PACE program version 2.6 (release April 28, 2008) is written in Matlab and can be downloaded from PACE 2.6 This program performs functional principal component analysis, a core technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. PACE does not use pre-smoothing of trajectories which is problematic if functional data are sparsely sampled or measurements are corrupted with noise. PACE2.6 provides the following core options: (1) Fitting of sparsely sampled random functions and their derivatives. (2) Functional linear regression, fitting functional linear regression models for both sparsely or densely sampled random trajectories, for cases where the predictor is a random function and the response is a scalar or a random function. (3) Diagnostics and bootstrap inference for functional linear regression. (4) Assessing functional dependence through functional singular value decomposition. Further description is available in the readme.txt file. If you use the program, refer to the articles below which contain some of the core methodology.

References:

Yao, F., Müller, H.G., Clifford, A.J., Dueker, S.R., Follett, J. Lin, Y., Buchholz, B. A., Vogel, J.S. (2003). Shrinkage estimation for functional component scores with application to the population kinetics of plasma folate. Biometrics 59 676-685.(pdf)Yao, F., Müller, H.G., Wang, J.L. (2005). Functional data analysis for sparse longitudinal data. Journal of the American Statistical Association 100 577-590.(pdf)Yao, F., Müller, H.G., Wang, J.L. (2005). Functional linear regression analysis for longitudinal data. Annals of Statistics 33 2873-2903. (pdf)Chiou, J.M., Müller, H.G. (2007). Diagnostics for functional regression via residual processes. Computational Statistics and Data Analysis 51 4849-4863. (pdf)Müller, H.G., Chiou, J.M., Leng, X. (2008). Inferring gene expression dynamics via functional regression analysis. BMC Bioinformatics 9:60 (pdf)