A Practical Guide to Splines

SciPy 1.0: fundamental algorithms for scientific computing in Python

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430

Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields

Guckenheimer, J., & Holmes, P. (1983) · Applied Mathematical Sciences · doi:10.1007/978-1-4612-1140-2 · Cited by 16,144

SciPy 1.0: fundamental algorithms for scientific computing in Python

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460

Generalized additive models for location, scale and shape.

Rigby, R. A., & Stasinopoulos, D. M. (2005) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/j.1467-9876.2005.00510.x · Cited by 3,228

Recurrent Neural Networks for Multivariate Time Series with Missing Values

Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-24271-9 · Cited by 2,370

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

Helber, P., Bischke, B., Dengel, A., & Borth, D. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2918242 · Cited by 1,951

Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Main Galaxy Sample

Strauss, M. A., Weinberg, D. H., Lupton, R. H., Narayanan, V. K., Annis, J., Bernardi, M., Blanton, M., Burles, S., Connolly, A. J., Dalcanton, J., et al. (2002) · The Astronomical Journal · doi:10.1086/342343 · Cited by 1,909

Some aspects of the spline smoothing approach to non-parametric regression curve fitting

Silverman, B. W. (1985) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1985.tb01327.x · Cited by 1,117

Elastically deformable models

Terzopoulos, D., Platt, J., Barr, A., & Fleischer, K. (1987) · ACM SIGGRAPH Computer Graphics · doi:10.1145/37402.37427 · Cited by 1,110

Sex Differences in Brain Gray and White Matter in Healthy Young Adults: Correlations with Cognitive Performance

Gur, R. C., Turetsky, B. I., Matsui, M., Yan, M., Bilker, W., Hughett, P., & Gur, R. E. (1999) · The Journal of Neuroscience · doi:10.1523/jneurosci.19-10-04065.1999 · Cited by 909

A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans

Huppert, T. J., Hoge, R. D., Diamond, S. G., Franceschini, M. A., & Boas, D. A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.08.065 · Cited by 846

On the use of generalized additive models in time-series studies of air pollution and health

Dominici, F. (2002) · American Journal of Epidemiology · doi:10.1093/aje/kwf062 · Cited by 728

Size and distribution of the global volume of surgery in 2012

Weiser, T. G., Haynes, A. B., Molina, G., Lipsitz, S. R., Esquivel, M. M., Uribe-Leitz, T., Fu, R., Azad, T., Chao, T. E., Berry, W. R., et al. (2016) · Bulletin of the World Health Organization · doi:10.2471/blt.15.159293 · Cited by 655

Direct evidence of surface exposed water ice in the lunar polar regions

Li, S., Lucey, P. G., Milliken, R. E., Hayne, P. O., Fisher, E., Williams, J.-P., Hurley, D. M., & Elphic, R. C. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802345115 · Cited by 625

Swarm of micro flying robots in the wild

Zhou, X., Wen, X., Wang, Z., Gao, Y., Li, H., Wang, Q., Yang, T., Lu, H., Cao, Y., Xu, C., et al. (2022) · Science Robotics · doi:10.1126/scirobotics.abm5954 · Cited by 657

A review of spline function procedures in R

Perperoglou, A., Sauerbrei, W., Abrahamowicz, M., & Schmid, M. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0666-3 · Cited by 585

Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons

Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601

Association Between Recreational Marijuana Legalization in the United States and Changes in Marijuana Use and Cannabis Use Disorder From 2008 to 2016

Cerdá, M., Mauro, C., Hamilton, A., Levy, N. S., Santaella-Tenorio, J., Hasin, D., Wall, M. M., Keyes, K. M., & Martins, S. S. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.3254 · Cited by 598

A geomagnetic field model for the Holocene based on archaeomagnetic and lava flow data

Pavón-Carrasco, F. J., Osete, M. L., Torta, J. M., & De Santis, A. (2014) · Earth and Planetary Science Letters · doi:10.1016/j.epsl.2013.11.046 · Cited by 384

Prehepatic Insulin Production in Man: Kinetic Analysis Using Peripheral Connecting Peptide Behavior*

EATON, R. P., ALLEN, R. C., SCHADE, D. S., ERICKSON, K. M., & STANDEFER, J. (1980) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem-51-3-520 · Cited by 363

