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Group Meeting

2020 Spring

  • Jan 22 - Haolun Shi
  • Jan 29 - Erin Zhang
  • Feb 5 - Alex Wang
  • Feb 12 - Haolun Shi
  • Feb 26 - Tianyu Guan
  • March 4 - Yuping Yang
  • March 11 - Aaron Danielson
  • March 18 - Sidi Wu
  • March 25 - Barinder Thind
  • April 1 - Boyi Hu
  • April 8 - Haolun Shi
  • April 15 - Tianyu Guan
  • April 22 - Efehan Ulas

2019 Fall

  • September 4, Boyi Hu
  • September 11, Haolun Shi
  • September 18, Aaron Danielson
  • September 27, Tianyu Guan
  • October 16 Yuping Yang
  • October 23 Alex Wang
  • October 30 Haolun Shi
  • November 6 Sidi Wu
  • November 13 Joel Therrien
  • November 20 Barinder Thind
  • November 27 Erin Zhang

2019 Summer

  • May 8: Sidi
  • May 15: Grace
  • May 22: Alex
  • June 5: Shufei
  • June 12: Shijia
  • June 19: Haolun
  • June 26: Tianyu
  • July 3: Yuping
  • Aug 7: Boyi
  • Aug 14: Yanjun
  • Aug 21: Erin

2019 Spring

  • Jan 2: Tianyu
  • Jan 9: Shijia
  • Jan 16: Haolun
  • Jan 23: Shufei
  • Jan 30: Leshun
  • Feb 6: Yuping
  • Feb 13: Alex
  • Feb 20: Boyi
  • Feb 27: Yanjun
  • Mar 6: Sidi
  • Mar 13: Grace
  • Mar 20 and the follwing Wednesdays: To decide.

2018 Fall

2018 Spring

  • April 18 - Yajie Zhou
  • April 4 - Cherlane Lin
  • March 21 - Joel Therrien
  • March 7 - Alex Wang
  • Feb 21 - Yunlong Nie
  • Feb 7 - Peijun Sang
  • Jan 24 - Yuping Yang

Differential Equations

Stochastic Differential Equations (SDEs)

In a stochastic differential equation, the unknown quantity is a stochastic process. The package sde provides functions for simulation and inference for stochastic differential equations. It is the accompanying package to the book by Iacus (2008). The package pomp contains functions for statistical inference for partially observed Markov processes. Packages adaptivetau and GillespieSSA implement Gillespie's "exact" stochastic simulation algorithm (direct method) and several approximate methods.

Ordinary Differential Equations (ODEs)

In an ODE, the unknown quantity is a function of a single independent variable. Several packages offer to solve ODEs. The "odesolve" package was the first to solve ordinary differential equations in R. It contains two integration methods. It is not actively maintained and has been replaced by the package deSolve. The package deSolve contains several solvers for solving ODEs. It can deal with stiff and nonstiff problems. The package deTestSet contains solvers designed for solving very stiff equations. The package odeintr generates and compiles C++ ODE solvers on the fly using Rcpp and Boost odeint . Delay Differential Equations (DDEs)

In a DDE, the derivative at a certain time is a function of the variable value at a previous time. The package PBSddesolve (originally published as "ddesolve") includes a solver for non-stiff DDE problems. Functions in the package deSolve can solve both stiff and non-stiff DDE problems. Partial Differential Equations (PDEs)

PDEs are differential equations in which the unknown quantity is a function of multiple independent variables. A common classification is into elliptic (time-independent), hyperbolic (time-dependent and wavelike), and parabolic (time-dependent and diffusive) equations. One way to solve them is to rewrite the PDEs as a set of coupled ODEs, and then use an efficient solver. The R-package ReacTran provides functions for converting the PDEs into a set of ODEs. Its main target is in the field of reactive transport modelling, but it can be used to solve PDEs of the three main types. It provides functions for discretising PDEs on cartesian, polar, cylindrical and spherical grids. The package deSolve contains dedicated solvers for 1-D, 2-D and 3-D time-varying ODE problems as generated from PDEs (e.g. by ReacTran). Solvers for 1-D time varying problems can also be found in the package deTestSet. The package rootSolve contains optimized solvers for 1-D, 2-D and 3-D algebraic problems generated from (time-invariant) PDEs. It can thus be used for solving elliptic equations. Note that, to date, PDEs in R can only be solved using finite differences. At some point, we hope that finite element and spectral methods will become available. Differential Algebraic Equations (DAEs)

Differential algebraic equations comprise both differential and algebraic terms. An important feature of a DAE is its differentiation index; the higher this index, the more difficult to solve the DAE. The package deSolve provides two solvers, that can handle DAEs up to index 3. Three more DAE solvers are in the package deTestSet. Boundary Value Problems (BVPs)

BVPs have solutions and/or derivative conditions specified at the boundaries of the independent variable. Package bvpSolve deals only with this type of equations. The package ReacTran can solve BVPs that belong to the class of reactive transport equations.


The simecol package provides an interactive environment to implement and simulate dynamic models. Next to DE models, it also provides functions for grid-oriented, individual-based, and particle diffusion models. Package scaRabee offers frameworks for simulation and optimization of Pharmacokinetic-Pharmacodynamic Models. In the package FME are functions for inverse modelling (fitting to data), sensitivity analysis, identifiability and Monte Carlo Analysis of DE models. The packages nlmeODE and PSM have functions for mixed-effects modelling using differential equations. mkin provides routines for fitting kinetic models with one or more state variables to chemical degradation data. The package CollocInfer implements collocation-inference for continuous-time and discrete-time stochastic processes. Root finding, equilibrium and steady-state analysis of ODEs can be done with the package rootSolve. The deTestSet package contains many test problems for differential equations. Package pracma contains solvers for ODEs, as pure R scripts, useful as a learning tool. The PBSmodelling package adds GUI functions to models. Package ecolMod contains the figures, data sets and examples from a book on ecological modelling (Soetaert and Herman, 2009). primer is a support package for the book of Stevens (2009).

Things to learn

  • October 22, 2015: Estimation of Stochastic Differential Equations with Sim.DiffProc Package Version 2.9
  • October 9, 2015: Haocheng Li mentioned to me that ECME algorithm by Joseph L. Schafer is very good for estimating linear mixed model. He also mentioned that the mixed effect model may be hard to estimate when the number of random effects are large. This can be a good problem to explore.
  • July 7, 2015 Functional Regression
  • Theory of functional differential equations by Hale, Jack K Applied mathematical sciences, 1977, [2d ed.]. --

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