Ramsey-Cass-Koopmans Model

Frank Ramsey, David Cass, Tjalling Koopmans

The Ramsey-Cass-Koopmans model, developed initially by Frank Ramsey in 1928 and formalized by David Cass and Tjalling Koopmans in 1965, is the workhorse model of macroeconomic growth theory. It describes how rational consumers optimize consumption and savings over an infinite horizon, subject to an aggregate production function, and derives the long-run growth path and the optimal allocation of resources.

Ramsey, F. P. (1928). A Mathematical Theory of Saving. Economic Journal, 38(152), 543–559.

Cass, D. (1965). Optimality and the Dynamic Stability of Equilibrium. Metroeconomica, 16(2), 101–115.

Koopmans, T. C. (1965). On the Concept of Optimal Economic Growth. Pontificiae Academiae Scientiarum Scripta Varia, 28, 1–75.


Ramsey RESET Test

Ramsey Regression Equation Specification Error Test (RESET)

RESET test
regression specification error test
Ramsey RESET fonksiyonel form testi

The Ramsey RESET test, proposed by James Ramsey in 1969, is a general test for functional-form misspecification in a linear regression — for omitted nonlinear relationships between the response and the regressors. It adds powers of the fitted values to the model and checks whether they significantly improve the fit; if they do, the original linear specification has left systematic structure unexplained.

Test for functional-form misspecification

Model is correctly specified (no omitted nonlinearity)

Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society: Series B, 31(2), 350–371.

ols-regression multiple-linear-regression polynomial-regression white-test


Random Effects Panel Model

random effects panel regression
RE estimator
GLS panel estimator
Panel Rassal Etkiler Modeli

Baltagi (textbook treatment); Hausman specification test

The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed parameter. Its validity is judged with the Hausman (1978) specification test, and it is developed in standard treatments such as Baltagi's Econometric Analysis of Panel Data.

Feasible generalized least squares (GLS)

Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251-1271.

Baltagi, B. H. (2005). Econometric Analysis of Panel Data. Wiley.

panel-fixed-effects hausman-test ols-regression hlm pooled-ols


Random Forest

Random Forest (Breiman Ensemble of Decision Trees)

Rastgele Orman (Random Forest)
rastgele orman
random decision forest
bagged tree ensemble

Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.

Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.

James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 8). Springer.

xgboost decision-tree svm-classification logistic-regression linear-regression


Random Projection

Random Projection (Johnson-Lindenstrauss Dimensionality Reduction)

random projections
Johnson-Lindenstrauss projection
sparse random projection
rastgele izdüşüm

Johnson & Lindenstrauss (lemma); Achlioptas (sparse variant)

Random projection reduces dimensionality by multiplying the data by a random matrix, relying on the Johnson-Lindenstrauss lemma (1984), which guarantees that projecting onto enough random directions approximately preserves all pairwise distances. Unlike PCA it does not analyze the data at all — the projection is random and data-oblivious — making it extremely cheap and well suited to very high-dimensional data and streaming or privacy-sensitive settings.

Linear, data-oblivious dimensionality reduction

Approximate pairwise-distance preservation (JL lemma)

Johnson, W. B., & Lindenstrauss, J. (1984). Extensions of Lipschitz mappings into a Hilbert space. Contemporary Mathematics, 26, 189–206.

Achlioptas, D. (2003). Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences, 66(4), 671–687.

principal-component-analysis locally-linear-embedding umap matrix-completion


Random Survival Forest

RSF
Rastgele Sağkalım Ormanı (RSF)
survival random forest

Random Survival Forest (RSF), introduced by Ishwaran, Kogalur, Blackstone, and Lauer in 2008, is an ensemble machine learning method that adapts the Random Forest algorithm to time-to-event (survival) data. Trees are grown using log-rank splitting to handle censored observations naturally, and the ensemble aggregates cumulative hazard functions across hundreds of trees to produce predictions and variable importance rankings.

Ensemble machine learning survival model

Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. (2008). Random Survival Forests. Annals of Applied Statistics, 2(3), 841–860.

cox-ph kaplan-meier nelson-aalen fine-gray-model weibull-aft


Random Utility Model

Random Utility Model with Probabilistic Choice

Discrete Choice Model
Probabilistic Choice
Stochastic Utility

The Random Utility Model explains discrete choice behavior by assuming agents derive uncertain utilities from alternatives and choose the option yielding highest utility. Introduced by Daniel McFadden in 1974, the model decomposes utility into systematic (observable) and random (idiosyncratic) components, permitting probabilistic choice predictions. The logit model, a parametric specification, yields closed-form choice probabilities that are widely used in marketing, transportation, and environmental valuation.

McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press.

Train, K. E. (2009). Discrete Choice Methods with Simulation (Second Edition). Cambridge University Press.

nash-equilibrium bayesian-nash-equilibrium principal-agent-model arrow-debreu-equilibrium


Randomization Inference

randomizasyon çıkarımı (fisher exact randomization)

Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is regarded as the gold standard for analysing designed experiments because its validity rests on the known assignment mechanism rather than on distributional assumptions.

Permutation distribution of a test statistic under the sharp null

Fisher sharp null (no treatment effect for any unit)

Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.

Imbens, G. W. & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press.

bootstrap-inference jackknife permutation-test quantile-regression-np ols-regression


Randomized clinical trial

RCT
randomized controlled trial
randomised controlled trial
clinical randomized trial

1948 (first rigorously conducted RCT — MRC streptomycin trial)

Austin Bradford Hill; MRC Streptomycin Trial team

A randomized clinical trial (RCT) is an experimental study design in which participants are randomly assigned to an intervention group or a control group, then followed prospectively to compare outcomes. Random allocation is the defining feature: it distributes known and unknown confounders across groups by chance, making the RCT the strongest individual study design for establishing causal efficacy of a treatment or intervention under controlled conditions.

Participant-level outcome data (continuous, binary, time-to-event)

Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015). Fundamentals of Clinical Trials (5th ed.). Springer.

Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.

cohort-study case-control-study cross-sectional-epidemiological-study phase-ii-clinical-trial phase-iii-clinical-trial survival-analysis


Randomized Complete Block Design

RCBD
randomized block design
complete block design
Tesadüf Bloklu Desen (RCBD)

The Randomized Complete Block Design (RCBD) is a parametric experimental design and hypothesis-testing framework that isolates and removes a known source of heterogeneity — called a block — before comparing treatment means. Introduced by Ronald A. Fisher in his 1935 monograph The Design of Experiments, it remains the foundational blocked design in agricultural, clinical, and industrial research.

Montgomery, D.C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.

Fisher, R.A. (1935). The Design of Experiments. Oliver & Boyd.

one-way-anova two-way-anova repeated-measures-anova latin-square-design friedman-test completely-randomized-design


Randomized Controlled Trial

Randomize Kontrollü Çalışma (RCT) Tasarımı

James Lind (early precursor, 1747); modern formulation: Austin Bradford Hill & Medical Research Council (1948)

A randomized controlled trial (RCT) is the gold standard experimental design in clinical and health research, in which participants are randomly allocated to a treatment group or a control group so that the effect of an intervention can be measured with the highest possible degree of internal validity. The modern parallel-group RCT was formalized by Austin Bradford Hill and the Medical Research Council in their landmark streptomycin trial of 1948, and its reporting is governed today by the CONSORT 2010 guidelines (Schulz et al., 2010).

simple randomization, stratified randomization, block randomization, single-blind, double-blind, triple-blind

intention-to-treat (ITT), per-protocol (PP)

Schulz, K.F., Altman, D.G., Moher, D., for the CONSORT Group (2010). CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomised Trials. BMJ, 340, c332.

Pocock, S.J. (1983). Clinical Trials: A Practical Approach. Wiley.

crossover-design adaptive-design factorial-design paired-t-test independent-t-test one-way-anova survival-analysis


Range of Motion Goniometry

Range of Motion Assessment via Goniometry

Goniometry is the standardized clinical measurement of joint angles using a goniometer (angle-measuring instrument) to quantify range of motion in degrees. Developed from orthopedic assessment traditions, goniometric measurement is a fundamental skill in physical therapy, occupational therapy, and orthopedic medicine for baseline assessment, monitoring progression, and documenting rehabilitation outcomes.

Norkin, C. C., & White, D. J. (2009). Measurement of joint motion: A guide to goniometry (4th ed.). F.A. Davis Company.

Boone, D. C., & Azen, S. P. (1979). Normal range of motion of joints in male subjects. Journal of Bone and Joint Surgery, 61(5), 756-759.


Rank Aggregation

Rank Fusion
Order Aggregation
Preference Aggregation
Sıralama Birleştirme

Rank Aggregation is a family of methods that combine multiple ranked lists of alternatives into a single consensus ranking. Formally studied in the context of web search by Dwork, Kumar, Naor, and Sivakumar (2001), these methods address the problem of synthesizing divergent preference orderings from multiple sources — such as search engines, expert judges, or voter ballots — into one coherent, representative ordering that minimizes overall disagreement across the input rankings.

Input: Multiple ranked lists of alternatives

Output: A single consensus ranked list

Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001). Rank aggregation methods for the web. Proceedings of the 10th International Conference on World Wide Web, 613–622.


