STATA
FP, MFP and all the extensions have been programmed by Patrick Royston in Stata. FP (commands fracpoly and fp) and MFP (command mfp) have been ‘factory’ routines, i.e. standard parts of Stata, for many years.
The following articles have appeared in the Stata Journal.

R&S (2007): Multivariable modelling with cubic regression splines: A principled approach.

R&S (2009): Bootstrap assessment of the stability of multivariable models.
In addition, the FP or MFPrelated packages listed below are available for download within Stata from Royston's UCL website via the following Stata command:
 net from http://www.homepages.ucl.ac.uk/~ucakjpr/stata/
 fp_plus: Extensions to mfp and fracpoly supporting factor variables
 fracpoly_powers: Extensions to mfp and fracpoly supporting factor variables
 metacurve: Meta analysis of a continuous covariate
 mfpboot: Bootstrapping MFP models for stability analysis
 mfpi: Modelling interactions between categorical and continuous variables
 mfpigen: Modelling interactions between continuous variables
 mfpmi: MFP for multiply imputed data
 stpmfp: Flexible parametric survival modelling with fractional polynomials
 stmfpt: Multivariable Cox models with timedependent covariate effects
MFP in R
The MFP in R software is implemented in two packages: mfp (Ambler G, and Benner A, 2023) and mfp2 (Kipruto E, Kammer M, Royston P, Sauerbrei W, 2023). The main advantage of mfp2 relative to mfp is informative output, computational efficiency, the ability to model sigmoid functions, availability of plotting functions and an additional interface that takes response y and design matrix X. The output of mfp2 is similar to mfp in Stata.
MFP in SAS
MFP is also available as a SAS macro.
For more details see Sauerbrei et al (2006): Multivariable regression model building by using fractional polynomials: description of SAS, STATA and R programs.