphical output; input: ML data; output: summary web page
30. insight: statistical models, summary statistics, algorithm choice; input: data.frame, numeric vector; output: summary statistics
31. ReinforcementLearning: reinforcement, ML, sampling, model strategy, dimensionality reduction; input: data.frame, numeric vectors; output: model estimates, summary statistics
32. tsfeaturex: time series, dimensionality reduction, feature selection; input: time series; output: summary statistics, model estimates
33. areal: interpolation, areal weights; input: spatial data; output: model estimates
34. riskclustr: risk, clustering, dimensionality reduction; input: data.frame, numeric vectors; output: summary statistics, model estimates
35. ChiRP: Monte Carlo, regression, clustering; input: data.frame, numeric vectors; output: model estimates
36. klrfome: kernel density, kernel logistic regression, spatial, areal statistics, similarity statistics, focal windows; input: data.frame; output: summary statistics
37. BoltzMM: ML, probability density, probability distributions, matrix algebra; input: binary vectors; output: summary statistics, probability
38. polyCub: integration, cubature; input: numeric vector; output: summary statistics
39. mbir: inference, effect sizes, test selection; input: numeric vectors; output: statistical parameters, summary statistics
40. arviz: Bayesian, model selection, EDA; input: ML data, data.frame; output: summary statistics
41. overlapping: kernel density, probability distributions; input: numeric vectors; output: summary statistics
42. iRF: random forests, dimensionality reduction; input: ML data; output: model estimates
43. qsort: categorical variables; input: application-specific data format; output: model estimates
44. ssdtools: probability distributions, information statistics, confidence intervals, maximum likelihood; input: data.frame; output: model estimates
45. gravity: probability distributions, regression; input: data.frame; output: model estimates, summary statistics
46. survxai: ML, regression, model selection, survival, visualization; input: ML data; output: summary statistics, reports
47. rr2: correlation, variance; input: numeric vectors; output: summary statistics
48. dml: ML, distance metrics; input: data.frame; output: summary statistics
49. data.adapt.multi.test: inference, effect sizes, dimensionality reduction; input: data.frame, ML data; output: summary statistics
50. compboost: dimensionality reduction, feature selection, regression, splines, visualization, survival, functional data analysis; input: data.frame; output: model prediction, summary statistics
51. multistateutils: discrete data, multi-state model, survival; input: numeric & categorical variables; output: model estimates, model predictions
52. ungroup: histograms, grouped data, latent variables, regression; input: binned data; output: model estimates
53. blendR: sampling, probability density, probability distributions, estimates, bias; input: numeric vectors; output: summary statistics
54. TDAstats: dimensionality reduction, feature selection, workflow; input: data.frame, numeric variables; output: model estimates, summary statistics
55. hhi: index, visualization, overlap; input: data.frame, numeric variables; output: summary statistics, visualization
56. logKDE: EDA, density estimator, kernel density; input: data.frame, numeric variables; output: model estimates
57. ggeffects: regression, interaction terms, marginal effects; input: data.frame, numeric variables; output: summary statistics, model predictions
58. iml: ML, feature selection; input: ML data; output: model estimates
59. rsimsum: Monte Carlo, estimates, simulation; input: data.frame, ML data; output: summary statistics, visualization
60. philentropy: similarity statistics, distance metrics; input: numeric variables, data.frame; output: summary statistics
61. disclapmix: probability distributions; input: application-specific data; output: probability estimates, model estimates, summary statistics
62. qicharts2: networks, stationarity; input: data.frame; output: summary statistics, visualization
63. scanstatistics: spatial, clustering; input: data.frame; output: summary statistics
64. humanleague: synthetic data, probability distributions, sampling; input: parameters; output: populations
65. autoplotly: workflow, data transformation, visualization; input: data.frame, numeric variables; output: visualization
66. MCMCvis: Monte Carlo, summary statistics; input: data.frame, ML data; output: summary statistics
67. comorbidity: categorical variables, index; input: data.frame; output: model estimates
68. vtreat: ML, data preparation, missing value processing, categorical variables; input: data.frame; output: transformed data.frame
69. coalitions: uncertainty, redistribution, threshold, aggregation, Monte Carlo, Bayesian; input: data.frame; output: model prediction
70. ivprobit-1.0: probit model, misspecification; input: data.frame; output: model estimates, summary statistics
71. registr: functional data analysis, regression, warping; input: time series; output: model estimates
72. grapherator: networks, optimization, benchmarking; input: parameters; output: custom graph format
73. EFAshiny: EDA, factor analysis, visualization; input: data.frame; output: web-based visualization
74. reper: annotation, classification; input: application-specific data format; output: application-specific data format
75. psycho.R: reporting, model selection; input: data.frame, arbitrary data; output: summary statistics, visualization
76. origami: cross-validation, ML; input: data.frame; output: model prediction
77. GammaGompertzCR: survival, Bayesian, Monte Carlo; input: matrix; output: model prediction
78. BayesianNetwork: Bayesian, networks, ML; input: application-specific data format; output: summary statistics, visualization
79. hei: index; input: data.frame, application-specific data format; output: index scores
80. mcMST: networks, optimization; input: application-specific data format, graph; output: application-specific data format, graph
81. varistran: noise, variance; input: application-specific data format; output: summary statistics, model estimates, visualization
82. biotmle: optimization, covariates; input: application-specific data format; output: summary statistics, visualization
83. learningCurve: learning curve, estimates, aggregation; input: data.frame; output: model estimates
84. walkr: Monte Carlo, spatial, sampling, dimensionality reduction; input: matrix, data.frame; output: model estimates, summary statistics
85. remBoot: synthetic data, variance; input: data.frame; output: model estimates
86. KraljicMatrix: sensitivity, matrix algebra; input: data.frame; output: summary statistics, model prediction
87. nse: standard error, estimates; input: data.frame, numeric vectors; output: summary statistics
88. RiskPortfolios: risk, variance, covariance; input: data.frame, numeric vectors; output: model estimates, summary statistics
89. vbvs.concurrent: functional data analysis, Bayesian; input: data.frame, time series; output: model predictions, summary statistics
90. rucrdtw: warping, time series; input: time series; output: model estimates
91. CTLmapping: correlation, ANCOVA; input: application-specific data format; output: summary statistics
92. edarf: EDA, random forests, covariates, trees; input: data.frame; output: model prediction
93. gwdegree: networks, maximum entropy; input: application-specific data format; output: summary statistics