RANK-REVERSAL

RANK-REVERSAL (Rank Reversal Analysis — Detection of ranking instability when alternatives are added/removed) is a ranking multi-criteria decision-making (MCDM) method introduced by Triantaphyllou, E. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Robustness diagnostic — rank reversal detection and quantification

Triantaphyllou, E. (2000). Multi-Criteria Decision Making Methods: A Comparative Study. Kluwer Academic Publishers, Dordrecht


Ranked Set Sampling

Ranked Set Sampling (RSS) is a data collection method introduced by G. A. McIntyre in 1952 that improves estimation efficiency when visual ranking of units is easier or cheaper than actual measurement. By deliberately selecting and measuring units that are ranked as most likely to yield desired outcomes, RSS reduces variance compared to simple random sampling while maintaining unbiasedness.

McIntyre, G. A. (1952). A method for unbiased selective sampling using ranked sets. Australian Journal of Agricultural Research, 3(4), 385–390.

Takahasi, K., & Wakimoto, K. (1968). On unbiased estimates of population mean based on the sample stratified by successive groups. Annals of the Institute of Statistical Mathematics, 20(1), 1–31.

Wolfe, D. A. (1992). Illustrated concepts of ranked-set sampling. The American Statistician, 46(4), 229–232.

stratified-sampling double-sampling systematic-sampling cluster-sampling


Rankine Cycle

Rankine Cycle for Steam Power Generation

Clausius-Rankine cycle
steam cycle
vapor power cycle

The Rankine Cycle is the fundamental thermodynamic cycle for steam power plants. It describes how thermal energy from burning fuel or concentrated solar radiation is converted to mechanical work and ultimately electricity. The cycle consists of four processes: isobaric heat addition in the boiler, isentropic expansion through the turbine, isobaric heat rejection in the condenser, and isentropic compression by the pump.

Smith, J. M., Van Ness, H. C., & Abbott, M. M. (2005). Introduction to Chemical Engineering Thermodynamics (7th ed.). McGraw-Hill.

Moran, M. J., Shapiro, H. N., Boettner, D. D., & Bailey, M. B. (2014). Fundamentals of Engineering Thermodynamics (8th ed.). Wiley.


RANSAC Regression

Random Sample Consensus (RANSAC) Regression

random sample consensus
RANSAC
robust regression
RANSAC Regresyonu

RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeatedly samples small subsets, fits a candidate model, and keeps the model that wins the largest consensus of agreeing points.

Random sample consensus (inlier-based fit)

Fischler, M. A. & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24(6), 381-395.

Torr, P. H. S. & Zisserman, A. (2000). MLESAC: A New Robust Estimator with Application to Estimating Image Geometry. Computer Vision and Image Understanding, 78(1), 138-156.

theil-sen-estimator least-trimmed-squares ols-regression quantile-regression robust-covariance


Rapid Review Methodology

Rapid Review (Accelerated Systematic Review)

Rapid Evidence Synthesis
Expedited Review
Fast-Track Systematic Review

Khangura et al. (2012), Codified by Cochrane Rapid Reviews (2020)

A rapid review is a systematic synthesis method that accelerates the evidence review process by streamlining or omitting certain systematic review steps while maintaining transparent, reproducible methodology. Pioneered by Khangura et al. (2012) and codified by the Cochrane Collaboration (2020), rapid reviews answer urgent policy or clinical questions in weeks to months rather than 12-18 months required by full systematic reviews. Methodological shortcuts—such as single screening of borderline studies, abbreviated search strategies, or limiting study designs—trades some rigor for speed. Rapid reviews are increasingly vital in responding to public health emergencies (pandemics, environmental crises) and evolving clinical practice questions where waiting for a full systematic review is not feasible.

Garritty, C., Gartlehner, G., Nussbaumer-Streit, B., et al. (2021). Cochrane Rapid Reviews interim guidance on methodological considerations for expedited reviews of interventions. Journal of Clinical Epidemiology, 130, 13–21.

Tricco, A. C., Antony, J., Zarin, W., et al. (2015). A scoping review of rapid review methods. BMC Medicine, 13, 224.

Khangura, S., Konnyu, K., Cushman, R., Grimshaw, J., & Moher, D. (2012). Evidence summaries: The evolution of a rapid review approach. Systematic Reviews, 1, 10.

systematic-review scoping-review-methodology evidence-synthesis-framework rapid-evidence-assessment


Rapid Review

rapid evidence review
accelerated systematic review
rapid evidence assessment
REA

2000s (rapidly adopted after 2005; Cochrane guidance 2020–2021)

Developed and formalised by health technology assessment agencies and the Cochrane Collaboration

A rapid review is a streamlined form of systematic review that deliberately simplifies or omits certain steps — such as dual screening, exhaustive grey-literature search, or full risk-of-bias assessment — in order to deliver timely, policy-relevant evidence synthesis within weeks rather than years. It is increasingly used by health agencies, governments, and organisations facing urgent decision-making needs where a full systematic review is not feasible within the available time and resources.

Published studies, systematic reviews, trial reports, grey literature

Garritty, C., Gartlehner, G., Nussbaumer-Streit, B., King, V. J., Hamel, C., Kamel, C., Affengruber, L., & Stevens, A. (2021). Cochrane Rapid Reviews Methods Group offers evidence-informed guidance to conduct rapid reviews. Journal of Clinical Epidemiology, 130, 13–22.

Tricco, A. C., Antony, J., Zarin, W., Strifler, L., Ghassemi, M., Ivory, J., Perrier, L., Hutton, B., Moher, D., & Straus, S. E. (2015). A scoping review of rapid review methods. BMC Medicine, 13, 224.

systematic-literature-review scoping-review umbrella-review narrative-review meta-analysis prisma-based-review


Routine Assessment of Patient Index Data 3 for Rheumatoid Arthritis

RAPID3 is a patient-reported outcome (PRO) measure of rheumatoid arthritis disease activity based on three simple self-report items: patient-counted swollen and tender joints and overall health assessment. Introduced by Pincus et al. in 2008, RAPID3 was designed for primary care and busy practices where joint examination is impractical or time-limited. Remarkably, RAPID3 correlates strongly with clinician-examined composite measures (DAS28, CDAI, SDAI) and predicts long-term radiographic progression equally well, making it a practical alternative for resource-limited settings and self-directed monitoring.

Pincus T, Bergman MJ, Sokka T, Roth SH, Swearingen C, Yazici Y. Activity of rheumatoid arthritis is similar in patients seen in a primary care physician-based practice and in an academic rheumatology-based practice. Arthritis Care Research. 2008;59(9):1229-1236.

Pincus T, Chung CP, Segurado OG. RAPID3 (Routine Assessment of Patient Index Data 3), a rheumatology outpatient clinical tool: discrimination of activity of disease. Semin Arthritis Rheum. 2010;40(2):89-96.

das28 cdai-rheumatoid-arthritis sdai-rheumatoid-arthritis basdai sledai


Rapidly-Exploring Random Tree

RRT
Incremental Sampling-based Algorithm

The Rapidly-Exploring Random Tree (RRT) is a motion planning algorithm that builds a tree of feasible paths by iteratively sampling random configurations in the workspace and connecting them to the nearest existing node in the tree. Introduced by LaValle in 1998, RRT is a breakthrough for high-dimensional motion planning, enabling robots to find collision-free paths in complex environments with obstacles, joint limits, and kinematic constraints.

LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical Report TR 98-11, Iowa State University.

Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. International Journal of Robotics Research, 30(7), 846-894.

LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press.


RAPS

RAPS (Ranking of Alternatives based on Preference Strength) is a ranking multi-criteria decision-making (MCDM) method introduced by Dezert, J., Tchamova, A. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Preference-strength aggregation (rank-reversal resistant)

Dezert, J., Tchamova, A. (2021). On the effectiveness of measures of uncertainty of basic belief assignments. Information & Security: An International Journal

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Rasch Model

Rasch Model (One-Parameter Logistic Item Response Theory)

1PL IRT
one-parameter logistic model
Rasch Modeli — 1PL IRT
1PL model

The Rasch model, introduced by Georg Rasch in 1960, is the simplest member of the Item Response Theory (IRT) family. It assigns a single difficulty parameter to each test item and places both item difficulties and person abilities on the same logit scale, enabling direct, sample-independent comparison of items and persons.

Item Response Theory / Latent trait model

Item difficulty parameters + person ability estimates on a common logit scale

Binary (0/1) or ordinal polytomous scored responses

Rasch, G. (1960). Probabilistic Models for Some Intelligence and Attainment Tests. Danish Institute for Educational Research, Copenhagen.

Bond, T. G. & Fox, C. M. (2015). Applying the Rasch Model: Fundamental Measurement in the Human Sciences (3rd ed.). Routledge.

two-pl-irt three-pl-irt graded-response-model exploratory-factor-analysis confirmatory-factor-analysis cronbach-alpha


RAT

RAT (Reference Alternative based Aggregation Technique) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Rank aggregation (signum-weighted Euclidean-square-difference)

Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları


Rate of Force Development

Rate of Force Development and Explosive Strength Assessment

RFD
explosive strength
force development rate
strength impulse

Rate of force development (RFD) is the speed at which force is produced during the initial phase of muscle contraction, typically expressed as the slope of the force-time curve in the first 50, 100, or 200 milliseconds of isometric contraction. Introduced comprehensively by Aagaard and colleagues (2002), RFD is a measure of explosive strength capacity and neural drive efficiency. Unlike maximal voluntary strength (which captures peak force), RFD captures how quickly an athlete can generate that force—a critical quality in sports requiring rapid, explosive movements (sprinting starts, jumping, tackling). RFD improves dramatically with strength training, reflecting increased motor unit recruitment rate and firing frequency.