Shape constrained additive models

Pya, N., & Wood, S. N. (2015) · Statistics and Computing · doi:10.1007/s11222-013-9448-7 · Cited by 315

A spline least squares method for numerical parameter estimation in differential equations

Varah, J. M. (1982) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0903003 · Cited by 274

Seasonal amplitude increase in atmospheric CO 2 concentration at Mauna Loa, Hawaii, 1959–1982

Bacastow, R. B., Keeling, C. D., & Whorf, T. P. (1985) · Journal of Geophysical Research: Atmospheres · doi:10.1029/jd090id06p10529 · Cited by 250

The oral glucose minimal model: Estimation of insulin sensitivity from a meal test

Della Man, C., Caumo, A., & Cobelli, C. (2002) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.995680 · Cited by 241

Smoothness Maximization Along a Predefined Path Accurately Predicts the Speed Profiles of Complex Arm Movements

Todorov, E., & Jordan, M. I. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.2.696 · Cited by 241

Genetic effects on age-dependent onset and islet cell autoantibody markers in type 1 diabetes

Graham, J., Hagopian, W. A., Kockum, I., Li, L. S., Sanjeevi, C. B., Lowe, R. M., Schaefer, J. B., Zarghami, M., Day, H. L., Landin-Olsson, M., et al. (2002) · Diabetes · doi:10.2337/diabetes.51.5.1346 · Cited by 230

Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data

Kim, S., Imoto, S., & Miyano, S. (2004) · Biosystems · doi:10.1016/j.biosystems.2004.03.004 · Cited by 224

The broiler chicken as a signal of a human reconfigured biosphere

Bennett, C. E., Thomas, R., Williams, M., Zalasiewicz, J., Edgeworth, M., Miller, H., Coles, B., Foster, A., Burton, E. J., & Marume, U. (2018) · Royal Society Open Science · doi:10.1098/rsos.180325 · Cited by 225

Calcium carbonate dissolution patterns in the ocean

Sulpis, O., Jeansson, E., Dinauer, A., Lauvset, S. K., & Middelburg, J. J. (2021) · Nature Geoscience · doi:10.1038/s41561-021-00743-y · Cited by 177

The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence

Marucci, M., Di Flumeri, G., Borghini, G., Sciaraffa, N., Scandola, M., Pavone, E. F., Babiloni, F., Betti, V., & Aricò, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-84196-8 · Cited by 170

Trimmed constrained mixed effects models: formulations and algorithms

Zheng, P., Barber, R., Sorensen, R. J. D., Murray, C. J. L., & Aravkin, A. Y. (2021) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2020.1868303 · Cited by 174

Dual layer automotive ammonia oxidation catalysts: Experiments and computer simulation

Scheuer, A., Hauptmann, W., Drochner, A., Gieshoff, J., Vogel, H., & Votsmeier, M. (2012) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2011.10.032 · Cited by 143

Modeling non-linear relationships in epidemiological data: the application and interpretation of spline models.

Schuster, N. A., Rijnhart, J. J. M., Twisk, J. W. R., & Heymans, M. W. (2022) · Frontiers in Epidemiology · doi:10.3389/fepid.2022.975380 · Cited by 125

Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition

Yan, H., Paynabar, K., & Shi, J. (2017) · Technometrics · doi:10.1080/00401706.2015.1102764 · Cited by 111

Genetic and expression analysis of cattle identifies candidate genes in pathways responding to Trypanosoma congolense infection

Noyes, H., Brass, A., Obara, I., Anderson, S., Archibald, A. L., Bradley, D. G., Fisher, P., Freeman, A., Gibson, J., Gicheru, M., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1013486108 · Cited by 88

New approaches to regression by generalized additive models and continuous optimization for modern applications in finance, science and technology

Taylan, P., Weber, G.-W., & Beck, A. (2007) · Optimization · doi:10.1080/02331930701618740 · Cited by 83

Determining the threshold effect of ozone on daily mortality: an analysis of ozone and mortality in Seoul, Korea, 1995–1999

Kim, S.-Y., Lee, J.-T., Hong, Y.-C., Ahn, K.-J., & Kim, H. (2004) · Environmental Research · doi:10.1016/j.envres.2003.09.006 · Cited by 79

