In the mathematical subfield of numerical analysis, an M-spline^[1][2] is a non-negative spline function.

A family of M-spline functions of order k with n free parameters is defined by a set of knots t₁  ≤ t₂  ≤  ...  ≤  t_n+k such that

• t₁ = ... = t_k
• t_n+1 = ... = t_n+k
• t_i < t_i+k for all i

The family includes n members indexed by i = 1,...,n.

An M-spline M_i(x|k, t) has the following mathematical properties

• M_i(x|k, t) is non-negative
• M_i(x|k, t) is zero unless t_i ≤ x < t_i+k
• M_i(x|k, t) has k − 2 continuous derivatives at interior knots t_k+1, ..., t_n−1
• M_i(x|k, t) integrates to 1

M-splines can be efficiently and stably computed using the following recursions:

For k = 1,

${\displaystyle M_{i}(x|1,t)={\frac {1}{t_{i+1}-t_{i}}}}$

if t_i ≤ x < t_i+1, and M_i(x|1,t) = 0 otherwise.

For k > 1,

${\displaystyle M_{i}(x|k,t)={\frac {k\left[(x-t_{i})M_{i}(x|k-1,t)+(t_{i+k}-x)M_{i+1}(x|k-1,t)\right]}{(k-1)(t_{i+k}-t_{i})}}.}$

M-splines can be integrated to produce a family of monotone splines called I-splines. M-splines can also be used directly as basis splines for regression analysis involving positive response data (constraining the regression coefficients to be non-negative).