Aagaard, P., Simonsen, E. B., Andersen, J. L., Magnusson, P., & Dyhre-Poulsen, P. (2002). Increased rate of force development and neural drive of human skeletal muscle following resistance training. Journal of Applied Physiology, 93(3), 1318-1326.

Viitasalo, J. T., & Bosco, C. (1982). Electromechanical behaviour of human muscles in vertical jumps. European Journal of Applied Physiology, 48(2), 253-262.

Haff, G. G., Carlock, J. M., Hartman, M. J., Kilgore, J. L., Kawamori, N., Jackson, J. K., ... & Stone, M. H. (2005). Force-time dependent characteristics of dynamic and isometric muscle actions. Journal of Strength and Conditioning Research, 19(2), 269-279.

force-velocity-profile counter-movement-jump reactive-strength-index electromechanical-delay 1rm-estimation


RAWEC

RAWEC (Ranking Alternatives With Equal Criteria weights) is a ranking multi-criteria decision-making (MCDM) method introduced by Puška, A., Štilić, A., Pamučar, D., Božanić, D., Nedeljković, M. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Rank-based equal-weight aggregation with distance from ideal

Puška, A., Štilić, A., Pamučar, D., Božanić, D., Nedeljković, M. (2024). Introducing a Novel multi-criteria Ranking of Alternatives with Weights of Criterion (RAWEC) model. MethodsX

siwec ahp anp bwm bwm-bayesian ccsd cilos cimas


Ray Tracing Propagation

deterministic propagation
site-specific modeling

Ray tracing is a deterministic propagation modeling technique for predicting electromagnetic field strength at specific locations. Instead of empirical formulas (like Okumura-Hata), ray tracing traces paths of electromagnetic energy as it reflects, diffracts, and scatters off buildings and terrain. With accurate 3D geometry and material properties, ray tracing predicts site-specific path loss, multipath delay profiles, and angle of arrival, making it ideal for detailed coverage planning, interference analysis, and system design. Ray tracing is now standard in professional cellular planning tools.

Maciel, T. F., Bertoni, H. L., & Xia, H. H. (1993). Unified approach to prediction of propagation over buildings for all ranges of frequencies. IEEE Transactions on Vehicular Technology, 42(1), 41-45.

Saleh, A. A., & Valenzuela, R. A. (1987). A statistical model for indoor multipath propagation. IEEE Journal on Selected Areas in Communications, 5(2), 128-137.

okumura-hata-model ofdm mimo zf-mmse-equalization


RCWA

RCWA method
coupled-wave method
diffraction grating analysis

Rigorous Coupled-Wave Analysis is a semi-analytical computational method for solving Maxwell's equations in periodic structures such as diffraction gratings and photonic crystals. Developed by Moharam and Gaylord in 1981, RCWA expands the electromagnetic fields in each periodic region into Fourier series and couples the fields at interfaces, enabling accurate and efficient simulation of light diffraction, resonances, and wave propagation in structured media.

Moharam, M. G., & Gaylord, T. K. (1981). Rigorous coupled-wave analysis of planar-grating diffraction. Journal of the Optical Society of America, 71(7), 811-818.

Gaylord, T. K., & Moharam, M. G. (1985). Analysis and applications of optical diffraction by gratings. Proceedings of the IEEE, 73(5), 894-937.

Li, L. (1997). Use of Fourier series in the analysis of discontinuous periodic structures. Journal of the Optical Society of America, 14(11), 2758-2767.


Regression Discontinuity Design

RDD
regression discontinuity
sharp regression discontinuity
Regresyon Süreksizliği Tasarımı (RDD)

Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik

Regression Discontinuity Design is a quasi-experimental method that estimates a local causal effect around a threshold (cutoff) value, comparing units just below and just above the cutoff as if they were almost randomly assigned. It is the design developed for applied practice by Imbens and Lemieux (2008) and by Lee and Lemieux (2010).

Local treatment effect at the cutoff (local polynomial regression)

Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.

Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.

Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.

ols-regression panel-fixed-effects instrumental-variables difference-in-differences propensity-score-matching


RDE Koutecky-Levich

Rotating Disk Electrode and Koutecky-Levich Analysis

RDE
rotating disk electrode
Koutecky-Levich

Rotating Disk Electrode (RDE) electrochemistry combined with Koutecky-Levich analysis is a powerful electrochemical technique that decouples diffusion-limited and kinetically limited electron-transfer processes. Developed by Levich in the 1960s, RDE enables determination of heterogeneous electron-transfer rate constants and mechanistic information by rotating an electrode to control mass transport.

Levich, V. G. (1962). Physicochemical Hydrodynamics. Prentice Hall.

Bard, A. J., & Faulkner, L. R. (2001). Electrochemical Methods: Fundamentals and Applications. John Wiley & Sons, 2nd edition.


RE-AIM Framework

RE-AIM: Reach, Effectiveness, Adoption, Implementation, Maintenance—A Five-Dimension Evaluation Framework for Implementation Science

Glasgow, R. E., Vogt, T. M., and colleagues

The RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance) is a five-dimension evaluation tool designed to assess the public health impact of evidence-based interventions in real-world settings. Developed by Glasgow et al. (1999) to address the gap between efficacy trials (controlled conditions) and effectiveness in practice, RE-AIM provides a comprehensive set of metrics to determine whether an intervention is 'worth it' from both scientific and practical perspectives. It has become the standard framework for evaluating implementation success across health domains.

Glasgow, R. E., Vogt, T. M., & Boles, S. M. (1999). Evaluating the public health impact of health promotion interventions: The RE-AIM framework. American Journal of Public Health, 89(9), 1322-1327.

Glasgow, R. E., Lichtenstein, E., & Marcus, A. C. (2003). Why don't we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. American Journal of Public Health, 93(8), 1261-1267.

Dzewaltowski, D. A., Glasgow, R. E., Klesges, L. M., Estabrooks, P. A., & Felton, G. (2016). RE-AIM: Evidence-based standards and a web resource to improve translation of research into practice. Annals of Behavioral Medicine, 51(3), 395-402.

cfir-framework knowledge-translation implementation-outcome-taxonomy fidelity-assessment normalization-process-theory


Reactive Distillation

Reactive Distillation (Reaction-Separation Integration)

integrated distillation-reaction
reactive column
reaction with separation

Reactive distillation couples reaction and separation in a single column, where reactants are separated from products continuously while simultaneously undergoing reaction on catalytic trays. Pioneered in the 1990s by Klaus Sundmacher and others, this process intensification technique dramatically reduces capital cost, energy consumption, and environmental impact for suitable reactions. It is now industrially proven for esterification, hydration, and transesterification processes.

Integrated reaction-separation process model

Sundmacher, K., & Kienle, A. (2003). Reactive Distillation: Status and Future Directions. Wiley-VCH.

Siringi, S., & Malone, M. F. (1997). Design of reaction/distillation columns for esterification. Computers & Chemical Engineering, 21(12), 1223-1238.

James, M. J., Baur, R., & Krishna, R. (2000). Models for reactive distillation. AIChE Journal, 46(12), 2350-2365.

cstr-model pfr-model pinch-analysis unifac


Reactive Power Compensation

Reactive Power Compensation and Power Factor Correction

power factor correction
VAR compensation
reactive power management

Power system voltage and reactive power control

Electrical utilities and equipment manufacturers

Reactive power compensation adjusts the flow of reactive power (VARs) in electrical networks to support voltage profiles, reduce losses, and improve power factor. Methods include fixed capacitor banks, switched capacitors, synchronous condensers, and FACTS devices. Proper compensation is essential for maintaining voltage stability and minimizing energy losses in modern power systems.

Hingorani, N. G., & Gyugyi, L. (2000). Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems. IEEE Press.

IEEE Std 18-2012: IEEE Standard for Shunt Power Capacitors.

Dugan, R. C., McGranaghan, M. F., Santoso, S., & Beaty, H. W. (2012). Electrical Power Systems Quality (3rd ed.). McGraw-Hill.

power-flow-analysis harmonic-distortion-analysis power-quality-assessment smart-grid-state-estimation


Reactive Strength Index

Reactive Strength Index and Elastic Rebound Capacity

RSI
stretch-shortening cycle
elastic response

The reactive strength index (RSI) is a measure of lower-body reactive strength and elastic energy utilization, calculated as jump height divided by the contact time between landing from a drop and takeoff. Introduced by Bobbert and colleagues (1987), RSI quantifies the efficiency of the stretch-shortening cycle (SSC)—the ability to rapidly switch from eccentric (lengthening) to concentric (shortening) muscle contractions. High RSI indicates rapid, forceful engagement of muscles' elastic properties (tendons, contractile proteins) and is relevant in sports requiring rapid rebound (sprinting, jumping, rebounding). RSI is trainable and sensitive to plyometric training.

Bobbert, M. F., Huijing, P. A., & van Ingen Schenau, G. J. (1987). Drop jumping. II. The influence of dropping height on the biomechanics of takeoff after landing. Medicine & Science in Sports & Exercise, 19(4), 339-346.

Flanagan, E. P., & Comyns, T. M. (2008). The stretch-shortening cycle training in sport. Strength & Conditioning Journal, 30(6), 32-39.

Taube, W., Leukel, C., & Gollhofer, A. (2016). How neurons make us jump: the role of the motor cortex in stretch-shortening cycle movements. Exercise and Sport Sciences Reviews, 44(1), 4-11.


