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With the large progress in searches for dark matter (DM) particles with indirect and direct methods, we develop a numerical tool that enables fast calculations of the likelihoods of specified DM particle models given a number of observational data, such as charged cosmic rays from space-borne experiments (e.g., PAMELA, AMS-02), γ -rays from the Fermi space telescope, and underground direct detection experiments. The purpose of this tool — LikeDM , likelihood calculator for dark matter detection — is to bridge the gap between a particle model of DM and the observational data. The intermediate steps between these two, including the astrophysical backgrounds, the propagation of charged particles, the analysis of Fermi γ -ray data, as well as the DM velocity distribution and the nuclear form factor, have been dealt with in the code. We release the first version (v1.0) focusing on the constraints from indirect detection of DM with charged cosmic and gamma rays. Direct detection will be implemented in the next version. This manual describes the framework, usage, and related physics of the code. Program summary Program Title: LikeDM Program Files doi: http://dx.doi.org/10.17632/p93d3ksfvd.1 Licensing provisions: GPLv3 Programming language: FORTRAN 90 and Python Nature of problem: Dealing with the intermediate steps between a dark matter model and data. Solution method: Fast computation of the likelihood of a given dark matter model (defined by a mass, cross section or decay rate, and annihilation or decay yield spectrum), without digging into the details of cosmic-ray propagation, Fermi-LAT data analysis, or related astrophysical backgrounds. [ABSTRACT FROM AUTHOR]