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Workshop on

Growth Curve Models in Population Dynamics Using R for Biologists

February 12-13, 2019

Indian Statistical Institute, Kolkata

Growth curve models are used as mathematical framework for quantitative studies of growth in many areas of applied science. The sigmoid functions: Gompertz, logistic and general von Bertalanffy and their associate differential equations have applications to model self-limited population growth in a diverse field. The workshop is specifically designed for the applied researchers to discuss the utility of various growth models in different fields of study (with special emphasis on population dynamics). The workshop is very applied in nature and the software will be utilized to connect the theoretical equations to connect with the natural populations.

Important information

 

Number of seats: 50

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Registration opens: December 18, 2018

Registration closes: January 15, 2019

 

Announcement of the selected participants: January 18, 2019

 

Registration fee: Rs. 1180 (Rs. 1000+18% service tax)

Payment opens: January 18, 2019

Payment closes: January 31, 2019

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Workshop agenda

  1. Quantification of growth: a) Absolute growth rate; b) Relative growth rate; c) Empirical estimate of relative growth rate under different data set up. d) Quantification of Errors in growth rate measurements. 

  2. Characterisation of various growth curves using relative growth rate.

  3. Classification of growth curves – a) Density independent; b) Density dependent model – Logistic, Theta-logistic & other extended logistic family; c) Time dependent model – Gompertz and related extended Gompertz family; d) Allee growth model.

  4. Dynamical stability for growth curves (Lyapunov’s stability)

  5. Stochastic growth curve analysis – a) Stochastic logistic b) Stochastic stability c) Stationary and quasi-stationary distribution.

  6. Goodness-of-fit test of growth curve model – a) Case study for logistic and Gompertz growth curve model; b) Selection of best of model from a set of growth models.

  7. Analysis of biological data using growth curve models involving random effect.

  8. Application of R: a) Plotting growth curves; b) symbolical computation and stability analysis in R; c) Fitting growth curve models to real data; d) Simulation experiment for stochastic growth models and summarize the results

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