Reactor Kinetics

Reactor Kinetics and Power Transient Analysis

neutron kinetics
power transient modeling
reactor control analysis

Reactor kinetics is the study of neutron population dynamics in a reactor core, originating from Fermi's first controlled chain reaction in 1942. It models power changes in response to control rod movements, temperature feedback, and accidental transients using coupled differential equations accounting for prompt and delayed neutrons, to ensure safe operation, predict transient behavior, and design control systems.

Lamarsh, J. R. (1983). Introduction to Nuclear Engineering (2nd ed.). Addison-Wesley.

Keepin, G. R., Wimett, T. F., & Zeigler, R. L. (1965). Delayed Neutrons from Fissionable Isotopes of Uranium, Neptunium, and Plutonium. Physical Review, 107(4), 1044–1049.

neutron-transport-calculation criticality-safety-analysis radiation-dose-assessment nuclear-decay-analysis monte-carlo-neutron-particle


Readability Analysis

Readability Analysis (Readability Formula Scoring)

readability scoring
readability formulas
Flesch-Kincaid analysis
Okunabilirlik Analizi

Readability analysis measures how well a text suits its intended audience by applying established readability formulas such as Flesch-Kincaid and Gunning Fog. The modern formula family was derived by Kincaid and colleagues in 1975, and it turns prose into a single score or target reading-grade level that signals how easy the text is to read.

Flesch-Kincaid, Gunning Fog, and related indices

Output: Readability score / target reading-grade level

Kincaid, J.P., Fishburne, R.P., Rogers, R.L. & Chissom, B.S. (1975). Derivation of New Readability Formulas for Navy Enlisted Personnel. Naval Technical Training Command.

DuBay, W.H. (2004). The Principles of Readability. Impact Information.


RCQ

The RCQ is a 12-item self-report instrument designed to assess an individual's stage of change motivation regarding substance use, particularly alcohol use. Developed by Rollnick and colleagues in 1992, it operationalizes the Transtheoretical Model of Change by measuring readiness across the precontemplation, contemplation, and action stages. The RCQ is a brief, cost-effective tool for identifying individuals who are ready to engage in behavior change and for tailoring the intensity and timing of intervention.

Rollnick, S., Heather, N., Gold, R., & Hall, W. (1992). Development of a short 'Readiness to Change' questionnaire for use in brief, opportunistic interventions among excessive drinkers. British Journal of Addiction, 87(5), 743–754.

dudit sadq cudit brief-addiction-monitor


Motivation for Reading Questionnaire

The Motivation for Reading Questionnaire (MRQ) is a self-report instrument assessing students' motivation to read and engagement with reading activities. Developed by Wigfield and Guthrie (2000), it measures both intrinsic motivation (reading for enjoyment and understanding) and extrinsic motivation (reading for grades or to avoid punishment), along with beliefs about reading efficacy and value. Reading motivation is a primary predictor of reading comprehension, frequency, and lifetime literacy engagement.

Wigfield, A., & Guthrie, J. T. (2000). Engagement and motivation in reading. In M. L. Kamil, P. B. Mosenthal, P. D. Pearson, & R. Barr (Eds.), Handbook of Reading Research (Vol. 3, pp. 403-422). Lawrence Erlbaum Associates.

Guthrie, J. T., Wiggins, A. P., & Perencevich, K. C. (2000). Scaffolding for engagement in elementary school reading instruction. Journal of Educational Research, 93(5), 310-319.

academic-motivation-scale study-process-questionnaire student-engagement-scale course-experience-questionnaire


Real Business Cycle Model

The Real Business Cycle (RBC) model, developed by Finn Kydland and Edward Prescott in 1982, is a dynamic stochastic general equilibrium framework that explains macroeconomic fluctuations as rational responses to exogenous technological shocks. Unlike Keynesian models that emphasize demand-side factors and nominal rigidities, the RBC model shows how productivity variations alone can generate business cycles that mimic observed employment, output, and investment dynamics.

Dynamic stochastic general equilibrium model

Kydland, F. E., & Prescott, E. C. (1982). Time to Build and Aggregate Fluctuations. Econometrica, 50(6), 1345–1370.

Prescott, E. C. (1986). Theory Ahead of Business Cycle Measurement. Carnegie-Rochester Conference Series on Public Policy, 25, 11–44.

Long, J. B., & Plosser, C. I. (1983). Real Business Cycles. Journal of Political Economy, 91(1), 39–69.


Real-World Evidence Studies

Real-World Evidence (RWE) and Real-World Data (RWD) Collection

real-world evidence
RWE
RWD
effectiveness research
observational evidence

FDA, EMA, and health agencies; Sherman et al. (2016) defined RWE formally

Real-World Evidence (RWE) is clinical evidence derived from Real-World Data (RWD)—data routinely collected in clinical practice from electronic health records, insurance claims, patient registries, and other healthcare sources. Formalized by the FDA in 2016 (Sherman et al.), RWE addresses a critical gap: while randomized trials test drugs under ideal conditions, RWE evaluates how treatments actually work in diverse, real patients with comorbidities, competing medications, and varied adherence. RWE complements (not replaces) trial evidence, accelerating regulatory decision-making and supporting post-market surveillance.

Sherman, R. E., Anderson, S. A., Dal Pan, G. J., Gray, G. W., Gross, T., Hunter, N. L., ... & Califf, R. M. (2016). Real-world evidence—what is it and what can it tell us? New England Journal of Medicine, 375(23), 2293–2297.

Levitan, B., Chan, E. W., Doshi, J. A., Hines, P., Komattireddy, H., Sanchez, R., & Sheridan, S. (2018). Collaboration and competition between real-world data and clinical trials. Therapeutic Innovation & Regulatory Science, 52(2), 172–176.

FDA (2021). Strengthening Our National Strategy on Adaptive Learning Systems for Health Care Quality and Safety: Report to Congress. US Food and Drug Administration.

registry-based-research pragmatic-clinical-trial cohort-study-design electronic-health-records claims-data-research


Realist Synthesis

Realist Synthesis (Context-Mechanism-Outcome Framework)

Realist Review
CMO Configuration
Mechanism-Based Synthesis

Realist synthesis is a theory-driven, interpretive method for evidence synthesis developed by Ray Pawson (2005) that focuses on understanding HOW and WHY interventions work, rather than WHETHER they work. Grounded in realist philosophy, realist synthesis examines Context-Mechanism-Outcome (CMO) configurations: how specific contextual conditions activate mechanisms that produce outcomes. Unlike traditional systematic reviews, which typically answer 'Does intervention X reduce outcome Y?', realist synthesis asks 'Under what conditions, through what mechanisms, for which populations does X work?' This approach is particularly valuable for complex interventions (policies, programs, multi-component treatments) where effectiveness varies dramatically across contexts, and for understanding why interventions succeed in some settings but fail in others.

Pawson, R., Greenhalgh, T., Harvey, G., & Walshe, K. (2005). Realist review—a new method of systematic review designed for complex policy and programme evaluation. Journal of Health Services Research & Policy, 10(S1), 21–35.

Pawson, R. (2013). The Science of Evaluation: A Realist Manifesto. SAGE Publications.

Wong, G., Westhorp, G., Pawson, R., & Greenhalgh, T. (2013). Realist synthesis: Introduction and some practical guidance. Cochrane Database of Systematic Reviews, 12, CD012032.

systematic-review qualitative-meta-synthesis logic-model-framework evidence-synthesis-framework implementation-science


Realized Volatility

Realized Volatility and the Heterogeneous Autoregressive (HAR) Model

heterogeneous autoregressive model of realized volatility

Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)

Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction.

Time-series regression of realized variance

OLS on daily, weekly, and monthly realized-volatility components

continuous (realized variance / volatility)

Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.

Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and Forecasting Realized Volatility. Econometrica, 71(2), 579-625.

egarch arima stochastic-volatility-model long-memory-models johansen-cointegration


Recall (Sensitivity)

Recall or Sensitivity (True Positive Rate)

Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.

Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.

precision f1-score specificity balanced-accuracy matthews-correlation-coefficient


Receiver Function Analysis

Receiver Function (RF) analysis is a seismic method that isolates P-to-S wave conversions at crustal and mantle discontinuities using teleseismic records from distant earthquakes. Introduced by Langston in 1979, RF analysis provides a cost-effective way to determine crustal thickness, Poisson's ratio, and upper mantle structure without requiring active seismic sources, making it a workhorse technique in crustal and lithospheric studies.

Teleseismic body wave analysis for subsurface imaging

Langston, C. A. (1979). Structure under Mount Rainier, Washington, inferred from teleseismic body waves. Journal of Geophysical Research, 84(B9), 4749-4762.

Ammon, C. J., Randall, G. E., & Zandt, G. (1990). On the nonlinear absolute amplitude calibration of a broadband seismometer: Theory and application to SRO and ASRO data. Seismological Research Letters, 61(2), 72-86.


Reception Analysis

Audience Reception and Interpretation Analysis

reception studies
audience analysis
reception theory

Reception Analysis is a methodological approach to studying media that focuses on how audiences actively interpret, engage with, and create meanings from media content rather than passively consuming predetermined messages. Developed from literary reception aesthetics and adapted to media studies by scholars like Stuart Hall, Ien Ang, and David Morley, the method examines the gap between what media texts 'offer' and what audiences actually make of them. Recognition that the same media content can be understood very differently by different viewers or readers revolutionized media studies, shifting focus from textual analysis alone to investigating the social, cultural, and personal contexts shaping interpretation.

Method for investigating how audiences actively interpret media content and create meanings

Jauss, H. R. (1982). Toward an Aesthetic of Reception (T. Bahti, Trans.). University of Minnesota Press.

