Operational Modes 
Parameter estimation Predictive analysis Regularization Pareto 
Parameter Estimation 
Gauss Marquardt Levenberg with Broyden Jacobian updating Shuffled Complex Evolution Covariance Matrix Adaptation 
Parameters 
No upper limit on number of parameters Logtransformed Untransformed Fixed Tied to other parameters Parameter scaling and offsetting Arbitrary parameter transformation and mathematical manipulation Selfregularizing parameter bounds enforcement through temporary, sequential fixing

Observations 
Individually weighted Covariance matrix can be supplied to grouped observations Objective function contributions calculated for individual observation groups Adjustment of observation group weight factors for equalization of contribution to objective function Computation of orthogonal "super observations" No limit to number of observations 
Prior Information 
Applied to individual parameters or to linear relationships between parameters Individually weighted Covariance matrix can be supplied to grouped prior information equations Objective function contributions calculated for individual prior information groups No limit to number of prior information equations 
Regularization 
Truncated singular value decomposition Tikhonov Paretoadjustable Tikhonov Subspaceenhanced Tikhonov LSQR "SVDAssist" All combinations of the above "Automatic user intervention" 
Model Interaction 
Writes model input files using templates of those files Reads model output files using instruction sets Runs model (or sequence of models) through system call Option to receive modelcomputed Jacobian matrix from model Parallelization of model runs across different nodes or machines 
Derivatives 
Two, three or five point finite difference stencil Parabolic, outsidepoints or bestfit options for three point stencil Maximum precision or minimum error variance options for five point stencil Userselectable relative or absolute parameter increments and/or combination of these Analysis of finitedifference derivatives integrity Partial detection and elimination of numericallycorrupted derivatives Computation of composite sensitivities Display, analysis and manipulation (including singular value decomposition) of (weighted) Jacobian matrix 
Uncertainty Analysis 
Linear overdetermined Nonlinear overdetermined through calibrationconstrained predictive maximization/minimization Linear highlyparameterized parameter/predictive error variance assessment Linear highlyparameterized parameter/predictive uncertainty assessment Nonlinear, regularized, calibrationconstrained parameter/predictive maximization/minimization Random parameter generation Monte Carlo analysis Postcalibration nullspace Monte Carlo analysis "Predictive calibration" analysis Paretoadjustable hypothesis testingĀ 
UncertaintyRelated 
Parameter identifiability Relative parameter error variance reduction and uncertainty reduction Resolution matrix Parameter (group) contributions to predictive error variance Parameter (group) contributions to predictive uncertainty Worth of different new or existing measurements in reducing parameter/predictive error variance Worth of different new or existing measurements in reducing parameter/predictive uncertainty Solution and null space contributions to parameter/predictive error variance Optimality of singular value truncation for minimization of predictive error variance Inference statistics such as observation leverage, Cook's D, DFBETAS KullbackLeibler (KL) Information Loss Statistics 
Other 
Restart without loss of data Userintervention Formulation of equivalent linear model Formulation of input dataset for scaled parameters General manipulation of matrices and vectors Global Jacobian matrix assembly from submatrices Comprehensive checking of PEST input dataset for correctness and consistency Automatic generation of PEST input dataset 
Operating System 
PC or UNIX (source code provided for compilation under UNIX) 32 bit and 64 bit executables available for PC