Non-life rate-making with Bayesian GAMs

Denuit, M., & Lang, S. (2004) · Insurance: Mathematics and Economics · doi:10.1016/j.insmatheco.2004.08.001 · Cited by 75

Handling Missing Data in the Modeling of Intensive Longitudinal Data

Ji, L., Chow, S.-M., Schermerhorn, A. C., Jacobson, N. C., & Cummings, E. M. (2018) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2017.1417046 · Cited by 74

Wildlife population dynamics in human-dominated landscapes under community-based conservation: the example of Nakuru Wildlife Conservancy, Kenya

Ogutu, J. O., Kuloba, B., Piepho, H.-P., & Kanga, E. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169730 · Cited by 71

Influence of Path Integration Versus Environmental Orientation on Place Cell Remapping Between Visually Identical Environments

Fuhs, M. C., VanRhoads, S. R., Casale, A. E., McNaughton, B., & Touretzky, D. S. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00132.2005 · Cited by 53

Efficient implementation of detailed surface chemistry into reactor models using mapped rate data

Votsmeier, M. (2009) · Chemical Engineering Science · doi:10.1016/j.ces.2008.12.006 · Cited by 58

Note—Estimating Geographic Customer Densities Using Kernel Density Estimation

Donthu, N., & Rust, R. T. (1989) · Marketing Science · doi:10.1287/mksc.8.2.191 · Cited by 52

Variation in wild pea (Pisum sativum subsp.elatius) seed dormancy and its relationship to the environment and seed coat traits

Hradilová, I., Duchoslav, M., Brus, J., Pechanec, V., Hýbl, M., Kopecký, P., Smržová, L., Štefelová, N., Vaclávek, T., Bariotakis, M., et al. (2019) · PeerJ · doi:10.7717/peerj.6263 · Cited by 52

Simulation of automotive NH3 oxidation catalysts based on pre-computed rate data from mechanistic surface kinetics

Votsmeier, M., Scheuer, A., Drochner, A., Vogel, H., & Gieshoff, J. (2010) · Catalysis Today · doi:10.1016/j.cattod.2010.01.018 · Cited by 52

Efficient machine learning based surrogate models for surface kinetics by approximating the rates of the rate-determining steps

Döppel, F. A., & Votsmeier, M. (2022) · Chemical Engineering Science · doi:10.1016/j.ces.2022.117964 · Cited by 38

Inter-data-center network traffic prediction with elephant flows

Li, Y., Liu, H., Yang, W., Hu, D., & Xu, W. (2016) · NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium · doi:10.1109/noms.2016.7502814 · Cited by 34

A cadaver-based biomechanical model of acetabulum reaming for surgical virtual reality training simulators

Pelliccia, L., Lorenz, M., Heyde, C.-E., Kaluschke, M., Klimant, P., Knopp, S., Schleifenbaum, S., Rotsch, C., Weller, R., Werner, M., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-71499-5 · Cited by 20

MobiGesture: Mobility-aware hand gesture recognition for healthcare

Zhao, H., Ma, Y., Wang, S., Watson, A., & Zhou, G. (2018) · Smart Health · doi:10.1016/j.smhl.2018.07.010 · Cited by 18

Spline‐based self‐controlled case series method

Ghebremichael‐Weldeselassie, Y., Whitaker, H. J., & Farrington, C. P. (2017) · Statistics in Medicine · doi:10.1002/sim.7311 · Cited by 16

Long-term historical and projected herbivore population dynamics in Ngorongoro crater, Tanzania

Moehlman, P. D., Ogutu, J. O., Piepho, H.-P., Runyoro, V. A., Coughenour, M. B., & Boone, R. B. (2020) · PLOS ONE · doi:10.1371/journal.pone.0212530 · Cited by 14

Tsukuba Challenge 2017 Dynamic Object Tracks Dataset for Pedestrian Behavior Analysis

Lambert, J., Liang, L., Morales, L. Y., Akai, N., Carballo, A., Takeuchi, E., Narksri, P., Seiya, S., Takeda, K., Graduate School of Informatics, N. U. F.-C., et al. (2018) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2018.p0598 · Cited by 11

Examining the Relationship between Free Sugars and Calorie Contents in Canadian Prepacked Foods and Beverages

Bernstein, J., Lou, W., & L’Abbe, M. (2017) · Foods · doi:10.3390/foods6090075 · Cited by 9