Hall, S. (1973). Encoding and decoding in television discourse. In S. Hall et al. (Eds.), Culture, Media, Language (pp. 507-517). Hutchinson.

Ang, I. (1985). Watching Dallas: Soap Opera and the Melodramatic Imagination. Methuen.

Morley, D. (1986). Family Television: Cultural Power and Domestic Leisure. Comedia.

media-framing-analysis discourse-analysis-media film-narrative-analysis visual-content-analysis agenda-setting-analysis


Recovery Assessment Scale

Corrigan, P. W., Salzer, M. S., Ralph, R. O., et al.

The Recovery Assessment Scale (RAS) is a 41-item self-report measure designed to assess personal recovery in individuals with serious mental illness. Developed by Corrigan and colleagues in 2004, it captures the subjective and multidimensional nature of recovery, including hope, autonomy, goal achievement, and symptom management. The RAS is widely used in psychiatric rehabilitation research and clinical practice.

Corrigan, P. W., Salzer, M. S., Ralph, R. O., Sangster, Y., & Keck, L. (2004). Examining the factor structure of the Recovery Assessment Scale. Psychiatric Services, 55(7), 779-784.


Recovery Experience Questionnaire

The Recovery Experience Questionnaire (REQ) is an assessment tool measuring the quality and dimensions of off-work recovery from occupational stress. Developed by Sonnentag and Fritz in 2007, the REQ evaluates four key recovery experiences: psychological detachment from work, relaxation, mastery, and control. The instrument is grounded in conservation of resources theory and provides insights into how employees restore wellbeing during non-work time, which is crucial for preventing burnout and maintaining work engagement.

Sonnentag, S., & Fritz, C. (2007). The Recovery Experience Questionnaire: Development and validation of a measure for assessing recuperation and unwinding from work. Journal of Occupational Health Psychology, 12(3), 204-221.

copenhagen-burnout-inventory oldenburg-burnout-inventory effort-reward-imbalance-scale areas-of-worklife-scale presenteeism-scale


Recovery-Oriented Practices Index

The Recovery-Oriented Practices Index (ROPI) is a measure assessing the degree to which mental health services and programs embody recovery-oriented principles and practices. Developed by Sanja P. Barbic, Trevor Krupa, and Inge Armstrong in 2009, the ROPI evaluates whether services prioritize consumer choice, hope, autonomy, social participation, peer support, and community integration—the hallmarks of recovery-oriented mental health care. The ROPI is used to assess and guide the transformation of mental health services from a traditional medical/deficit model toward a recovery-oriented, consumer-centered approach.

Service- and consumer-report questionnaire

Barbic, S. P., Krupa, T., & Armstrong, I. (2009). A framework for the development of recovery-oriented mental health services and citizenship. American Journal of Psychiatric Rehabilitation, 12(3), 186-194.

recovery-assessment-scale empowerment-scale-rogers personal-recovery-questionnaire mental-health-recovery-measure


Recrystallization

Recrystallization for Compound Purification

Recrystallization is a classical purification technique in which a solid compound is dissolved in hot solvent, then allowed to crystallize upon cooling, yielding pure crystals while impurities remain in solution. Practiced for centuries in chemistry laboratories, recrystallization remains one of the most effective and accessible methods for purifying organic solids, especially when the target compound has low solubility at low temperatures.

Pavia, D. L., Lampman, G. M., Kriz, G. S., & Engel, R. G. (2014). A Small-Scale Approach to Organic Laboratory Techniques (4th ed.). Cengage Learning.

Still, W. C., Kahn, M., & Mitra, A. (1978). Rapid chromatographic purification based on solvent-induced density differences and easy detection. The Journal of Organic Chemistry, 43(14), 2923–2925.


Recurrence Quantification Analysis

RQA
Recurrence Plot Analysis
Nonlinear Recurrence Analysis
Tekrarlama Kantifikasyon Analizi

Recurrence Quantification Analysis (RQA) is a nonlinear method for characterizing the dynamics of a time series by quantifying the small-scale structure of its recurrence plot. Introduced in its modern, comprehensive form by Marwan, Romano, Thiel, and Kurths in 2007, RQA extracts scalar measures — such as recurrence rate, determinism, laminarity, and Shannon entropy — that capture periodicity, chaos, stationarity, and transitions in complex dynamical systems.

Output: Scalar recurrence statistics (RR, DET, LAM, ENTR, etc.)

Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5–6), 237–329.


Recurrent Event Model

Tekrarlayan Olay Modeli (Recurrent Events)

Andersen & Gill (AG, 1982); Prentice, Williams & Peterson (PWP, 1981); Wei, Lin & Weissfeld (WLW, 1989)

A recurrent event model is a survival analysis extension, formalised through the landmark contributions of Prentice, Williams and Peterson (1981), Andersen and Gill (1982), and Wei, Lin and Weissfeld (1989), that models time-to-event data when the same event — such as a hospital readmission, disease relapse, or equipment failure — can occur multiple times in the same individual. The three principal frameworks are the Andersen-Gill (AG) model, the Prentice-Williams-Peterson (PWP) stratified model, and the Wei-Lin-Weissfeld (WLW) marginal model, each making different assumptions about within-subject dependence.

Semi-parametric hazard model for repeated events

Multiple events per subject, right-censoring, within-subject correlation

Longitudinal (counting-process format: start, stop, event)

Andersen-Gill (AG), Prentice-Williams-Peterson (PWP), Wei-Lin-Weissfeld (WLW)

Cook, R.J. & Lawless, J.F. (2007). The Statistical Analysis of Recurrent Events. Springer.

Amorim, L.D.A.F. & Cai, J. (2015). Modelling Recurrent Events: A Tutorial for Analysis in Epidemiology. International Journal of Epidemiology, 44(1), 324–333.

cox-ph kaplan-meier frailty-model fine-gray-model negative-binomial-regression poisson-regression


Recurrent Neural Network

RNN
Elman network
Jordan network
simple recurrent network

A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.

Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.

long-short-term-memory gated-recurrent-unit transformer convolutional-neural-network bert-based-classification


Redox Reaction Mechanism Analysis

redox mechanism
electron transfer mechanism
oxidation-reduction

Redox reaction mechanism analysis is the systematic study of electron transfer pathways in oxidation-reduction reactions. Formalized by Rudolph Marcus in the 1950s (earning him the Nobel Prize in 1992), this framework explains how electrons move between reactants, what factors control reaction rates, and how electronic and geometric factors influence the ease of electron transfer.

Marcus, R. A. (1956). On the theory of oxidation-reduction reactions involving electron transfer. I. The Journal of Chemical Physics, 24(5), 966–978.

Atkins, P., & de Paula, J. (2010). Physical Chemistry (9th ed.). Oxford University Press.


Redundancy Analysis

Redundancy Analysis (RDA) is a multivariate technique developed by van den Wollenberg (1977) that combines multiple regression and principal component analysis. RDA finds linear combinations of predictor variables that best predict variation in response variables, making it ideal for understanding how sets of predictors collectively explain multivariate outcomes.

van den Wollenberg, A. L. (1977). Redundancy analysis: An alternative for canonical correlation analysis. Psychometrika, 42(2), 207-219.

Legendre, P., & Legendre, L. (1998). Numerical Ecology (2nd ed.). Elsevier.

Knudsen, S., Andersen, T., & Hansen, J. (2007). Redundancy analysis of multivariate data using PLS. Chemometrics and Intelligent Laboratory Systems, 87(2), 264-272.


RPQ

Reflection Questionnaire
Reflective Learning Scale

The RPQ is a self-report instrument measuring the degree to which healthcare students and professionals engage in reflective practice—the deliberate examination of their clinical experiences, decisions, and actions to extract learning and improve future practice. Developed by Sobral and refined by Saarikoski and colleagues in the early 2000s, the RPQ assesses both the frequency and depth of reflection, including reflection-in-action (thinking while performing) and reflection-on-action (analyzing after the fact). The scale is used in nursing and medical education to evaluate whether students are developing critical thinking and self-directed learning habits that sustain professional growth.

Sobral, D. T. (2000). An appraisal of medical students' reflection-in-action. Med Educ 34(3): 182–187.

Saarikoski, M., Kontio, E., Kauhanen, L., & Leino-Kilpi, H. (2005). Competence in intensive and critical care nursing: A literature review. Intensive Crit Care Nurs 21(1): 21–34.


Reflexive Thematic Analysis

Reflexive Thematic Analysis (Braun & Clarke)

RTA
reflexive TA
Braun and Clarke thematic analysis
qualitative thematic analysis

2006 (seminal paper); explicitly named 'reflexive' from ~2019

Reflexive Thematic Analysis (RTA) is a widely used qualitative method for identifying, analysing, and interpreting patterns of shared meaning — called themes — across a dataset. Developed by Virginia Braun and Victoria Clarke, it is theoretically flexible, works across epistemological positions, and foregrounds the researcher's active, interpretive role rather than treating themes as features that simply emerge from data. It differs from older 'codebook' approaches by treating the analyst's subjectivity as a resource rather than a source of bias to be suppressed.

