  Large scale structure of the Universe becomes a leading source of precision
cosmological information. We present two particular tools that can be used in
cosmological analyses of the redshift space galaxy clustering data: a new
open-source code CLASS-PT and the theoretical error approach. CLASS-PT computes
one-loop power auto- and cross-power spectra for matter fields and biased
tracers in real and redshift spaces. We show that the code meets the precision
standards set by the upcoming high-precision large-scale structure surveys. The
theoretical error likelihood approach allows one to analyze galaxy clustering
data without having to measure the scale cut $k_{\rm max}$. This approach takes
into account that theoretical uncertainties affect parameter estimation
gradually, which helps optimize data analysis and ensures that all available
cosmological information is extracted.
