BUG: cummin, max, sum not valid when used with agg + NaN not skipped.
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

---

#### Code Sample, a copy-pastable example

```python
import pandas as pd
import numpy as np
from random import seed, randint

# Data
kp = pd.period_range(start='2020-01-01 00:00', end='2020-01-01 00:25', freq='5T')
sp = pd.period_range(start='2020-01-01 00:00', end='2020-01-01 00:25', freq='1h')
seed(1)
values = [randint(0,10) for p in kp]
dft = pd.DataFrame({'Values' : values}, index=kp)
dft.loc[kp[-2]] = np.nan

# Trouble 1: `cummin`, `cummax`, `cumsum` not available through `agg`?
resampler = dft.resample(sp.freqstr)
progress = resampler.agg('cummin')

```
Get (same with `cummax` and `cumsum`):
AttributeError: 'cummin' is not a valid function for 'PeriodIndexResampler' object.

```
# Trouble 2: `cummin`, `cummax`, `cumsum` appear to work when used in a dict,
# but not skipna parameter. Whatever `skipna` (`True` or `False`) result is the same.

resampler = dft.resample(sp.freqstr)
progress = resampler.agg({('Values','cummin')},skipna=False)

```
#### Output obtained
progress
                          Values
                          Values
2020-01-01 00:00    2.0
2020-01-01 00:05    2.0
2020-01-01 00:10    1.0
2020-01-01 00:15    1.0
2020-01-01 00:20    NaN
2020-01-01 00:25    1.0


#### Problem description
It appears `cumsum`, `cummin`, `cumsum` cannot be used directly with `agg`. The documentation appears to state differently:
"Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply."
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.resample.Resampler.aggregate.html

#### Expected Output
For trouble 1 :
`progress` (with `cummin`)
                            Values
2020-01-01 00:00     2.0
2020-01-01 00:05     2.0
2020-01-01 00:10     1.0
2020-01-01 00:15     1.0
2020-01-01 00:20    NaN
2020-01-01 00:25     1.0

For trouble 2:
`progress` (with `cummin` & `skipna=False`)
                            Values
2020-01-01 00:00     2.0
2020-01-01 00:05     2.0
2020-01-01 00:10     1.0
2020-01-01 00:15     1.0
2020-01-01 00:20    NaN
2020-01-01 00:25    NaN


As a side question, is it possible to have an additional parameter `fill_value=0` to have then following output?

`progress` (with `cummin` & `fill_value=0`)
                            Values
2020-01-01 00:00     2.0
2020-01-01 00:05     2.0
2020-01-01 00:10     1.0
2020-01-01 00:15     1.0
2020-01-01 00:20     1.0
2020-01-01 00:25     1.0


#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.6.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.3.0-51-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : fr_FR.UTF-8
LOCALE           : fr_FR.UTF-8

pandas           : 1.0.3
numpy            : 1.16.3
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.1.3.post20200330
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : 2.4.4
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.1
IPython          : 7.13.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : 0.3.3
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.0.3
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.16.0
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : 0.48.0

</details>