Text data — interviews, focus groups, open-ended survey responses, documents

No fixed rule; commonly 6–30 participants for interview data

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.

thematic-analysis content-analysis phenomenology grounded-theory narrative-analysis discourse-analysis


Reflexivity in Qualitative Research

Reflexive Examination of Researcher Positionality and Influence

reflexive practice
researcher reflexivity
positionality
reflective practice

Reflexivity is the practice of examining how the researcher's identity, assumptions, relationships, and values influence the research process and findings. Rather than treating objectivity as achievable detachment, reflexivity acknowledges that the researcher is embedded within the research and cannot be fully separated from it. Originating in sociology and anthropology, reflexivity has become central to qualitative research rigor across disciplines. Reflexive researchers critically examine their own influence at each stage: research design, participant recruitment, data collection, interpretation, and presentation. This transparency strengthens rigor by making visible the lens through which data are collected and interpreted.

Finlay, L. (2002). Outing the researcher: The provenance, process, and practice of reflexivity. Qualitative Health Research, 12(4), 531-545.

Johnson, R. B., & Christensen, L. (2017). Educational Research: Quantitative, Qualitative, and Mixed Approaches (6th ed.). SAGE Publications.

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589-597.

Malterud, K. (2001). Qualitative research: standards, challenges, and guidelines. The Lancet, 358(9280), 483-488.

in-depth-interview-method participant-observation qualitative-rigor-criteria member-checking


Reformer

Efficient Transformer
LSH Transformer
Locality-Sensitive Hashing Transformer
Verimli Dönüştürücü

Nikita Kitaev, Łukasz Kaiser & Anselm Levskaya

The Reformer is an efficient variant of the Transformer architecture introduced by Kitaev, Kaiser, and Levskaya at ICLR 2020. It addresses the prohibitive O(L²) memory and computational cost of standard self-attention for long sequences. The key innovations are locality-sensitive hashing (LSH) attention, which approximates full attention in O(L log L) time, and reversible residual layers that dramatically reduce activation memory during training.

Memory-efficient attention-based sequence model

Kitaev, N., Kaiser, Ł., & Levskaya, A. (2020). Reformer: The efficient transformer. ICLR.


Regime-Switching Model

Markov Regime-Switching Model for Financial Applications (Hamilton MS-AR)

Rejim Değiştirme Modeli — Finansal Uygulamalar (Hamilton MS)

The Markov regime-switching model, introduced by James D. Hamilton in 1989, is a hidden-state time-series model in which financial series such as returns or volatility behave with different parameters across distinct economic regimes (bull/bear or high/low volatility). It is the financial application of Hamilton's MS-AR model, where an unobserved Markov state governs which parameter set is active at each point in time.

Markov regime-switching time-series model

Maximum likelihood via the Hamilton filter

discrete regimes (e.g. bull/bear, high/low volatility)

Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.

Ang, A., & Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business & Economic Statistics, 20(2), 163-182.

garch-volatility arima-forecasting hidden-markov-model var-vector-autoregression ols-regression


REGIME

REGIME (Ordinal multi-criteria method based on pairwise regime analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Hinloopen, E., Nijkamp, P., Rietveld, P. in 1983. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pairwise regime matrix aggregation (ordinal weights)

Hinloopen, E., Nijkamp, P., Rietveld, P. (1983). Qualitative discrete multiple criteria choice models in regional planning. Regional Science and Urban Economics

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Regional Homogeneity

Regional Homogeneity (ReHo) is a measure of synchronization between a voxel and its spatial neighbors in resting-state fMRI. Introduced by Zang and colleagues in 2004, ReHo quantifies local within-cluster activity coherence, reflecting the degree to which brain regions exhibit synchronized spontaneous activity at rest.

Zang, Y. F., He, Y., Zhu, C. Z., et al. (2004). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development, 26(7), 429–439.

Yang, Y., Raine, A., Han, C. B., et al. (2007). Localizing brain abnormalities in ADHD: a meta-analysis of neuroimaging studies. Neuroscience & Biobehavioral Reviews, 31(4), 508–515.


Registry-Based Research

Clinical Registry-Based Observational Research

registry research
registry study
disease registry
registry-based cohort

Patient registries began mid-20th century; modern registry research formalized 2000s–2010s

Registry-based research uses systematically collected clinical data from patient registries—organized databases of patients with a specific disease or condition—to conduct observational studies. Registries began in the mid-20th century but have proliferated since the 2000s as electronic health records expanded and funding agencies recognized their value for real-world evidence generation. Registry studies provide large, diverse, representative populations without the cost of recruiting and following prospectively, enabling rapid generation of clinical evidence.

Gini, R., Francesconi, P., Mazzaglia, G., Brignoli, G., Cricelli, C., Lapi, F., & Cricelli, A. (2020). Chronic disease prevalence from Italian administrative databases: the PREVALENTIST study. BMC Public Health, 13, 191.

Hoque, D. M. E., Ruseckaite, R., Braithwaite, J., & Ting, H. P. (2017). Quality of life measurement in patients with Parkinson's disease: a systematic review of generic and disease-specific instruments and their clinimetric properties. Quality of Life Research, 26(8), 2117–2130.

Ikehara, S., Iso, H., Yamagishi, K., Yamagishi, K., Maruyama, K., & Inoue, M. (2016). Healthy lifestyle and life expectancy among Japanese adults: findings from the JPHC study. Journal of Epidemiology, 26(2), 88–97.

cohort-study-design real-world-evidence pragmatic-clinical-trial observational-study-design data-validation


Regression discontinuity design in education research

Regression Discontinuity Design in Education Research

RDD in education
education RD design
sharp RDD education
score-cutoff design

1960 (origination); 1999-2010 (education economics canon)

Thistlethwaite & Campbell (1960); popularized in education economics by Angrist & Lavy (1999), Lee & Lemieux (2010)

Regression discontinuity design (RDD) in education research exploits a score-based eligibility cutoff — such as a test score threshold, GPA requirement, or age cutoff — to estimate the causal effect of a program, intervention, or policy on student or school outcomes. Units just below and just above the cutoff are treated as near-randomly assigned, enabling credible causal inference without a randomized trial.

Continuous running variable with a score cutoff; outcome measured for units near the threshold

Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), 281-355.

Thistlethwaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology, 51(6), 309-317.

fuzzy-regression-discontinuity difference-in-differences propensity-score-matching instrumental-variables interrupted-time-series event-study-design


Regression Discontinuity

RDD
regression discontinuity design
sharp RDD
fuzzy RDD
Regresyon Süreksizliği (RDD)

Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)

Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.

Local average treatment effect (LATE) at the cutoff

Sharp or fuzzy discontinuity in a running variable

Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.

Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.

iv-2sls interrupted-time-series propensity-score-matching matching-methods ols-regression


Regression Kink Design

Generalized Regression Kink Design (RKD)

Regresyon Kırılma Tasarımı (RKD — Regression Kink Design)

The Regression Kink Design is a quasi-experimental method that estimates a causal effect when a policy rule creates a change in slope (a kink) — rather than a jump — at a known threshold of a running variable. It was formalised as a generalized design by Card, Lee, Pei and Weber (2015) and is the slope-based counterpart of the regression discontinuity design.

Quasi-experimental design (slope-based RDD)

Ratio of slope changes (first derivative) at the kink, local polynomial

sharp / fuzzy kink at a known policy threshold

Card, D., Lee, D. S., Pei, Z. & Weber, A. (2015). Inference on Causal Effects in a Generalized Regression Kink Design. Econometrica, 83(6), 2453-2483.

regression-discontinuity ols-regression instrumental-variables difference-in-differences local-linear-regression


Regression Splines

splines
cubic splines
natural splines
smoothing splines
B-spline regression
regresyon spline'ları

Spline regression literature; P-splines by Eilers & Marx

Regression splines model a nonlinear relationship by fitting piecewise polynomials that join smoothly at a set of points called knots. Cubic and natural splines are the most common, and smoothing splines add a roughness penalty that automatically balances fit against smoothness. Splines are the standard flexible building block for univariate nonlinear regression and the basis of generalized additive models.

Truncated power, B-spline, or natural-spline basis

Smooth nonlinear univariate relationships

Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer.

generalized-additive-model loess polynomial-regression mars


Regularized Boosting

Regularized Gradient Boosting (Shrinkage and Penalized Objective Boosting)

shrinkage boosting
penalized boosting
regularized gradient boosting
L1/L2 boosting

Friedman, J. H.; extended by Chen & Guestrin

Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks.

Regularized ensemble (boosting with shrinkage/penalty)

Tabular (continuous, categorical, binary, ordinal)

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.

boosting xgboost regularized-random-forest regularized-gradient-boosting gradient-boosting regularized-extra-trees


Regularized CatBoost

Regularized CatBoost (Categorical Boosting with Explicit Regularization)

CatBoost with regularization
regularized categorical boosting
CatBoost L2 regularization
penalized CatBoost

Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (Yandex Research)

Regularized CatBoost applies explicit regularization controls — L2 leaf regularization, tree depth constraints, shrinkage rate, and model size penalties — on top of CatBoost's ordered gradient boosting framework, reducing overfitting while retaining CatBoost's native handling of categorical features and its low prediction latency on tabular datasets.

Tabular data (numerical, categorical, mixed)

Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31.

Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.

catboost xgboost regularized-xgboost regularized-lightgbm gradient-boosting regularized-gradient-boosting


Regularized Decision Tree

Regularized Decision Tree (Pruned and Constrained CART)

pruned decision tree
cost-complexity pruned tree
penalized decision tree
constrained CART

A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.

Tabular (continuous and categorical features)

Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth.

Esposito, F., Malerba, D., & Semeraro, G. (1997). A comparative analysis of methods for pruning decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5), 476–491.

decision-tree random-forest regularized-random-forest extra-trees regularized-linear-regression boosting


Regularized Federated Learning

Regularized Federated Learning (Proximal and Penalty-Based Approaches)

FedProx
federated learning with regularization
proximal federated learning
penalized federated optimization

Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base)

Regularized federated learning extends the federated learning framework by adding penalty terms to each client's local objective, anchoring local updates closer to the global model. The canonical formulation — FedProx — adds a proximal term that controls how far any single client can drift, improving convergence and stability when client data distributions differ substantially.

Distributed optimization with regularization

Decentralized tabular, image, or text data across clients

Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated Optimization in Heterogeneous Networks. Proceedings of Machine Learning and Systems (MLSys), 2, 429–450.

McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54, 1273–1282.

federated-learning regularized-gradient-boosting semi-supervised-learning transfer-learning online-learning regularized-logistic-regression


Regularized Few-Shot Learning

Regularized Few-Shot Learning (Regularization-Enhanced Meta-Learning)

FSL with regularization
regularized meta-learning
few-shot learning with regularization
regularized episodic learning

Multiple (Chen et al., Tian et al., Snell et al., and others)

Regularized few-shot learning augments standard few-shot learning pipelines with explicit regularization mechanisms — such as weight decay, dropout, data augmentation, label smoothing, or manifold constraints — to reduce overfitting to the tiny support sets that define each episode. This produces more generalizable models when only one to thirty labeled examples per class are available.

Meta-learning framework with explicit regularization

Small labeled sets (few examples per class), large unlabeled or auxiliary data

Chen, W., Liu, Y., Kira, Z., Wang, Y. F., & Huang, J. (2019). A Closer Look at Few-Shot Classification. International Conference on Learning Representations (ICLR).

Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J. B., & Isola, P. (2020). Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? European Conference on Computer Vision (ECCV).

few-shot-learning meta-learning transfer-learning regularized-transfer-learning semi-supervised-few-shot-learning self-supervised-learning


Regularized Gaussian Mixture Model

Regularized Gaussian Mixture Model (Covariance-Regularized EM Clustering)

Regularized GMM
GMM with covariance regularization
stabilized Gaussian mixture model
penalized GMM

Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter)

A Regularized Gaussian Mixture Model (GMM) adds a small positive constant to the diagonal of each component covariance matrix during the Expectation-Maximization algorithm, preventing singular or near-singular matrices that cause numerical failures when the data are sparse, high-dimensional, or contain near-duplicate observations.

Probabilistic clustering with regularization

Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9). Springer.

gaussian-mixture-model k-means regularized-k-means bayesian-gaussian-mixture-model one-class-svm regularized-k-nearest-neighbors


Regularized Gaussian Process

Regularized Gaussian Process Regression and Classification

Regularized GP
GP with noise regularization
sparse regularized Gaussian process
regularized Gaussian process regression

2006 (canonical formulation); kernel regularization roots 1990s

A Regularized Gaussian Process (GP) is a probabilistic kernel-based model that places a prior over functions and explicitly controls overfitting through a noise regularization parameter — the observation noise variance — that prevents the model from memorizing training labels. It produces calibrated uncertainty estimates alongside predictions, making it uniquely suited to small or expensive datasets where knowing how confident the model is matters as much as the prediction itself.

Probabilistic kernel model with regularization

Continuous features; continuous or binary targets

Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.

Scholkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.

gaussian-process support-vector-machine regularized-support-vector-machine bayesian-gaussian-process kernel-ridge-regression regularized-linear-regression


Regularized Gradient Boosting

Regularized Gradient Boosting (L1/L2-Penalized Additive Tree Ensemble)

penalized gradient boosting
shrinkage-regularized boosting
XGBoost-style regularization
L1/L2 gradient boosting

2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)

Chen, T. & Guestrin, C. (building on Friedman, J. H.)

Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.

Regularized ensemble (additive tree model)

Tabular (continuous, categorical, binary, ordinal, count)

Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.

xgboost boosting gradient-boosting regularized-random-forest regularized-decision-tree lightgbm


Regularized k-means

sparse k-means
penalized k-means
regularized clustering
constrained k-means

Witten, D. M. & Tibshirani, R. (sparse k-means formulation)

Regularized k-means extends standard k-means by adding a penalty term — most commonly an L1 (lasso-type) or L2 constraint — to the objective function. This discourages degenerate cluster solutions and, in the sparse variant introduced by Witten and Tibshirani (2010), simultaneously selects the features that drive cluster separation, making it especially valuable in high-dimensional settings where many features are irrelevant.

Witten, D. M., & Tibshirani, R. (2010). A framework for feature selection in clustering. Journal of the American Statistical Association, 105(490), 713–726.

k-means regularized-gaussian-mixture-model sparse-pca regularized-dbscan gaussian-mixture-model regularized-hdbscan


Regularized k-nearest neighbors

Regularized k-Nearest Neighbors (Kernel-Weighted kNN)

regularized kNN
kernel-weighted kNN
distance-regularized nearest neighbors
kNN with regularization

Extends Cover & Hart (1967); regularization formulations developed through kernel smoothing literature

Regularized k-Nearest Neighbors (kNN) extends the classical nearest-neighbor algorithm by incorporating regularization mechanisms — most commonly kernel-based distance weighting or bandwidth control — that smooth predictions, reduce sensitivity to the choice of k, and lower variance. The result is a more stable and better-calibrated instance-based learner for classification and regression tasks on tabular data.

Instance-based / lazy learner with regularization

Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.

Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 13). Springer.

k-nearest-neighbors regularized-support-vector-machine regularized-gaussian-process gaussian-process support-vector-machine regularized-logistic-regression


Regularized LightGBM

Regularized Light Gradient Boosting Machine

LightGBM with L1/L2 regularization
penalized LightGBM
LightGBM ridge/lasso
regularized LGBM

Regularized LightGBM applies L1 (lasso) and L2 (ridge) penalty terms to the leaf weight objective of LightGBM — Microsoft's highly efficient gradient boosting framework — to control model complexity, reduce overfitting, and improve generalization on tabular classification and regression tasks with high-dimensional or noisy feature sets.

Tabular (numeric and categorical features)

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.

lightgbm xgboost regularized-xgboost gradient-boosting regularized-gradient-boosting catboost


Regularized linear regression

Regularized Linear Regression (Ridge, Lasso, Elastic Net)

Ridge regression
Lasso regression
Elastic Net regression
penalized regression

Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)

Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.

Continuous numeric features; continuous target

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.

Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 3). Springer.

linear-regression-ml logistic-regression-ml regularized-logistic-regression support-vector-machine elastic-net lasso


Regularized Logistic Regression

Regularized Logistic Regression (L1 / L2 / Elastic Net Penalized Binary and Multinomial Classification)

penalized logistic regression
L1 logistic regression
L2 logistic regression
elastic net logistic regression

Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)

Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.

Tabular, numerical and encoded categorical features with a binary or multinomial outcome

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 4, 18). Springer.

logistic-regression-ml regularized-linear-regression support-vector-machine linear-discriminant-analysis elastic-net naive-bayes


Regularized Naive Bayes

Smoothed Naive Bayes
Laplace-smoothed Naive Bayes
Regularized NB
Complement Naive Bayes

Good, I. J. (Laplace smoothing formalized); Rennie et al. (complement regularization)

Regularized Naive Bayes augments the classical Naive Bayes probabilistic classifier with explicit smoothing or shrinkage — most commonly Laplace (additive) smoothing — to prevent zero-probability estimates for unseen feature values and to reduce overfitting. The result is a fast, robust classifier that generalizes better than unsmoothed Naive Bayes, particularly on sparse or high-dimensional data such as text.

Probabilistic classifier with regularization

Categorical, text (bag-of-words), or continuous features

Rennie, J. D. M., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of Naive Bayes text classifiers. In Proceedings of the 20th International Conference on Machine Learning (ICML-2003), pp. 616–623.

naive-bayes logistic-regression regularized-logistic-regression support-vector-machine regularized-support-vector-machine k-nearest-neighbors


Regularized Online Learning

Regularized Online Learning (Online Learning with Regularization)

FTRL
Follow-the-Regularized-Leader
online regularized optimization
regularized dual averaging

Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.

Regularized online learning extends the online learning paradigm by incorporating a regularization penalty into each weight update, controlling model complexity while processing data one example at a time. Algorithms such as Follow-the-Regularized-Leader (FTRL) and Regularized Dual Averaging (RDA) make this approach practical at scale, enabling sparse, well-calibrated models on streaming data.

Online optimization framework with regularization

Sequential / streaming labeled or unlabeled tabular data

Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596.

Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.

online-learning regularized-linear-regression regularized-logistic-regression semi-supervised-learning transfer-learning stochastic-gradient-descent


Regularized random forest

RRF
Guided Regularized Random Forest
GRRF
regularized tree ensemble

Regularized Random Forest (RRF), introduced by Deng and Runger in 2012, extends the standard Random Forest by adding a penalty that discourages splits on features not already used in the ensemble. This built-in regularization produces sparser, less redundant feature subsets, making the model especially valuable when feature selection is as important as predictive accuracy.

Regularized ensemble (penalized feature selection in trees)

Tabular (continuous, categorical, mixed); labeled data

Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8.

Deng, H., & Runger, G. (2013). Gene selection with guided regularized random forest. Pattern Recognition, 46(12), 3483–3489.

random-forest extra-trees regularized-decision-tree regularized-gradient-boosting regularized-xgboost decision-tree


Regularized semi-supervised learning

Regularized Semi-Supervised Learning (Manifold Regularization and Graph-Based SSL)

manifold regularization
graph-regularized SSL
semi-supervised regularization
Laplacian regularization

Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data are scarce.

Labeled and unlabeled tabular, text, or graph-structured data

Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.

semi-supervised-learning regularized-logistic-regression gaussian-process label-propagation self-supervised-learning regularized-random-forest


Regularized Stacking Ensemble

Regularized Stacking Ensemble (Stacked Generalization with Regularized Meta-Learner)

regularized stacked generalization
ridge stacking
lasso meta-learner ensemble
penalized stacking

Wolpert, D. H. (stacking); Breiman, L. (regularized meta-learner formulation)

Regularized Stacking Ensemble is a two-level ensemble method in which predictions from multiple diverse base learners are combined by a regularized meta-learner — typically ridge regression, lasso, or elastic net — to suppress overfitting in the combination layer. Regularization ensures that the meta-learner assigns stable, well-calibrated weights to base model outputs rather than memorizing noise in the training fold predictions.

Ensemble (stacked generalization with regularized meta-learner)

Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.

Breiman, L. (1996). Stacked Regressions. Machine Learning, 24(1), 49–64.

stacking-ensemble voting-ensemble boosting regularized-random-forest regularized-gradient-boosting random-forest


Regularized Support Vector Machine

Regularized Support Vector Machine (L1/L2-penalized SVM)

Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)

Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings.

Regularized discriminative classifier / regressor

Numerical, binary or multi-class labels; SVR for continuous targets

Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

Zhu, J., Rosset, S., Tibshirani, R. & Hastie, T. (2004). 1-norm support vector machines. Advances in Neural Information Processing Systems (NIPS), 16.

support-vector-machine regularized-logistic-regression regularized-linear-regression kernel-svm lasso-regression linear-discriminant-analysis


Regularized Transfer Learning

Regularized Transfer Learning (Regularization-Constrained Domain Adaptation)

regularized domain adaptation
transfer learning with regularization
penalized transfer learning
regularized fine-tuning

Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors

Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels are scarce.

Regularized supervised/semi-supervised learning framework

Labeled source-domain data plus labeled or unlabeled target-domain data

Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

Li, Z., Nie, F., Chang, X., & Yang, Y. (2014). Beyond trace norm: Robust matrix recovery via bi-sparsity pursuit. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1736–1742.

transfer-learning regularized-logistic-regression regularized-random-forest semi-supervised-transfer-learning metric-learning few-shot-learning


Reinforcement Learning

Reinforcement Learning (Agent-Environment Reward Optimization)

RL
reward-based learning
trial-and-error learning
policy optimization

Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)

Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback.

State-action-reward trajectories (environment interactions)

Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.

Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533.

deep-q-network policy-gradient convolutional-neural-network recurrent-neural-network transformer multi-armed-bandit


Reintegration to Normal Living Index

Wood-Dauphinee, Opzoomer, Williams, Marchand, Spitzer

The Reintegration to Normal Living Index (RNLI) is a brief, patient-report measure designed to assess how completely a person has returned to 'normal' community living following a major health event (stroke, head injury, cardiac event, or other condition requiring significant recovery). Developed by Wood-Dauphinee and colleagues in the 1980s, RNLI captures the subjective experience of reintegration: the degree to which the person feels they have resumed their pre-illness social roles, activities, and independence.

Wood-Dauphinee, S. L., Opzoomer, M. A., Williams, J. I., Marchand, B., & Spitzer, W. O. (1988). Assessment of global function: a new measure for evaluating the outcome of rehabilitation of post-stroke patients. Archives of Physical Medicine and Rehabilitation, 69(7), 506–515.

Schubert, D. S., Buchsbaum, M. S., Orsulak, P. J., King, R. J., & Stoddard, G. (1992). Neuropsychological evidence for a defect of thalamic filtering in schizophrenia. Biological Psychiatry, 32(7), 556–567.

community-integration-questionnaire participation-scale impact-participation-autonomy assessment-life-habits craig-handicap-assessment


Rejection Sensitivity Questionnaire

Rejection Sensitivity Questionnaire (RSQ)

The RSQ is an 18-item self-report measure of rejection sensitivity—the disposition to anxiously expect, readily perceive, and intensely react to rejection from others. Developed by Downey and Feldman in 1996, it captures both anxiety about rejection and expectancy of rejection. Rejection sensitivity is recognized as transdiagnostic interpersonal vulnerability predicting social anxiety, depression, relationship conflict, and self-harm.

Downey, G., & Feldman, S. I. (1996). Implications of rejection sensitivity for intimate relationships. Journal of Personality and Social Psychology, 70(6), 1327–1343.


Relation Extraction

Relation Extraction (Semantic Relation Extraction)

semantic relation extraction
İlişki Çıkarma (Relation Extraction)

Relation extraction is a natural-language-processing task that detects and classifies the semantic relations that hold between entities mentioned in text. Building on early kernel-based methods (Zelenko and colleagues, 2003) and later neural matching approaches (Baldini Soares and colleagues, 2019), it turns free-form text into structured facts of the form entity–relation–entity.

Output: Classified semantic relations between entity pairs

Zelenko, D., Aone, C. & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106.

Soares, L. B., FitzGerald, N., Ling, J. & Kwiatkowski, T. (2019). Matching the Blanks: Distributional Similarity for Relation Learning. Proceedings of ACL 2019.

named-entity-recognition text-classification semantic-similarity keyword-extraction


Relational Survey

correlational survey
associational survey
relationship survey design
relational descriptive survey

Mid-20th century onward (systematised ~1960s–1990s)

Established in educational and social science research methodology; systematised by Fraenkel & Wallen and others

Relational survey research is a quantitative, non-experimental design that gathers structured self-report data from a sample and examines the statistical associations among two or more variables. Unlike purely descriptive surveys, which only characterise distributions, relational surveys ask whether and how strongly variables co-vary — providing evidence of relationships without manipulating conditions or establishing causation.

Quantitative non-experimental survey design

Numeric scores from questionnaires, scales, or standardised instruments

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill.

Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications.

correlational-research descriptive-research cross-sectional-research survey-research causal-comparative-research explanatory-research


Relationship Assessment Scale

RAS
Sternberg Relationship Assessment Scale

Susan Hendrick (based on Sternberg's Triangular Theory of Love)

The Relationship Assessment Scale is a brief, widely used instrument for measuring global relationship satisfaction and quality in romantic partnerships. Developed by Susan Hendrick in 1988 and based on Robert Sternberg's Triangular Theory of Love, the RAS measures the three core components of love: intimacy (emotional closeness and connection), passion (attraction and desire), and commitment (dedication and decision to maintain the relationship). The RAS is valued for its brevity (7 items), applicability across diverse relationship types, and strong psychometric properties.

Sternberg, R. J. (1988). Triangulating love. In R. J. Sternberg & M. L. Barnes (Eds.), The psychology of love (pp. 119-138). New Haven, CT: Yale University Press.

Hendrick, S. S. (1988). A generic measure of relationship satisfaction. Journal of Marriage and the Family, 50(1), 93-98.


Reliability Analysis

Life Data Analysis
Survival Analysis (Engineering)
Time-to-Failure Analysis
Güvenilirlik Analizi

Statistical reliability analysis models the time-to-failure of components, systems, or products using parametric lifetime distributions fitted to observed or censored failure data. Formalized comprehensively by William Q. Meeker and Luis A. Escobar in their 1998 Wiley monograph, the framework integrates maximum likelihood estimation, censoring mechanisms, and distributional diagnostics to produce probability-of-failure curves, hazard rates, and quantile estimates that support design, warranty, and maintenance decisions.

Output: Failure probability, hazard rate, quantiles

Meeker, W. Q., & Escobar, L. A. (1998). Statistical Methods for Reliability Data. Wiley.


Reliability Block Diagram

A Reliability Block Diagram (RBD) is a visual representation of a system's architecture that models how component reliabilities combine to determine overall system reliability. Each block represents a component or subsystem with a known reliability (probability of functioning without failure), and connections between blocks represent functional relationships (series, parallel, or mixed). RBD analysis is a fundamental tool in reliability engineering, used to identify critical components, optimize redundancy, and predict system-level failure rates.

Ebeling, C. E. (2010). An introduction to reliability and maintainability engineering (2nd ed.). Long Grove, IL: Waveland Press.

Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution (2nd ed.). Milwaukee: ASQ Quality Press.

total-productive-maintenance facility-layout assembly-line-balancing scor-model job-shop-scheduling


Everett L. Worthington Jr., Nathaniel G. Wade, Tamara L. Hight, Jennifer S. Ripley, Michael E. McCullough, & others

The Religious Commitment Inventory-10 (RCI-10), developed by Worthington and colleagues in 2003, is a brief 10-item self-report measure of religious commitment: the degree to which an individual dedicates themselves to religious beliefs, practices, and community. The RCI-10 distinguishes between two dimensions of commitment: Intrapersonal (personal faith conviction, spiritual discipline, religious significance) and Interpersonal (engagement with faith community, public religious identity, shared practices). It has become widely used in counseling psychology, pastoral care, and research on religiosity and well-being to assess the strength and breadth of religious dedication.

Worthington, E. L., Jr., Wade, N. G., Hight, T. L., Ripley, J. S., McCullough, M. E., Berry, J. W., ... Schmitt, M. M. (2003). The Religious Commitment Inventory-10: Development, refinement, and validation of a brief scale for research and counseling. Journal of Counseling Psychology, 50(1), 84–96.

intrinsic-extrinsic-religiosity duke-religion-index brief-religious-coping-scale daily-spiritual-experience-scale